2015年第46卷增刊共收錄48篇
<RECORD 1>
Accession number:20160301828019
Title:Outlier samples detection method for NIR multicomponent analysis
Authors:Yin, Baoquan (1, 2); Shi, Yinxue (1); Sun, Ruizhi (1); Wang, Wendi (1)
Author affiliation:(1) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University,
Beijing, China; (2) Yantai Academy, China Agricultural University, Yantai, China
Corresponding author:Sun, Ruizhi([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:122-127
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Near infrared spectroscopy is currently a highly versatile tool used in diverse fields. However, outlier samples strongly affect the
performance of the prediction model in near infrared spectroscopy analysis. Therefore, detecting and eliminating the outlier samples is a major and
important procedure in near infrared spectroscopy analysis. Using the outlier samples detection based on joint X-Y distances (ODXY) method, a
special outlier samples detection method for multicomponent analysis was proposed and proved, termed as MODXY method. Experimental data was derived
from the near infrared spectra of 80 corns. Based on these, the PLS models of moisture content, oil content, protein content and starch content were
constructed by eliminating outlier samples using different outlier detection methods. The obtained models were compared in terms of performance by
the predictive root mean square error (RMSEP) and the coefficient of determination (R<sup>2</sup>). The results showed that in most cases the MODXY
method had better outlier sample recognition capability in NIR multicomponent analysis compared with other methods. Both ODXY method and MODXY
method had their own suitable range, and they were more effective when the relative standard deviation of components was large enough. © 2015,
Chinese Society for Agricultural Machinery. All right reserved.
Number of references:13
Main heading:Infrared devices
Controlled terms:Mean square error - Near infrared spectroscopy - Spectrum analysis - Statistics
Uncontrolled terms:Mahalanobis distances - Multi-component analysis - NIR spectroscopy - Outlier samples - PLS regression
Classification code:922.2 Mathematical Statistics
DOI:10.6041/j.issn.1000-1298.2015.S0.021
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 2>
Accession number:20160301828006
Title:Screening method of abnormal corn ears based on machine vision
Authors:Zhang, Fan (1, 2); Li, Shaoming (1, 2); Liu, Zhe (1, 2); Zhu, Dehai (1, 2); Wang, Yue (1, 2); Ma, Qin (1, 2)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China; (2) Key Laboratory of
Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
Corresponding author:Ma, Qin([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:45-49
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:The quality of corn seed production and new variety breeding are affected by the problem of abnormal corn ears. Taking the whole corn ear
as research object, the sorting method of three abnormal grains (namely moldy corn ears, worm-eaten corn ears and mechanically damaged corn ears)
was researched based on two-dimensional fast imaging technology. Firstly, the portable image acquisition device was constructed based on the
monocular vision and the corn ear image was acquired. According to these characteristics of corn ear images, six color features in RGB model and HIS
model and five texture features in gray scale images were extracted and normalized to build the classification model of these abnormal corn ears.
The classifiers were trained with the support vector machine (SVM) and BP neural network for comparison analysis by using the known feature vectors.
The result showed that the SVM classifier had higher accuracy than BP neural network classifier. The accuracies of moldy corn ears sorting, worm-
eaten corn ears sorting and mechanically damaged corn ears sorting were 96.0%, 93.3% and 90.0%, respectively. The study made an important foundation
for realizing the automatic machine screening of abnormal corn ears and had high application value in improving the corn seed quality. © 2015,
Chinese Society for Agricultural Machinery. All right reserved.
Number of references:15
Main heading:Image processing
Controlled terms:Computer vision - Image acquisition - Imaging techniques - Neural networks - Screening - Support vector machines
Uncontrolled terms:Abnormal corn ears - Acquisition device - Automatic machines - BP neural network classifier - BP neural networks - Classification
models - Comparison analysis - High application value
Classification code:723 Computer Software, Data Handling and Applications - 723.5 Computer Applications - 746 Imaging Techniques - 802.3 Chemical
Operations
DOI:10.6041/j.issn.1000-1298.2015.S0.008
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 3>
Accession number:20160301828033
Title:Performance analysis of vehicle-mounted multi-spectral imaging system at different vehicle speeds
Authors:Wen, Yao (1); Li, Minzan (1); Zhao, Yi (1); Zhang, Meng (1); Sun, Hong (1); Song, Yuanyuan (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China
Corresponding author:Sun, Hong([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:215-221
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to rapidly detect the chlorophyll content of winter wheat canopy leaves in the field, a vehicle-mounted multi-spectral imaging
system with 2-CCD camera was developed, and the working performance of the system was analyzed at different vehicle speeds. The FOTON-4040 tractor
was used as the vehicle platform equipped multi-spectral image intelligent sensing system. Four speeds were set up in field experiments, which were
S1 (0.54 m/s), S2 (0.83 m/s), S3 (1.04 m/s) and S4 (1.72 m/s). Visible and near infrared canopy images of winter wheat were collected. Meanwhile,
the GPS position information was obtained and the SPAD values which indicated the chlorophyll content of winter wheat leaves were measured. Multi-
spectral images were processed by adaptive smoothing filtering and canopy segmentation. There were 10 parameters in the image detection. The average
gray values of four bands (R, G, B and NIR) were extracted, and four vegetation indices (NDVI, NDGI, RVI and DVI), mean value of H in HSI model and
canopy cover degree C were calculated. The correlation between each parameter of the image and the SPAD value of the chlorophyll index was analyzed.
The results showed that the correlations between the parameters of each image and the chlorophyll index at speed of S1, S2 and S3 were higher than
that at speed of S4. The correlation coefficients between NDVI, RVI, NDGI and the SPAD value reached over 0.50 at speed of S1, S2 and S3. MLR models
for the diagnosis of the chlorophyll content were established at different speeds of S1, S2 and S3, respectively. The model precision met the
requirements of crop growing space distribution map. In order to further improve the diagnostic efficiency of the crops growth parameters in the
field, the MLR model of the chlorophyll content in winter wheat leaves was built by NDVI, NDGI and RVI. The results showed that the model was
universal. The research can provide support for the rapid diagnosis of field crop growth. © 2015, Chinese Society for Agricultural Machinery.
All right reserved.
Number of references:20
Main heading:Image processing
Controlled terms:Chlorophyll - Crops - Cultivation - Forestry - Image segmentation - Imaging systems - Infrared devices - Spectroscopy - Speed -
Vegetation - Vehicle performance - Vehicles
Uncontrolled terms:Chlorophyll Index - Correlation coefficient - Diagnostic efficiency - Multi-spectral imaging systems - Multispectral imaging -
Vegetation index - Visible and near infrared - Winter wheat
Classification code:662.1 Automobiles - 746 Imaging Techniques - 804.1 Organic Compounds - 821.3 Agricultural Methods - 821.4 Agricultural Products
DOI:10.6041/j.issn.1000-1298.2015.S0.035
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 4>
Accession number:20160301828013
Title:Sensitive electrochemical determination of trace cadmium and lead using ionic liquid and Nano-Fe<inf>3</inf>O<inf>4</inf>modified screen-
printed carbon electrode
Authors:Wang, Hui (1, 2); Zhao, Guo (1, 2); Wang, Zhiqiang (3); Liu, Gang (1, 2)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, China; (3)
College of Computer Science and Technology, Shandong University of Technology, Zibo, China
Corresponding author:Liu, Gang([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:84-89
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:A modified screen-printed carbon electrode was prepared and manufactured by using ionic liquid, nano-Fe<inf>3</inf>O<inf>4</inf>and in situ
plating bismuth film methods, which were used to detect heavy metal ions of cadmium and lead by stripping square wave voltammetry. Some
electrochemical methods such as cyclic voltammetry, electrochemical impedance spectroscopy and square wave stripping voltammetry were further
applied to investigate the properties of this electrode. It illustrated that the formed bismuth film/ionic liquid/nano-
Fe<inf>3</inf>O<inf>4</inf>layer on the top of screen-printed carbon electrode could remarkably improve the electron transfer and the specific
surface area of the electrode owing to ionic conductivity and adhesiveness of IL and high electron transfer of nano-Fe<inf>3</inf>O<inf>4</inf>.
Under the optimum conditions, the linear detection range of modified electrode was 0.2~35.0 μg/L and 0.2~20.0 μg/L for cadimium and lead with
detection limit of 0.06 μg/L and 0.1 μg/L (S/N=3). Finally, the proposed analytical procedure was applied to detect trace metal ions in real
samples with satisfactory selectivity and results. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:18
Main heading:Electrochemical electrodes
Controlled terms:Bismuth - Cadmium - Cadmium compounds - Chemical detection - Cyclic voltammetry - Electrochemical impedance spectroscopy -
Electrodes - Electron transitions - Heavy metals - Ionic liquids - Liquids - Metal ions - Metals - Trace elements - Voltammetry
Uncontrolled terms:Electrochemical determination - ELectrochemical methods - Heavy metal determination - Linear detection ranges - Nano-Fe -
Screen-printed carbon electrodes - Square wave voltammetry - Square-wave stripping voltammetry
Classification code:531 Metallurgy and Metallography - 531.1 Metallurgy - 549.3 Nonferrous Metals and Alloys excluding Alkali and Alkaline Earth
Metals - 801 Chemistry - 801.4.1 Electrochemistry - 804 Chemical Products Generally
DOI:10.6041/j.issn.1000-1298.2015.S0.015
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 5>
Accession number:20160301828003
Title:Research of 3D points cloud color correction method for fruit tree canopy
Authors:Guo, Cailing (1, 2); Zong, Ze (1); Zhang, Xue (1); Ma, Xiaodan (1); Liu, Gang (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Department of Electromechanical Engineering, Tangshan University, Tangshan, China
Corresponding author:Liu, Gang([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:27-34
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:There are large differences between the actual colour of fruit trees canopy and the point cloud from the ground three-dimensional laser
scanner in the complex outdoors environment. In order to solve this problem, a new color correction method for color correction was proposed.
Firstly, the Pearson coefficient and Spearman correlation coefficient were calculated to make sure that there are relationships among the R, G, B
values of the scanning spot and the R, G, B values of the 24-Patch Color Checker Chart Classic, the solar radiation values, the angle θ
between 24-Patch Color Checker Chart Classic and the earth, scan quality, and light direction variables. And then, with confidence level of 95%, an
R, G, B components multiple regression model was established by using the dual-sifting stepwise regression in statistical method. Lastly, the model
was used to correct three-dimensional points cloud colors. The experiment results show that, using the three-dimensional point correlation
regression model, the correlation coefficient between the three-dimensional point cloud color R, G, B value and the real R, G, B values increased
from less than 0.69 to above 0.90 after correction, and color-corrected standard deviation fell significantly from above 50% to below 13%. The
method could be used to provide a theoretical basis for terrestrial laser scanning to obtain more accurate three-dimensional color points cloud.
© 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:26
Main heading:Color
Controlled terms:Electromagnetic wave attenuation - Forestry - Fruits - Laser applications - Linear regression - Orchards - Regression analysis
Uncontrolled terms:3D point cloud - Color correction - Fruit trees - Multiple linear regressions - Spearman correlation coefficients - Terrestrial
laser scanning - Three-dimensional laser scanners - Three-dimensional point clouds
Classification code:711 Electromagnetic Waves - 741.1 Light/Optics - 744.9 Laser Applications - 821.3 Agricultural Methods - 821.4 Agricultural
Products - 922.2 Mathematical Statistics
DOI:10.6041/j.issn.1000-1298.2015.S0.005
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 6>
Accession number:20160301827999
Title:Research of INS/GNSS heading information fusion method for agricultural machinery automatic navigation system
Authors:Zhang, Jing (1); Chen, Du (2); Wang, Shumao (1); Yu, Zhenjun (3); Wei, Liguo (4); Jia, Quan (4)
Author affiliation:(1) College of Engineering, China Agricultural University, Beijing, China; (2) Beijing Laboratory of Modern Agricultural
Equipment Optimization Design, Beijing, China; (3) Beijing Agricultural Machinery Experiment Appraisal Popularize Station, Beijing, China; (4)
Chinese Academy of Agricultural Mechanization Sciences, Beijing, China
Corresponding author:Chen, Du([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:1-7
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In the field operation of agricultural machinery automatic navigation system, the windbreak trees of field edge will have strong
disturbance to the satellite signal. Because of the requirement of accuracy for agricultural machinery navigation and the general automatic
navigation system has poor resistance to environmental interference, the algorithm of heading information fusion was studied based on the integrated
navigation system of INS/GNSS. This algorithm adopted adaptive Kalman filter to reduced the noise with the measurement of heading data for single
antenna GNSS and obtained the error estimation of heading data by using compensated Kalman filter. According to the quality of GNSS signal and the
heading angle gradient, it could also reasonably allocate the weights of different fusion data by the calculation of federated filter. The results
of simulation experiment and actual application test showed that: taking the heading measured value of double antenna GNSS as the reference data,
the average absolute error of fusion heading data was -0.02° and the standard deviation was 0.50° in the process of linear driving. In the
process of steering driving, the average absolute error of fusion heading data was 0.62° and the standard deviation was 2.42°. The accuracy
of heading output after fusion had an obvious improvement when compared with using INS or GNSS separately. The noise of heading measured value of
GNSS was filtered from the output of fusion heading and the update rate of GNSS calculated value was increased at the same time. The algorithm of
heading information fusion could enhance the accuracy of the automatic navigation system for agricultural machinery and give full play to the
advantages of the INS/GNSS integrated navigation system. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:20
Main heading:Global positioning system
Controlled terms:Agricultural machinery - Agriculture - Air navigation - Antennas - Errors - Inertial navigation systems - Information fusion -
Kalman filters - Navigation systems - Satellite antennas - Statistics
Uncontrolled terms:Adaptive kalman filter - Automatic navigation systems - Average absolute error - Environmental interference - Global Navigation
Satellite Systems - Heading angles - Information fusion method - Integrated navigation systems
Classification code:431.5 Air Navigation and Traffic Control - 716 Telecommunication; Radar, Radio and Television - 821 Agricultural Equipment and
Methods; Vegetation and Pest Control - 821.1 Agricultural Machinery and Equipment - 903.1 Information Sources and Analysis - 922.2 Mathematical
Statistics
DOI:10.6041/j.issn.1000-1298.2015.S0.001
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 7>
Accession number:20160301828007
Title:Maize plant type parameters extraction based on depth camera
Authors:Zong, Ze (1, 2); Guo, Cailing (1); Zhang, Xue (1); Ma, Li (1); Liu, Gang (1); Yi, Jinggang (2)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Mechanical and Electrical Engineering College, Agricultural University of Hebei, Baoding, China
Corresponding author:Liu, Gang([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:50-56
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:During the whole crop growing period and high-precision breeding process, measuring crop plant type parameters to achieve its phenotypic
analysis is one of the important link. In view of the problem that the maize plant type parameters are obtained mainly by artificial field
measurement in China at present, and it is high labor intensity and time-consuming. Thus a rapid and nondestructive measurement method of maize
plant type parameters was proposed based on the photonics mixer device (PMD) camera with improved skeleton extraction algorithm. Firstly, the RGB
pseudo color depth and distance information of depth image were used, and the depth of the image skeleton extraction was obtained by the improved
skeleton extraction algorithm without complex background interference. Secondly, the binary skeleton image per corn plant was got by taking
advantage of the improved corner detection classification algorithm for extracting skeleton image feature points. Finally, the feature points in the
skeleton image were corresponded with depth images. Three kinds of corn plant type parameters: plant height, stem diameter and leaf angle were
calculated by using mathematics method combined with space geometry feature point. Field test of the method in the practical application environment
showed that the plant height could be measured at the seedling stage of maize plant, the correlation coefficient (r) of maize plant type parameters
between the measured results by using the proposed method and the results measured artificially was 0.986, and the maximum relative error was less
than 2 cm. Farmland crops breeding resistance analysis also showed that maize plant type parameters and lodging resistance had significant
correlation. The results provide technical support for the inversion of phenotypic analysis of crop breeding. © 2015, Chinese Society for
Agricultural Machinery. All right reserved.
Number of references:16
Main heading:Parameter estimation
Controlled terms:Cameras - Crops - Cultivation - Edge detection - Extraction - Image processing - Musculoskeletal system - Nondestructive
examination - Plants (botany)
Uncontrolled terms:Depth image - Lodging resistance - Maize - Plant types - Time-of-flight cameras
Classification code:461.3 Biomechanics, Bionics and Biomimetics - 742.2 Photographic Equipment - 802.3 Chemical Operations - 821.3 Agricultural
Methods - 821.4 Agricultural Products
DOI:10.6041/j.issn.1000-1298.2015.S0.009
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 8>
Accession number:20160301828036
Title:Counting method of wheatear in field based on machine vision technology
Authors:Fan, Mengyang (1, 2); Ma, Qin (1, 2); Liu, Junming (1, 2); Wang, Qing (1, 2); Wang, Yue (1, 2); Duan, Xiongchun (1, 2)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China; (2) Key Laboratory of
Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
Corresponding author:Ma, Qin([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:234-239
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Wheat is a main crop in China and the timely and accuracy estimation of wheat yield is significant. The number of wheater in certain area
is an important element in wheat yield estimation. The counting method of wheatear based on machine vision technology was studied, which was cheap
and suitable for local area. The method was very significant for wheat growth monitoring and yield estimation. Firstly, the counting method for
wheatear in field based on machine vision technology was studied by collecting images of wheatear colony with cameras deployed in the field. The
analysis method for wheatear image feature, the thinning method for wheat ear outline and wheatear counting method based on skeleton were realized.
The low resolution images of wheat plant were collected with cameras deployed in field. Then the color features and texture features of images were
extracted. The outline of wheatear was extracted to get binary image of wheatear by using learning method of SVM. The database of wheatear feature
was constructed at the same time and wheatear skeletons were generated by thinning the wheatear binary image. Finally, the number of wheatears was
calculated by calculating the number of skeletons and skeleton intersection points. The method was tested in Zhaohe Demonstration Area, Fangcheng
County, in May of 2014 and 2015. As a result, it took averagely only 1.7 s to calculate the number of wheatears and the accuracy was 93.1%, which
means the wheatear counting method presented meets the requirement of both speed and accuracy, and it can provide reliable data for wheat yield
estimation. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:14
Main heading:Image thinning
Controlled terms:Binary images - Bins - Cameras - Computer vision - Image processing - Musculoskeletal system - Support vector machines
Uncontrolled terms:Accuracy estimation - Growth monitoring - Image features - Intersection points - Low resolution images - Skeleton - Wheatear -
Yield estimation
Classification code:461.3 Biomechanics, Bionics and Biomimetics - 694.4 Storage - 723 Computer Software, Data Handling and Applications - 742.2
Photographic Equipment
DOI:10.6041/j.issn.1000-1298.2015.S0.038
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 9>
Accession number:20160301828042
Title:Simulation of air temperature within winter wheat near-ground layer based on SHAW model
Authors:Liu, Junming (1); Wang, Nian (1); Wang, Pengxin (1); Hu, Xin (2); Huang, Jianxi (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China; (2) Wheat Research
Institute, Shangqiu Academy of Agriculture and Forestry Sciences, Shangqiu, China
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:274-282
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:The air temperature within near-ground layer is an important surrounding factor that can affect winter wheat growth. The simultaneous heat
and water (SHAW) model, which is a detailed process model of heat and water movement in the plant-snow-residue-soil system, was evaluated in
simulating the air temperature within near-ground layer from 0 cm to 40 cm at after-jointing stage of winter wheat. Field experiment was taken in
Shangqiu City, Henan Province to observe the winter wheat growth and surrounding factors, such as air temperature. The SHAW model was calibrated and
driven with inputs of part of field experiment data and empirical parameters. The results showed that the SHAW model performed well in simulating
air temperature within near-ground layer in winter wheat field, with 48% of the absolute error of simulated values was less than 1, 75% of the
absolute error of simulated values was less than 2, and the model efficiency at different heights was higher than 0.94. The simulated values had
higher biases during the day than those at night and they were increased with the increase of height from ground, and their biases generally reached
the largest value during 11:00 and 14:00. The daily mean values of the simulated and observed air temperature values were basically the same, while
the daily lowest values were overestimated and the daily highest values were underestimated. The model had better effects at jointing, filling and
dough stages than those at booting, blooming and heading stages. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:23
Main heading:Atmospheric temperature
Controlled terms:Crops - Shaw process
Uncontrolled terms:Air temperature - Different heights - Empirical parameters - Model efficiency - Near-ground layer - Shaw model - Winter wheat -
Winter wheat field
Classification code:443.1 Atmospheric Properties - 534.2 Foundry Practice - 821.4 Agricultural Products
DOI:10.6041/j.issn.1000-1298.2015.S0.044
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 10>
Accession number:20160301828014
Title:Development of movable acquisition system for soil optical-electrical parameters
Authors:Pei, Xiaoshuai (1); Sun, Hong (1); Zheng, Lihua (1); Li, Minzan (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China
Corresponding author:Li, Minzan([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:90-95
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Fast and efficient access to the information of farmland is the basis of precision agriculture. Soil is an important part of agriculture
for crop growth. Soil electrical conductivity is used for measuring the capability of soil conduct current. In addition to soil texture, soil
electrical conductivity is also capable of reflecting the properties of moisture content, salinity, organic content, etc. Soil spectral data can be
used for analyzing soil moisture, nutrient content, etc., without sampling or stirring the soil. The correction of soil electrical conductivity and
spectral data can improve the accuracy of the system. Based on the embedded technology, a soil electrical conductivity and soil spectral reflectance
integrated detection system was developed. Based on the improved “current-voltage four terminal” principle, the soil electrical
conductivity system used subsoiling plow as electrode sensor. It could conduct measurement and loose soil as well. STS-NIR miniature spectrometers
were used for acquiring the real-time spectral reflectance data. GPS information was acquired and saved synchronously with soil electrical
conductivity data and spectral reflectance data for soil characteristics distribution. The system can handle a variety of data real-timely, and
display, save data synchronously, and it has good application prospects. © 2015, Chinese Society for Agricultural Machinery. All right
reserved.
Number of references:18
Main heading:Soil surveys
Controlled terms:Agriculture - Electric conductivity - Moisture - Reflection - Soil moisture - Soils
Uncontrolled terms:Electrical parameter - Global position systems - Miniature Spectrometer - Precision Agriculture - Soil characteristics - Soil
electrical conductivity - Soil spectral reflectance - Spectral reflectances
Classification code:483.1 Soils and Soil Mechanics - 701.1 Electricity: Basic Concepts and Phenomena - 821 Agricultural Equipment and Methods;
Vegetation and Pest Control
DOI:10.6041/j.issn.1000-1298.2015.S0.016
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 11>
Accession number:20160301828032
Title:Tomato photosynthetic rate prediction models under interaction of CO<inf>2</inf>enrichments and soil moistures
Authors:Li, Ting (1); Ji, Yuhan (1); Zhang, Man (1); Sha, Sha (1); Jiang, Yiqiong (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China
Corresponding author:Zhang, Man([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:208-214
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Photosynthesis is the basis of crop growth and metabolism. CO<inf>2</inf>concentration and soil moisture are the important environmental
factors affecting plant's photosynthetic rate under controlled temperature and light intensity in greenhouse. To effectively evaluate the effect on
plant's photosynthesis, reasonably elevating CO<inf>2</inf>concentration under different soil moisture conditions is of great significance to
achieve precision regulation of CO<inf>2</inf>concentration. To achieve the requirements, the photosynthetic rate prediction models based on back-
propagation (BP) neural network were proposed at different growth stages of tomato plants. The two-factors interaction experiment was designed, in
which the CO<inf>2</inf>concentration was set to four different levels ((700±50) (C1), (1000±50) (C2), (1300±50) μmol/mol
(C3), and ambient CO<inf>2</inf>concentration in greenhouse (450 μmol/mol, CK)) combined with three different soil moisture levels (35%~45%
(low), 55%~65% (moderate), 75%~85% of saturated water content (high)). The sensor nodes of WSN were used to realize the real-time monitoring of
greenhouse environmental factors, including air temperature and humidity, light intensity, CO<inf>2</inf>concentration and soil moisture. An LI-
6400XT photosynthesis analyzer was used to measure net photosynthetic rate of tomato leaf. The environmental factors were used as input variables of
models after processed by normalization, and the photosynthetic rate was taken as the output variable. The model verification test was conducted by
comparing and analyzing the observed values and predicted data. The results indicated that the training determination coefficient (R<sup>2</sup>) of
photosynthesis prediction model was 0.953, and root mean square error (RMSE) was 1.019 μmol/(m<sup>2</sup>·s); testing R<sup>2</sup>of the
model was 0.925|, RMSE was 1.224 μmol/(m<sup>2</sup>·s) at seedling stage. At flowering stage, the training R<sup>2</sup>of the model was
0.958 and RMSE was 0.939 μmol/(m<sup>2</sup>·s); testing R<sup>2</sup>of the model was 0.920 and RMSE was 1.276 μmol/
(m<sup>2</sup>·s). At fruiting stage, the training R<sup>2</sup>of the model was 0.980 and RMSE was 0.439 μmol/(m<sup>2</sup>·s);
testing R<sup>2</sup>of the model was 0.958 and RMSE was 0.722 μmol/(m<sup>2</sup>·s). It was concluded that the model based on BP neural
network reached high accuracy. Furthermore, the relationship between CO<inf>2</inf>concentration and photosynthetic rate was described by the
established BP neural network model aiming at CO<inf>2</inf>saturation points under different soil moisture conditions at different growth stages.
The observed and predicted results showed the same trend. The results can provide a theoretical basis for quantitative regulation of
CO<inf>2</inf>enrichments to tomato plants in greenhouse. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:19
Main heading:Carbon dioxide
Controlled terms:Atmospheric composition - Backpropagation - Forecasting - Fruits - Greenhouses - Mean square error - Moisture - Neural networks -
Photosynthesis - Sensor nodes - Soil moisture - Soils - Water content - Wireless sensor networks
Uncontrolled terms:Back propagation neural networks - BP neural network model - Determination coefficients - Different growth stages - Net
photosynthetic rate - Photosynthetic rate - Root mean square errors - Tomato
Classification code:483.1 Soils and Soil Mechanics - 722 Computer Systems and Equipment - 722.3 Data Communication, Equipment and Techniques - 723.4
Artificial Intelligence - 741.1 Light/Optics - 801 Chemistry - 804.2 Inorganic Compounds - 821.4 Agricultural Products - 821.6 Farm Buildings and
Other Structures - 922.2 Mathematical Statistics
DOI:10.6041/j.issn.1000-1298.2015.S0.034
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 12>
Accession number:20160301828043
Title:Phenotype feature selection for crop breeding evaluation based on ranking relevance
Authors:Liu, Zhongqiang (1, 2); Zhao, Xiangyu (3); Wang, Kaiyi (4); Li, Minzan (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) National Engineering Research Center for Information Technology in Agriculture, Beijing, China; (3) Key Laboratory
of Agri-informatics, Ministry of Agriculture, Beijing, China; (4) Beijing Engineering Research Center of Agricultural Internet of Things, Beijing,
China
Corresponding author:Li, Minzan([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:283-289
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Traditional breeding evaluation methods focus on information of crop traits, while ignoring the previous evaluation results. In order to
enhance the efficiency of material evaluation under the condition of large-scale breeding, the comprehensive evaluation of crop traits was
integrated into the breeding evaluation, and a method of phenotype feature selection for crop breeding evaluation based on ranking relevance was
proposed. Firstly, the training sample set and the candidate feature set were selected from breeding data, and the correlation between the phenotype
feature and the results of evaluation and the similarity of the agronomic traits were calculated. Then, considering the characteristics of the
maximum correlation coefficient and the minimum similarity, a model of phenotype feature selection for breeding evaluation based on ranking
relevance was constructed. The established model can be used for different breeding objectives focusing on collection of characters, and validity of
the model was verified by using three kinds of soybean identification trial in 2013. The model also can be used as the preprocess of breeding
evaluation to determine the weights of the traits accurately. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:23
Main heading:Feature extraction
Controlled terms:Crops
Uncontrolled terms:Breeding evaluation - Breeding objectives - Comprehensive evaluation - Efficiency of materials - Evaluation results - Information
breeding - Maximum correlation coefficient - Ranking relevance
Classification code:821.4 Agricultural Products
DOI:10.6041/j.issn.1000-1298.2015.S0.045
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 13>
Accession number:20160301828001
Title:Applied research on John Deere AutoTrac automatic navigation's versatility
Authors:Jin, Jundong (1, 2); Zhao, Zuoxi (1, 2); Huang, Peikui (1, 2); Li, Yelin (1, 2); Ke, Xinrong (1, 2)
Author affiliation:(1) Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural
University, Guangzhou, China; (2) College of Engineering, South China Agricultural University, Guangzhou, China
Corresponding author:Zhao, Zuoxi([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:15-20
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:John Deere AutoTrac automatic navigation system is an advanced and cost-effective navigation system. But this kind of systems is designed
for John Deere's tractor, it is not suitable for general vehicles. This paper proposes a mechanical modifications method based on XUV825i utility
vehicle to solve the problem that ATU (AutoTrac universal) steering wheel can not be installed at utility vehicle. The mechanical modification
method that installing a special joints between utility vehicle steering axis and ATU steering wheel can improve the concentricity, making utility
vehicle under the control of AutoTrac automatic navigation system with high navigation precision. XUV825i utility vehicle's structure is as same as
general vehicles so that the mechanical modification method can also be adapted to the general vehicle. Finally, an navigation tests were carried
out based on the modified utility vehicle. During the experiment, the path under manual driving was as same as the path under AutoTrac automatic
navigation's control driving. The average lateral deviation of the machine's navigation path was less than 2 cm when the navigation system had been
tested well and was working under the good road condition. This indicaties that the mechanical modification method is feasible and it does not
affect the navigation accuracy of the AutoTrac automatic navigation system. At the same time, it shows that the mechanical modification method is
applicable to general vehicles, that means, AutoTrac automatic navigation system can be used on general vehicles. © 2015, Chinese Society for
Agricultural Machinery. All right reserved.
Number of references:15
Main heading:Navigation systems
Controlled terms:Automobile steering equipment - Cost effectiveness - Steering - Vehicle wheels - Vehicles - Wheels
Uncontrolled terms:Applied research - Automatic navigation - Automatic navigation systems - AutoTrac - Lateral deviation - Modification methods -
Navigation accuracy - Navigation precision
Classification code:601.2 Machine Components - 662.4 Automobile and Smaller Vehicle Components - 911.2 Industrial Economics
DOI:10.6041/j.issn.1000-1298.2015.S0.003
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 14>
Accession number:20160301828008
Title:Regional planning in GNSS-controlled land leveling based on spatial clustering method
Authors:Niu, Dongling (1); Li, Xiao (2); Kang, Xi (2); Liu, Gang (1, 2)
Author affiliation:(1) Key Laboratory for Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing, China; (2) Key
Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, China
Corresponding author:Liu, Gang([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:57-62
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:The land precision leveling technology could improve farmland micro-topography and distribution uniformity of irrigation water and soil
nutrients, save water and fertilizer and increase both production and income. To improve the working efficiency of GNSS-controlled land leveling
system, a method of farmland regional planning based on spatial clustering was explored. Firstly, a topography contour map was generated by using
terrain rapid measurement method. Farmland altitude data was obtained by the topography map. Secondly, the altitude data was divided into different
categories by using K-means clustering algorithm, and the data in the space was marked by each category. Thirdly, the discrete marked data belonged
to each category in the space was merged and error points were removed by using density clustering algorithm. According to the principle of land
leveling, regional division was finished by adjusting the amount of digging earth and the size of the farmland area. Finally, farmland experiments
were carried out and the results showed that the proposed method could be used to guide the driving well in the process of land leveling. The sum
proportion of overload and empty load during the time when the land was planning and navigation functions were working was no more than 5%, which
was better than the situation without these functions. The condition of farmland terrain under each region was improved significantly after being
leveled. The value of the standard deviation of altitude was less than 6 cm and it could meet the preliminary goal. © 2015, Chinese Society for
Agricultural Machinery. All right reserved.
Number of references:16
Main heading:Clustering algorithms
Controlled terms:Farms - Irrigation - Leveling (machinery) - Nutrients - Planning - Regional planning - Topography
Uncontrolled terms:Clustering analysis - Distribution uniformity - K-Means clustering algorithm - Land leveling - Navigation functions - Precision
leveling - Standard deviation - Working efficiency
Classification code:403.2 Regional Planning and Development - 603.1 Machine Tools, General - 821 Agricultural Equipment and Methods; Vegetation and
Pest Control - 821.3 Agricultural Methods - 903.1 Information Sources and Analysis - 912.2 Management - 951 Materials Science
DOI:10.6041/j.issn.1000-1298.2015.S0.010
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 15>
Accession number:20160301828022
Title:Identification of cabbage ball shape based on machine vision
Authors:Li, Hongqiang (1, 2); Sun, Hong (1); Li, Minzan (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, China Agricultural University, Beijing, China;
(2) Schoolof Science, Hebei Institute of Architecture and Civil Engineering, Zhangjiakou, China
Corresponding author:Li, Minzan([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:141-146
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:The head cabbage has three types according to its external ball shape, i.e., tip, flat and round shape types. The traditional
identification method of cabbage ball shape is done artificially. A new method for rapid identification of cabbage ball shape was proposed using
machine vision technology combined with BP neural network. Firstly, four absolute cabbage shape parameters were extracted, such as height, width,
long axis and area, based on image processing technology. Five relative shape parameters were defined based on the above absolute parameters, which
were ratio of height to width, circular degree, rectangle degree, ellipse degree and dome shape index. These nine parameters were used to describe
the cabbage shape. Since the parameter ranges overlapped, the individual parameter did not have separating classification ability. Secondly, three
recognition models of cabbage ball shape with BP neural network were established using three types of input datasets, four absolute parameters (long
axis, height, width, area), five relative parameters (ratio of height to width, circular degree, rectangle degree, ellipse degree, dome shape index)
and all above nine parameters. Each network had ten neurons in implicit layer, three neurons in output layer. Scaled conjugate gradient algorithm
was used to train the network. The test results showed that the prediction accuracy of BP neural network model took four absolute parameters as the
input was 62.5%, and the prediction accuracies of other two models were 100%. The model with relative parameters was relatively small and simple,
and could shorten the time of network computing. Meanwhile, the center distance values of every two type training sample groups were computed, and
the result showed that the model with all nine parameters had the biggest distance, which made the network be adapted to a wider sample spherical
recognition. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:14
Main heading:Image processing
Controlled terms:Computer vision - Domes - Neural networks - Pattern recognition
Uncontrolled terms:Ball shape - BP neural network model - BP neural networks - Cabbage - Classification ability - Image processing technology -
Machine vision technologies - Scaled conjugate gradient algorithm
Classification code:408.2 Structural Members and Shapes - 723.5 Computer Applications
DOI:10.6041/j.issn.1000-1298.2015.S0.024
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 16>
Accession number:20160301828028
Title:Segmentation of thermal infrared image for sow based on improved convex active contours
Authors:Ma, Li (1, 2); Duan, Yuyao (2); Zong, Ze (1); Liu, Gang (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) College of Information Science and Technology, Agricultural University of Hebei, Baoding, China
Corresponding author:Liu, Gang([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:180-186
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to solve the on-line detection of the body surface temperature for sow based on thermal infrared video, the image segmentation
method for the fast and efficient target detection was proposed. The thermal infrared image of the sow has the features of low pixel, low contrast
and edge blur. In piggery environment conditions, the sow body temperature and background radiance were the main factors to affect thermal infrared
image brightness and handling results. Because of the strong correlation between the intensity of the background radiation and light intensity, in
order to study the effect of background radiation on the thermal infrared image segmentation, the thermal infrared images of different illumination
intensities were collected. Firstly, the point operation was used to enhance the contrast enhancement; and then, instead of a constant value for
ω, a weight function that varies dynamically with the global and local contrast of the image was chosen, so as to dynamically balance the
global energy and the local energy; finally, an improved LGIF model was established with the global fitting energy and the local energy. 300 thermal
infrared images were collected by using infrared thermal imaging system, and the image segmentation experiments were performed. These pictures were
taken in different positions, light conditions, and sow varieties. Classification tests were carried out under three conditions of low light
intensity (100~600 lx), middle illumination (600~1000 lx) and high illumination (1500~2500 lx). In order to analyze the accuracy and real-time
performance of the algorithm, the average running time and the correct segmentation rate of different segmentation algorithms were calculated
respectively. The cause of the poor effect of the partial sample was analyzed, and the direction of improvement was put forward. Experimental
results show that the improved method can extract the sow more efficiently, and the average single image segmentation time was 49.67 s, the correct
segmentation rate reached more than 98% which demonstrated the accuracy and superiority of the proposed model. © 2015, Chinese Society for
Agricultural Machinery. All right reserved.
Number of references:19
Main heading:Image segmentation
Controlled terms:Atmospheric temperature - Infrared imaging - Infrared radiation - Radiation
Uncontrolled terms:Active contours - Contrast Enhancement - Direction of improvement - Infrared thermal imaging systems - Segmentation algorithms -
Sow - Thermal infrared images - Thermal infrared videos
Classification code:443.1 Atmospheric Properties - 741.1 Light/Optics - 746 Imaging Techniques
DOI:10.6041/j.issn.1000-1298.2015.S0.030
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 17>
Accession number:20160301828004
Title:Research on 3D reconstruction of fruit tree and fruit recognition and location method based on RGB-D camera
Authors:Mai, Chunyan (1); Zheng, Lihua (1); Sun, Hong (1); Yang, Wei (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China
Corresponding author:Zheng, Lihua([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:35-40
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to provide a scientific and reliable technical guidance for fruit harvesting robot in orchard, a method was proposed in this paper
to reconstruct 3D image for apple tree and carry out recognition and location for each apple fruit. Firstly, the color image and depth image of the
fruit trees were taken by an RGB-D camera, and the 3D reconstruction of each fruit tree was carried out by fusing its color and depth information.
Then, 3D point cloud of the fruit region were segmented from tree's point cloud by applying the color threshold of R-G. Finally, the 3D shape of
each fruit point cloud was extracted and its 3D spatial position information and radius were also obtained by using iteratively the RANSAC (Random
sample consensus) algorithm to fit each fruit to a pre-defined apple model. The experimental results showed that the proposed method of 3D
reconstruction of apple tree and recognition and location of its fruits had good real-time performance and strong robustness. In the measurement
range of 0.8~2.0 m, the correct recognition rates of fruits under frontlighting and backlighting conditions were 95.5% and 88.5% respectively, and
the correct recognition rate was 87.4% in the case that the sheltered area of fruit point clouds was over 50%, besides, the average position
calculation error of the fruit was 8.1 mm, and the average radius calculation error was 4.5 mm. © 2015, Chinese Society for Agricultural
Machinery. All right reserved.
Number of references:31
Main heading:Three dimensional computer graphics
Controlled terms:Cameras - Color - Forestry - Fruits - Image processing - Image reconstruction - Iterative methods - Location - Orchards
Uncontrolled terms:3D reconstruction - Harvesting robot - Point cloud - Recognition - Rgb-d cameras
Classification code:723.2 Data Processing and Image Processing - 741.1 Light/Optics - 742.2 Photographic Equipment - 821.3 Agricultural Methods -
821.4 Agricultural Products - 921.6 Numerical Methods
DOI:10.6041/j.issn.1000-1298.2015.S0.006
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 18>
Accession number:20160301828037
Title:Retrieving vegetation coverage index of winter wheat based on image colour characteristic
Authors:Sun, Hong (1); Wen, Yao (1); Zhao, Yi (2); Li, Minzan (1, 2); Chen, Jun (3); Yang, Wei (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, China; (3)
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling; Shaanxi, China
Corresponding author:Li, Minzan([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:240-245
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to rapidly acquire winter wheat growing information in the field, the retrieval method of vegetation coverage index(VCI) was
researched based on multi-spectral imaging technique and imaging processing technology. Firstly, a 2-CCD multi-spectral image monitoring system was
used to acquire the canopy images. The system was based on a dichroic prism, allowing precise separation of the visible (RGB) and near-infrared
(NIR) band. Secondly, after the image smoothing using adaptive smooth filtering algorithm, the canopy image of winter wheat was segmented. HSI color
model and automated threshold method were used to segment the RGB and NIR image respectively. The hue threshold was [π/4, 6π/5]. The segmented
results of RGB and NIR were combined to improve the segmentation accuracy and the VCI was calculated. Thirdly, the image parameters were abstracted
based on the original visible and NIR images including the average gray value of each channel (A<inf>R</inf>, A<inf>G</inf>, A<inf>B</inf>) and
near-infrared (A<inf>NIR</inf>), the vegetation indices (NDVI, NDGI, RVI, DVI) which were widely used in remote sensing, and the H average value of
canopy. The correlation analysis results showed that the correlation coefficients between vegetation indices and VCI were above 0.90. As a result,
the retrieving multiple linear regressions (MLR) model was built by using NDVI, NDGI, RVI and DVI with R<inf>c</inf><sup>2</sup>=0.948 and
R<inf>v</inf><sup>2</sup>=0.884. It was feasible to diagnose vegetation coverage in the field and indicate the growth status. © 2015, Chinese
Society for Agricultural Machinery. All right reserved.
Number of references:21
Main heading:Image acquisition
Controlled terms:Color - Crops - Image segmentation - Imaging techniques - Infrared devices - Remote sensing - Spectroscopy - Vegetation
Uncontrolled terms:Correlation analysis - Correlation coefficient - Multiple linear regressions - Multispectral images - Multispectral imaging -
Segmentation accuracy - Vegetation coverage - Vegetation index
Classification code:723 Computer Software, Data Handling and Applications - 741.1 Light/Optics - 746 Imaging Techniques - 821.4 Agricultural
Products
DOI:10.6041/j.issn.1000-1298.2015.S0.039
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 19>
Accession number:20160301828026
Title:Online monitoring system for water quality parameters based on ZigBee
Authors:Li, Xinxing (1); Wang, Cong (1, 2); Tian, Ye (1, 3); Lü, Xiongjie (2); Fu, Zetian (3, 4); Zhang, Lingxian (1, 3)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China; (2) Information Institute,
Tianjin Academy of Agricultural Sciences, Tianjin, China; (3) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of
Agriculture, Beijing, China; (4) Beijing Laboratory of Food Quality and Safety, Beijing, China
Corresponding author:Zhang, Lingxian([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:168-173
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Dissolved oxygen, pH value, conductivity, temperature, etc. are key factors for analyzing water quality. And, how to measure the key
factors real-timely is particularly important. It is impossible for traditional empirical methods and chemical detection to satisfy production
requirements today. However, with the development of intelligent and networked sensors, wireless network technology can satisfy the requirements of
accuracy and real-time in water quality monitoring. ZigBee technology not only has characteristics of short distance, low complexity, powerful
networking capability, low cost and high stability, but also has self-owned wireless transmission standard in which multiple modes which can relay
each other achieve effective communication in measuring nodes, which fully meets the needs of the wireless water quality monitoring. This paper puts
forward wireless water quality monitoring system based on JN5139 ZigBee wireless module. The system gathers perception module, micro-control module
and wireless transmission module. Multiplex water quality parameters are collected periodically by wireless network, stored and finally displayed on
host computer. These data can also be checked by users after connecting JN5139-Z01-M02/4 system through computer. In case of buildings, trees and
obstruction, distance accuracy is 100 m at least. According to experiments, the system has high scalability, low power consumption, strong stability
and so on, which can meet the requirements of real-time and data accuracy in water quality monitoring. © 2015, Chinese Society for Agricultural
Machinery. All right reserved.
Number of references:13
Main heading:Monitoring
Controlled terms:Biochemical oxygen demand - Chemical detection - Complex networks - Dissolved oxygen - Water quality - Wireless networks - Zigbee
Uncontrolled terms:JN5139 - On-line monitoring system - Water monitoring - Water quality monitoring - Water quality monitoring systems - Wireless
network technologies - Wireless transmi-ssion modules - Wireless transmissions
Classification code:445.2 Water Analysis - 722 Computer Systems and Equipment - 722.3 Data Communication, Equipment and Techniques - 801 Chemistry
DOI:10.6041/j.issn.1000-1298.2015.S0.028
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 20>
Accession number:20160301828034
Title:Design and test of nutrition diagnosis system for wheat canopy based on spectroscopy
Authors:Zhao, Yi (1); Wen, Yao (1); Sun, Hong (1); Li, Minzan (1); Zhang, Meng (1); Wu, Lixuan (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture Integration Research, Ministry of Education, China Agricultural University,
Beijing, China
Corresponding author:Li, Minzan([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:222-227
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to realize precision management of wheat crop, a spectral analyzer was developed to diagnose crop canopy nutrition, and the
performance experiment was conducted in a wheat field. The system consisted of optical system, signal acquisition driver module and controller. The
optical sensor could collect spectral reflectance between 300 nm and 1100 nm. The signal acquisition driver module was used to provide a stable
voltage and A/D conversion. A spectral acquisition and control software was developed to receive, display, process and save the collected data.
Calibration experiments and field experiments were carried out in wheat experimental field. The correlation between the result measured by the
spectral analyzer and the result of ASD FieldSpec HandHeld 2 were analyzed. The results indicated that high correlation existed between the
reflectance datasets detected by the two equipments. The minimum coefficient of correlation was 0.9918. The correlation between the winter wheat
chlorophyll content and reflectance measured by instrument was analyzed. Selecting the higher correlation band at 550~900 nm, the principal
component analysis was applied to establish chlorophyll forecasting model. The determination coefficient R<sup>2</sup>of the calibration model was
0.575 and the R<sup>2</sup>of the validation model was 0.595. Results show that the developed instrument can effectively detect and evaluate
chlorophyll content of wheat canopy, and provide theoretical and technical support for the precision cultivation of wheat. © 2015, Chinese
Society for Agricultural Machinery. All right reserved.
Number of references:13
Main heading:Signal processing
Controlled terms:Agriculture - Analog to digital conversion - Chlorophyll - Crops - Cultivation - Nutrition - Optical systems - Principal component
analysis - Reflection - Signal analysis - Signal detection - Spectrum analyzers
Uncontrolled terms:Calibration experiments - Coefficient of correlation - Determination coefficients - Normalized difference vegetation index -
Performance experiment - Precision Agriculture - Spectral analyzers - Spectral reflectances
Classification code:461.7 Health Care - 716.1 Information Theory and Signal Processing - 741.3 Optical Devices and Systems - 804.1 Organic Compounds
- 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 922.2 Mathematical Statistics
DOI:10.6041/j.issn.1000-1298.2015.S0.036
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 21>
Accession number:20160301828023
Title:Image segmentation of underwater sea cucumber using GrabCut with saliency map
Authors:Guo, Chuanxin (1); Li, Zhenbo (1, 2); Qiao, Xi (1); Li, Chen (1); Yue, Jun (3)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China; (2) Key Laboratory of
Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, China; (3) College of Information and Electrical Engineering,
Ludong University, Yantai, China
Corresponding author:Li, Zhenbo([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:147-152
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to realize the automatic harvesting of sea cucumber and diagnose the disease of sea cucumber, first, the problem of the image
segmentation of sea cucumber under real aquaculture environment should be solved. In this paper, a new method of image segmentation of sea cucumber
using GrabCut with saliency map was proposed. This method improved the traditional GrabCut algorithm, enhanced underwater images through the single
scale Retinex algorithm. Based on global contrast based salient region detection method and histogram equalization, part of foreground and possible
background of regional image of sea cucumber could be obtained, the mask of GrabCut algorithm can be initialized using this information. At last,
GrabCut algorithm ran iterated to get the result of image segmentation. Experiment results proved that the proposed method can segment the sea
cucumber images more accurately than the Otsu method, the watershed method and the traditional GrabCut algorithm, and overcome the background noise
and preserve the details of the target image. The accuracy of the algorithm was 90.13%. © 2015, Chinese Society for Agricultural Machinery. All
right reserved.
Number of references:16
Main heading:Image segmentation
Controlled terms:Algorithms
Uncontrolled terms:GrabCut - Histogram equalizations - Retinex - Retinex algorithms - Saliency map - Salient region detections - Sea cucumber -
Watershed methods
DOI:10.6041/j.issn.1000-1298.2015.S0.025
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 22>
Accession number:20160301828000
Title:Design and implementation of a corn weeding-cultivating integrated navigation system based on GNSS and MV
Authors:Zhang, Man (1); Xiang, Ming (1); Wei, Shuang (2); Ji, Yuhan (2); Qiu, Ruicheng (1); Meng, Qingkuan (1)
Author affiliation:(1) Key Laboratory for Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, China
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:8-14
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to achieve the automatic navigation of corn weeding-cultivating operations and improve the efficiency and accuracy of automatic
weeding, a corn weeding-cultivating integrated navigation system based on GNSS and MV was designed and developed. The system, which consisted of two
parts, the agricultural vehicle automatic navigation based on GNSS and the weeding implement navigation based on machine vision, could achieve
automatic navigation in the process of corn weeding-cultivating. In order to avoid the damage corn, the proper safety distance between implement
moldboard and crop rows is necessary when the agricultural machinery is working, which is controlled by tracing the GNSS information of corn
planting navigation and the image information of crop rows acquired by industrial camera. During the hardware part, the steering wheel controlling
and the front wheel angle testing parts were designed and refitted. The steering control circuit and the implement moldboard hydraulic control
circuit were designed based on PLC and stepper motor drive. To the vehicle navigation, the steering wheel controlling and the front wheel angle
testing institutions were designed. The vehicle operated through tracing the corn row by the GNSS information of corn planting navigation. In the
integrated navigation intelligent decision module, the fuzzy control system was taken as the main control algorithm for navigation control
decisions, and the lateral deviation of agricultural machinery and lateral deviation error rate were used as input variables of fuzzy control. In
the implement visual navigation module, the crop row detection algorithm based on scanning filter was adopted, which could improve the precision of
navigation line detection and processing efficiency. Corresponding experiments were designed to test the feasibility of the system, the accuracy of
automatic navigation were calculated respectively. The results show that, in the speed of 0.6 m/s, the maximum lateral deviation of GNSS navigation
is 10.04 cm, the average deviation is 4.62 cm. The maximum lateral deviation of integrated navigation is 6.35 cm, the average deviation is 2.73 cm.
The corn weeding-cultivating integrated navigation system can effectively satisfy the requirement of the corn weeding. © 2015, Chinese Society
for Agricultural Machinery. All right reserved.
Number of references:18
Main heading:Global positioning system
Controlled terms:Accident prevention - Agricultural machinery - Agriculture - Air navigation - Algorithms - Automobile steering equipment - Computer
vision - Crops - Efficiency - Electric drives - Fuzzy control - Hydraulic machinery - Navigation systems - Plants (botany) - Reconfigurable
hardware - Steering - Stepping motors - Vehicles - Wheels
Uncontrolled terms:Agricultural vehicles - Corn Weeding-cultivating - Design and implementations - GNSS - Hydraulic control circuit - Integrated
navigation - Integrated navigation systems - Intelligent decisions
Classification code:431.5 Air Navigation and Traffic Control - 601.2 Machine Components - 632.2 Hydraulic Equipment and Machinery - 662.4 Automobile
and Smaller Vehicle Components - 705.3 Electric Motors - 721.3 Computer Circuits - 723.5 Computer Applications - 731 Automatic Control Principles
and Applications - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 913.1 Production Engineering - 914.1 Accidents and Accident
Prevention
DOI:10.6041/j.issn.1000-1298.2015.S0.002
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 23>
Accession number:20160301828030
Title:Measurement system of light intensity in solar greenhouse
Authors:Wang, Junheng (1); Li, Li (1); Mu, Yonghang (2); Wang, Haihua (1); Fu, Qiang (2)
Author affiliation:(1) Key Laboratory for Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, China
Corresponding author:Li, Li([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:194-200
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Light intensity is one of the indispensable factors for plant growth and transpiration. Real-time monitoring light intensity and guiding
irrigation by it can stimulate plant growth, simultaneously, and play a role in saving water and energy. In this paper, a practical light intensity
detection system was designed in a simple way with low-cost. The silicon solar panel was used as the solar radiation sensor, which could directly
transfer the sun's radiant energy into electric signals. The PIC16F876A MCU was used as the processor. The least square procedure was used to
establish the model between the output voltage of silicon solar panel and the light intensity, then experiment was carried out to verify the
performance of this system. The result showed that the average relative errors were around 1.19% in sunny days, around 1.57% in cloudy days, around
7.19% in rainy days, around 6.15% in changing weather, respectively. The average relative error was always below 10% in different weathers. The
system accuracy was increasing while the light intensity increased. The system worked more precise when the light intensity was above 15000 lx with
the average error of 1.41%. The resolution was 0.1 lx. The system repeatability error was very low (<0.63%), which means the system was running
in high stability. In summary, this system could work stably in the solar greenhouse in different weathers. © 2015, Chinese Society for
Agricultural Machinery. All right reserved.
Number of references:17
Main heading:Greenhouses
Controlled terms:Errors - Silicon - Solar cell arrays - Solar concentrators - Solar heating - Vaporization
Uncontrolled terms:Average relative error - Least-square procedure - Light intensity - Light intensity detection - Measurement system - Solar
greenhouse - Solar panels - Solar radiation sensors
Classification code:549.3 Nonferrous Metals and Alloys excluding Alkali and Alkaline Earth Metals - 657.1 Solar Energy and Phenomena - 702.3 Solar
Cells - 802.3 Chemical Operations - 821.6 Farm Buildings and Other Structures
DOI:10.6041/j.issn.1000-1298.2015.S0.032
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 24>
Accession number:20160301828015
Title:Prediction of soil nitrate-nitrogen based on sensor fusion
Authors:Ren, Haiyan (1); Zhang, Miao (2); Kong, Pan (2); Li, Yanhua (2); Pu, Pan (2); Zhang, Li'nan (1)
Author affiliation:(1) Key Laboratory on Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural
University, Beijing, China
Corresponding author:Zhang, Miao([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:96-101
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:The conventional method of soil nitrate-nitrogen prediction based on ion-selective electrode had the problem of complex soil suspension
components and the limited prediction accuracy and precision in single input variables. To improve the prediction accuracy and precision of soil
NO<inf>3</inf><sup>-</sup>-N concentration employing ion-selective electrodes (ISEs), the support vector machine (SVM) model of soil
NO<inf>3</inf><sup>-</sup>-N prediction based on sensor fusion was built. Grey relational analysis was applied to screen the major interference
factors, which had a great impact on the soil NO<inf>3</inf><sup>-</sup>-N detection employing ISEs, and the support vector machine (SVM) model
based on sensor fusion was built with the major factors. Then, the classical Nernst model and the SVM model with major factors and all considered
factors were compared with the conventional method. According to the testing results, EC values, temperature and Cl<sup>-</sup>were the three major
interference factors which had great influence on the prediction accuracy and precision of soil NO<inf>3</inf><sup>-</sup>-N concentration employing
ISEs. With the optimized input parameters of NO<inf>3</inf><sup>-</sup>-N ISE potentials, EC, temperature and Cl<sup>-</sup>ISE potentials, the
adjusted R<sup>2</sup>, average absolute error and root mean square error of the SVM model were 0.98, 3.38 mg/L and 4.51 mg/L, respectively. The SVM
model based on sensor fusion showed more advantages than the Nernst model and it could successfully achieve the prediction purpose of
NO<inf>3</inf><sup>-</sup>-N with high prediction accuracy and precision of the ISEs in soil extracted solution. © 2015, Chinese Society for
Agricultural Machinery. All right reserved.
Number of references:14
Main heading:Ion selective electrodes
Controlled terms:Chemical sensors - Data flow analysis - Electrodes - Forecasting - Ions - Mean square error - Nitrates - Nitrogen - Soil testing -
Soils - Support vector machines
Uncontrolled terms:Average absolute error - Conventional methods - Grey relational analysis - Interference factor - Nitrate nitrogen - Root mean
square errors - Sensor fusion - Single input variable
Classification code:483.1 Soils and Soil Mechanics - 723 Computer Software, Data Handling and Applications - 801 Chemistry - 802.1 Chemical Plants
and Equipment - 804 Chemical Products Generally - 804.2 Inorganic Compounds - 922.2 Mathematical Statistics
DOI:10.6041/j.issn.1000-1298.2015.S0.017
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 25>
Accession number:20160301828035
Title:Diagnosis of chlorophyll content in corn canopy leaves based on multispectral detector
Authors:Liu, Haojie (1); Zhao, Yi (2); Wen, Yao (1); Sun, Hong (1); Li, Minzan (1, 2); Zhang, Qin (3)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural
University, Beijing, China; (3) Center for Precision and Automated Agricultural Systems, Washington State University, Washington, United States
Corresponding author:Sun, Hong([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:228-233 and 245
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to rapidly detect the nutrition content of crop, a portable multispectral detector was developed. The detector was composed of a
controller and an optical sensor node, which communicated with each other through the ZigBee protocol. The optical sensor node can measure both the
intensity of solar light and crop reflective light at 550 nm, 766 nm, 650 nm and 850 nm wavebands, respectively. The control unit is a PDA, in which
a ZigBee wireless communication module is embedded. As the coordinator of the whole wireless sensor network, the ZigBee wireless communication
module is responsible for receiving, processing and displaying the spectral data transmitted by the measuring unit. The objective of the research
was to assess the agronomic performance of the detector, i.e., the accuracy of chlorophyll content estimation when using the instrument in different
arable crops. Field experiments were conducted on four varieties of corn (Nongda No. 8 (G1), Zhengdan (G2), Xianyu (G3) and Jingnongke (G4)). Crop
canopy reflectance was measured by the detector at jointing stage. The chlorophyll contents of sampling leaves were measured by the
spectrophotometer in the laboratory. According to the typical spectral characteristics of crop and soil, the differences of reflectance between 550
nm and 650 nm (T<inf>D</inf>) were used to remove the soil background data points (T<inf>D</inf>>0). Furthermore, combinations of
R<inf>nir</inf>and R<inf>r</inf>((850, 550), (850, 650), (766, 550) and (766, 650)) were used to calculate vegetation indices, including DVI, NDVI
and RVI. Relationship between each vegetation index and chlorophyll content of each variety was analyzed. The results showed that the optimal
parameters of G1~G4 were RVI (766, 550), DVI (850, 650), NDVI (850, 550) and RVI (766, 550), respectively, and the correlation coefficients were
above 0.6. The chlorophyll content of the four varieties was clustered respectively at intervals of 0.2 mg/L, 0.5 mg/L and 0.8 mg/L. The correlation
analysis results showed that the optimal resolution of the multispectral detector for detecting chlorophyll content of corn was 0.5 mg/L. The
correlation coefficients of NDVI (850, 550), NDVI (766, 550) and RVI (850, 550) and chlorophyll content were 0.8370, 0.7737 and 0.7677,
respectively. The NDVI (850, 550) and RVI (850, 550) were selected to establish the diagnosis model with R<inf>C</inf><sup>2</sup>of 0.7154 and
R<inf>V</inf><sup>2</sup>of 0.6840. The research could provide theoretical and technical support for the diagnosis of chlorophyll content of corn at
jointing stage. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:14
Main heading:Chlorophyll
Controlled terms:Crops - Forestry - Optical sensors - Reflection - Sensor nodes - Vegetation - Wireless sensor networks - Wireless telecommunication
systems - Zigbee
Uncontrolled terms:Canopy reflectance - Chlorophyll contents - Correlation coefficient - Crop canopy reflectance - Multi-spectral detector -
Spectral characteristics - Vegetation index - ZigBee wireless communication
Classification code:722 Computer Systems and Equipment - 722.3 Data Communication, Equipment and Techniques - 741.3 Optical Devices and Systems -
804.1 Organic Compounds - 821.4 Agricultural Products
DOI:10.6041/j.issn.1000-1298.2015.S0.037
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 26>
Accession number:20160301828025
Title:Fusion of WSN cluster head data based on improved K-means clustering algorithm
Authors:Gao, Hongju (1); Liu, Yanzhe (1); Chen, Sha (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:162-167
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Data fusion for wireless sensor networks (WSN) can reduce the energy consumption of sensor nodes and prolong the network lifetime, so that
it has attracted wide spread attention in a variety of applications. The existing algorithms for spatial data fusion that have been used in
agricultural monitoring always aggregate the data within a certain area into one value by means of averaging. However, in addition to redundancy
resulted from correlation, the sensed data also has variance due to larger monitoring area, more monitoring nodes and larger amount of data in
farmland environment. Hence, data fusion in farmland monitoring should retain the differences of data while eliminating the redundancy. The idea
that applying data fusion algorithm on WSN cluster head to aggregate spatially correlated data by clustering was proposed. While the parameters
whose values are quite different will be clustered into different categories so that differences between the data can be reserved. An improved
adaptive K-means clustering algorithm was proposed to be used in cluster head. Simulation results indicate that, the amount of data uploaded with
fusion algorithm was decreased by 41.19% compared with that without fusion algorithm, and the maximum error before and after the proposed fusion
algorithm is less than 36% of that before and after the averaging fusion method. When there is no clear accuracy requirement, the proposed algorithm
can reduce the amount of data uploaded and maintain the relative error less than 10%, avoiding enormous error caused by improper number of clusters.
When there are specific accuracy requirements, the relative error produced by the proposed algorithm can meet the error requirements strictly.
© 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:20
Main heading:Clustering algorithms
Controlled terms:Aggregates - Algorithms - Data fusion - Energy utilization - Errors - Farms - Monitoring - Redundancy - Sensor data fusion - Sensor
nodes - Wireless sensor networks
Uncontrolled terms:Agricultural monitoring - Data difference - Data fusion algorithm - Fusion algorithms - Improved K-Means algorithm - Improved k-
means clustering - K-Means clustering algorithm - Number of clusters
Classification code:406 Highway Engineering - 525.3 Energy Utilization - 722 Computer Systems and Equipment - 722.3 Data Communication, Equipment
and Techniques - 723.2 Data Processing and Image Processing - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 903.1
Information Sources and Analysis
DOI:10.6041/j.issn.1000-1298.2015.S0.027
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 27>
Accession number:20160301828031
Title:Design of CO<inf>2</inf>fertilizer optimizing control system on WSN
Authors:Ji, Yuhan (1); Li, Ting (1); Zhang, Man (1); Sha, Sha (1)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China
Corresponding author:Zhang, Man([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:201-207
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Carbon dioxide (CO<inf>2</inf>) is an important raw material of the plant photosynthesis. Increasing CO<inf>2</inf>fertilizer rationally
can improve the net photosynthetic rate of plant leaf, and further improve crop yield and quality. To achieve precision management of
CO<inf>2</inf>fertilizer in greenhouse, a greenhouse CO<inf>2</inf>fertilizer optimizing control system based on wireless sensor network (WSN) was
designed and developed. The whole system includes four monitoring and controlling nodes, an intelligent gateway and a remote management software.
The monitoring and controlling node, which connected to sensors and an electromagnet, can real time monitor greenhouse environmental parameters and
control the switch of CO<inf>2</inf>source according to the demand of crop. The intelligent gateway can process and transmit the data and commands
between nodes and remote management software. It can also storage and display environment parameters locally. Besides, user can control the
CO<inf>2</inf>source by gateway. The remote management software, which embeds photosynthetic rate prediction model, can not only process and
transmit the data, but also control CO<inf>2</inf>fertilizer remotely. To achieve precision management of CO<inf>2</inf>fertilizer supplement, it
was necessary to build an accurate and reliable net photosynthetic rate prediction model. The paper measured environment parameters by the system
above mentioned, and obtained single-leaf net photosynthetic rate by LI-6400XT photosynthesis analyzer. Then a photosynthetic rate prediction model
based on SVM was established. In order to improve the generality of prediction model, tomatoes in late seedling stage were cultivated in four
different fertilizer levels ((700±50)μmol/mol (C1), (1000±50)μmol/mol (C2), (1300±50)μmol/mol (C3), ambient about 450
μmol/mol (CK)). The photosynthetic rate prediction model was established by support vector machine (SVM). The environment parameters were used as
input variables, and the photosynthetic rate was taken as output variable. The performances of designed system and prediction model were evaluated.
The system can work stably and reliably, therefore it can be used to monitor environment information and control the CO<inf>2</inf>fertilizer in
solar greenhouse. The prediction results of the model showed that R between predicted and measured data was 0.9815 and RMSE was 1.0925 μmol/
(m<sup>2</sup>·s). According to the analysis, it was concluded that the prediction model can be good used as the basis of the quantitative
regulation of CO<inf>2</inf>fertilization to tomato plants in greenhouse. © 2015, Chinese Society for Agricultural Machinery. All right
reserved.
Number of references:15
Main heading:Carbon dioxide
Controlled terms:Carbon - Control systems - Crops - Digital storage - Fertilizers - Forecasting - Fruits - Gateways (computer networks) -
Greenhouses - Photosynthesis - Reconfigurable hardware - Remote control - Sensor nodes - Support vector machines - Wireless sensor networks
Uncontrolled terms:Controlling system - Display environments - Environment information - Environmental parameter - Intelligent gateway - Monitoring
and controlling - Net photosynthetic rate - Photosynthetic rate
Classification code:721.3 Computer Circuits - 722 Computer Systems and Equipment - 723 Computer Software, Data Handling and Applications - 731.1
Control Systems - 741.1 Light/Optics - 804 Chemical Products Generally - 804.2 Inorganic Compounds - 821.4 Agricultural Products - 821.6 Farm
Buildings and Other Structures
DOI:10.6041/j.issn.1000-1298.2015.S0.033
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 28>
Accession number:20160301828040
Title:Contrast of automatic geometric registration algorithms for GF-1 remote sensing image
Authors:Wang, Yuan (1); Ye, Sijing (1); Yue, Yanli (2); Liu, Diyou (1); Xiong, Quan (2); Zhu, Dehai (1)
Author affiliation:(1) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, China; (2) Key
Laboratory of Agricultural Land Quality (Beijing), Ministry of Land and Resources, China Agricultural University, Beijing, China
Corresponding author:Zhu, Dehai([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:260-266
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:The geometrical registration of remote sensing image is an important premise for the subsequent processing of image. And it's also an
important security for the application, such as agricultural condition monitoring. Different algorithms of automatic geometry registration lead to
various registration effects. It's hard to meet the registration requirements of all images. Four testing types of plains, mountains, summer and
winter were selected based the features of terrain and time. The main three registration methods were: cross correlation algorithm based on region
gray, mutual information algorithm based on region gray and SIFT algorithm based on features. SIFT feature is the partial feature of the image,
which can keep the invariance in rotating, scale-zooming and brightness changing. Then the automatic geometric registration was made for four
classes of GF-1 remote sensing image using the above three algorithms. Two kinds of experiments were conducted for GF-1 remote sensing image under
various conditions such as different terrains and different imaging time. The comparison of different geometric registration algorithms were made in
the aspects of accuracy, efficiency and stability. The results show that the SIFT algorithm is the most appropriate one. The visual edge effect is
good and the root mean square error reaches the magnitude of 10<sup>-5</sup>, which can satisfy the demand of precision. This method is simple and
efficient, and it can be applied into agricultural condition monitoring and other business efficiently. © 2015, Chinese Society for
Agricultural Machinery. All right reserved.
Number of references:19
Main heading:Remote sensing
Controlled terms:Agriculture - Algorithms - Condition monitoring - Geometry - Image reconstruction - Mean square error
Uncontrolled terms:Agricultural conditions - Cross-correlation algorithm - Geometric registrations - GF-1 - Precision evaluation - Registration
methods - Remote sensing images - Root mean square errors
Classification code:821 Agricultural Equipment and Methods; Vegetation and Pest Control - 921 Mathematics - 922.2 Mathematical Statistics
DOI:10.6041/j.issn.1000-1298.2015.S0.042
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 29>
Accession number:20160301828011
Title:Test and optimization of sampling frequency for yield monitor system of grain combine harvester
Authors:Li, Xincheng (1, 2); Li, Minzan (1); Zheng, Lihua (1); Zhang, Man (3); Wang, Xijiu (4); Sun, Maozhen (4)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao, China; (3) Key
Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China; (4) Huantai
County Agriculture Bureau, Zibo, China
Corresponding author:Li, Minzan([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:74-78
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Aimed at the questions caused by unsuitable sampling frequency, such as data redundancy, high cost in hardware and software produced by
high sampling frequency and the accuracy and stability which are hardly ensured under the condition of low sampling frequency, the grain impact
frequency and sampling theorem were considered, consequently, the maximum sampling frequency was determined at 50 Hz. In data preprocessing, for
high frequency sampling signal, two methods were put forward, one was an arithmetic average method, and the other was double threshold filtering
mean method. The analysis result showed that the effect of double threshold value filtering and average treatment was better than that of the
former. In order to investigate the influence of sampling frequency on yield estimation, the yield estimation tests of different sampling
frequencies were carried out, and a frequency extraction method was proposed to generate different sampling signals. Under four kinds of sampling
frequency as 1, 10, 25 and 50 Hz, four types of yield estimation models were established, and the prediction effects were also compared. The test
results showed that high accuracy of the estimated yield can be obtained with high sampling frequency; using 50 Hz sampling frequency, the lowest
average relative error was 3.04%; using sampling frequency higher than 10 Hz, it could ensure the average relative error was not higher than 5%. As
a result, it is necessary to adopt at least 10 Hz as the sampling frequency for estimation of grain yield. © 2015, Chinese Society for
Agricultural Machinery. All right reserved.
Number of references:14
Main heading:Frequency estimation
Controlled terms:Digital signal processing - Harvesters - Signal sampling
Uncontrolled terms:Arithmetic average methods - Double threshold values - Grain combines - High sampling frequencies - High-frequency sampling -
Sampling frequencies - Yield estimation - Yield measurement
Classification code:821.1 Agricultural Machinery and Equipment - 922 Statistical Methods
DOI:10.6041/j.issn.1000-1298.2015.S0.013
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 30>
Accession number:20160301828017
Title:Intelligent switcher design in water and fertilizer integration equipment
Authors:Fu, Qiang (1); Mei, Shuli (2); Li, Li (1); Wang, Junheng (1); Wang, Haihua (1, 2)
Author affiliation:(1) Key Laboratory on Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) College of Electrical and Information, China Agricultural University, Beijing, China
Corresponding author:Wang, Haihua([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:108-115
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:It was necessary to monitor liquid level in both mixing fertilizer tank and recycle tank respectively to design the integrating equipment
of water and fertilizer in the intelligent hydroponic control system for facility vegetable production. In practical engineering project, the liquid
level floater is widely used for the existing liquid level detection. The effect of the floater can be disturbed by some conditions, such as
waterlines fluctuation when the floater was too close to inlet or outlet. Shaking up and down of the floater result in pump to start and stop
intermittently, which will damage the pump with great starting current. To solve these problems, the counter was used to design digital filter
circuit and MCU timer interrupt respectively in the hardware and software. In order to test the performance of the new intelligent liquid level
switcher, the comparison experiments of different depths, pressures and water flows were carried out under the same conditions. The results show
that this method can effectively filter out interfering signal. When both the charging resistance and discharge resistance of the counting pulse
generating circuit are 4.7 kΩ, the filtered signal delayed 2.11 s after the fact output signal. The effective output signal was cleared off
disturbing signal under 2 s by digital filter circuit and kept high stability. The delay time is related to the counting and pulse generating
circuit, so different disturbing signals can be declined by configuring filter plus width in circuit design. On the way of software design, timer
programming method was used in microcontroller considering width of the wave curve to detect the status of ports at certain intervals. The algorithm
was introduced to remove disturbance and improve the efficiency of SCM. The method can be used to filter out the interference signal by setting
different values according to the actual situation, such as different power pumps so the smart switcher can be more feasible. © 2015, Chinese
Society for Agricultural Machinery. All right reserved.
Number of references:21
Main heading:Signal processing
Controlled terms:Buoys - Controllers - Digital filters - Fertilizers - Integrated circuit manufacture - Jitter - Liquids - Reconfigurable hardware -
Signal detection - Signal interference - Software design - Tanks (containers)
Uncontrolled terms:Hardware and software - Integration systems - Intelligent Switch - Liquid level - Liquid level detection - Practical engineering
- Pulse generating circuits - Vegetable productions
Classification code:619.2 Tanks - 703.2 Electric Filters - 714.2 Semiconductor Devices and Integrated Circuits - 716.1 Information Theory and Signal
Processing - 721.3 Computer Circuits - 723.1 Computer Programming - 732.1 Control Equipment - 804 Chemical Products Generally
DOI:10.6041/j.issn.1000-1298.2015.S0.019
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 31>
Accession number:20160301828002
Title:Development of agricultural implement visual navigation terminal based on DSP and MCU
Authors:Xiang, Ming (1); Wei, Shuang (2); He, Jie (1); Qiu, Ruicheng (1); Zhang, Man (1)
Author affiliation:(1) Key Laboratory for Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, China
Corresponding author:Zhang, Man([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:21-26
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Agricultural implement automatic navigation is a major trend of modern agriculture. Agricultural implement navigation can avoid the errors
during implement shaking in field and improve the precision of navigation and flexibility of operation. At present, most of the navigation terminals
were developed based on the industrial computer which is expensive and difficult to be spread. In this paper, an agricultural implement visual
navigation terminal based on DSP and MCU for automatic weeding was designed. As the core processor of the system, DSP was responsible for the image
acquisition, crop rows detection and offset calculation of navigation line. MCU is the main control unit of the system, so it was used to manage the
working process, receive, store and forward GNSS data, and also control the implement. The corresponding protocol of serial communication, network
communication and CAN bus communication in the system was normalized to make sure the stability of the communication. In the image processing, the
OTSU method and the crop row detection algorithm based on boundary detection and scan-filter (BDSF) were adopted, which could improve the accuracy
and efficiency of navigation line detection. In order to verify the validity and stability of the system, the algorithm adaptive test, offset
accuracy test and different system comparison tests were carried out. The experiment results showed that the crop line detection algorithm could
work adaptively in the weed and crop thinning conditions. The average error of offset detection is 1.29 cm and the maximum error is 4.1 cm. The
systems comparison test verified the economic feasibility of the proposed system by compared with the PC and ARM, which can satisfy the requirements
of filed operation. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:17
Main heading:Agricultural implements
Controlled terms:Agricultural machinery - Agriculture - Computer vision - Crops - Errors - Image acquisition - Image processing - Information
management - Microcontrollers - Navigation - Network protocols - Signal detection - System stability - Tools
Uncontrolled terms:Can bus communications - Crop row detection - DSP - Economic feasibilities - Industrial computers - Line detection algorithms -
Network communications - Serial communications
Classification code:716.1 Information Theory and Signal Processing - 723 Computer Software, Data Handling and Applications - 723.5 Computer
Applications - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 961 Systems Science
DOI:10.6041/j.issn.1000-1298.2015.S0.004
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 32>
Accession number:20160301828021
Title:Analysis of influence factors of watermelon vibration response
Authors:Gao, Zongmei (1); Zhang, Wen (1); Ren, Mengjia (1); Wu, Hualin (1); Cui, Di (1)
Author affiliation:(1) College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
Corresponding author:Cui, Di([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:134-140
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:The internal quality of watermelon is closely related to its vibration characteristics. As a representative of the non-contact vibration
method, laser Doppler vibrometry technology can accurately measure the real vibration of agricultural tissue, so as to obtain the information of
internal quality of the agricultural products. In this paper, the influence of vibration parameters on the watermelon vibration frequency response
characteristics was firstly studied based on single factor experiments with the amplitude of acceleration, the frequency sweep rate and testing
point of watermelon. Then, a interaction of multi-factor orthogonal experiment was conducted, which was performed by the laser Doppler vibrometry
system based on the above factors and repeated three times at each parameter combination. The results of the single factor experiment indicated that
the amplitude of acceleration and frequency sweep rate had significant effects on frequency spectrums, but the effect of testing point was not
significant. The results of interaction of multi-factor orthogonal experiment showed that the optimal combination of the amplitude of acceleration,
the frequency sweep rate and testing point for watermelon vibration measurement was 2.5 g, 1000 Hz/min and the sunny side of the equator. The
results laid a foundation for accurate nondestructive detection of internal quality for watermelon. © 2015, Chinese Society for Agricultural
Machinery. All right reserved.
Number of references:19
Main heading:Vibration analysis
Controlled terms:Agricultural products - Agriculture - Classifiers - Doppler effect - Factor analysis - Frequency response - Laser Doppler
velocimeters - Lasers - Nondestructive examination - Quality control - Vibration measurement
Uncontrolled terms:Analysis of influence factors - Doppler vibrometry - Influencing factors analysis - Internal quality - Laser doppler vibrometry
system - Single-factor experiments - Vibration characteristics - Watermelon
Classification code:744.1 Lasers, General - 744.9 Laser Applications - 802.1 Chemical Plants and Equipment - 821 Agricultural Equipment and Methods;
Vegetation and Pest Control - 821.4 Agricultural Products - 913.3 Quality Assurance and Control - 922.2 Mathematical Statistics - 943.1 Mechanical
Instruments - 943.2 Mechanical Variables Measurements
DOI:10.6041/j.issn.1000-1298.2015.S0.023
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 33>
Accession number:20160301828045
Title:Object-oriented detection of land use changes based on high spatial resolution remote sensing image
Authors:Sun, Zhongping (1, 2); Bai, Jinting (3); Shi, Yuanli (2); Liu, Suhong (1, 2); Jiang, Jun (2); Wang, Changzuo (2)
Author affiliation:(1) School of Geography, Beijing Normal University, Beijing, China; (2) Satellite Environment Center, Ministry of Environmental
Protection, Beijing, China; (3) College of Forest, Beijing Forestry University, Beijing, China
Corresponding author:Liu, Suhong([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:297-303
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:The remote sensing images from GF-1 satellite in 2013 and 2014 were used to detect the land use changes of the coastal zone in Haiyan
County, Zhejiang Province. Two detective methods were compared through the pixel-based and object-oriented changes. In the pixel-based land use
change detection, the multi-band difference and ratio methods were used to detect the land use changes based on multi-spectral and fusion images. In
the object-oriented land use change detection, the effect of multi-spectral and fusion images were researched using multi-band difference and ratio
methods on the single-level and multi-level. On this basis, the detection results of human activities region combined with shape features were
analyzed. In addition, the change vector analysis (CVA) was adopted to conduct land use change detection basing on the multi-spectral and fusion
images. The results showed that the overall accuracy of object-oriented land use change detection was 86.29%, and Kappa coefficient was 0.72, which
were better than those of the pixel-based land use change detection. In the object-oriented land use change detection, the detection results of
multi-level multi-band difference and ratio methods which using fusion image were the best, and they were better than those of CVA. And in the
pixel-based land use change detection, the results of multi-band difference method which used fusion image were better than those of the other
methods. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:14
Main heading:Land use
Controlled terms:Image fusion - Image reconstruction - Object detection - Pixels - Remote sensing - Signal detection
Uncontrolled terms:Change detection - Change vector analysis - Difference method - High spatial resolution - Multilevels - Object oriented - Overall
accuracies - Remote sensing images
Classification code:403 Urban and Regional Planning and Development - 716.1 Information Theory and Signal Processing - 723.2 Data Processing and
Image Processing
DOI:10.6041/j.issn.1000-1298.2015.S0.047
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 34>
Accession number:20160301828005
Title:Simulation research for individual young apple tree pruning
Authors:Yang, Lili (1); Chen, Jiafeng (1); Xie, Rui (1); Hua, Jing (2); Kang, Mengzhen (2); Dong, Qiaoxue (1)
Author affiliation:(1) College of Information and Electric Engineering, China Agricultural University, Beijing, China; (2) Institute of Automation,
Chinese Academy of Sciences, Beijing, China
Corresponding author:Dong, Qiaoxue([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:41-44
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Pruning is one of the important measures for fruit trees management. Different pruning methods have different effects on the growth of
fruit trees. Research on the law of apple tree growth after pruning is of great significance to guide production. Apple tree, as a perennial, is
quite complex in observation, modeling and calibration, which makes researches related quite a few. In order to study more reasonable pruning
technology, judgment of pruning reaction needs to ensure the accuracy, intuition and timeliness and the simulation technology makes it possible. In
this thesis, apple tree was taken as the research object, the data which was measured from the field experiment was analyzed, the law of pruning
reaction was summarized and the structure model and pruning model of apple tree were built. The simulation software of apple tree pruning was
established based on the Qt framework and OpenGL graphics library. For a given production target, the simulation software of apple tree pruning
provides an optimized solution of pruning through the optimal algorithm. © 2015, Chinese Society for Agricultural Machinery. All right
reserved.
Number of references:6
Main heading:Trees (mathematics)
Controlled terms:Application programming interfaces (API) - Computer graphics - Computer software - Forestry - Fruits - Orchards
Uncontrolled terms:Apple trees - Optimized solutions - Pruning - Simulation - Simulation research - Simulation software - Simulation technologies -
Structure modeling
Classification code:723 Computer Software, Data Handling and Applications - 723.5 Computer Applications - 821.3 Agricultural Methods - 821.4
Agricultural Products - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory
DOI:10.6041/j.issn.1000-1298.2015.S0.007
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 35>
Accession number:20160301828024
Title:Time and space event model for complex event processing in internet of things in farmland
Authors:Li, Xiang (1); Wang, Jianlun (1); Gao, Hongju (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China
Corresponding author:Gao, Hongju([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:153-161
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In internet of things in farmlands, control commands are usually triggered by complex events which contain many dimensionalities of
information. Complex events are detected by combining mass of atomic events directly sensed by sensors. That is a typical procedure of complex event
processing (CEP). As a basis of CEP, a model of complex events, which describes how atomic events construct complex events, is necessary. Current
complex event models focus on temporal logic, but spatial logic relations among farmland events are not considered. In this paper, a novel model
that considers both temporal logic and spatial logic was proposed and a relevant XML-based specification language was designed. In the model,
multi-level complex events were modeled to a directed graph. For one complex event in the graph, based on analysis of temporal and spatial logic
relations among component events, nine time relation operators and eight spatial relation operators were defined to describe above relations; five
time combination operators and seven spatial combination operators were defined to calculate attributes of complex events from attributes of atomic
events. Based on the model, a XML-based language was defined, which balances readability of users, descriptive abilities and difficulties of parsing
and compiling. The proposed event model and language were compared to those used in popular CEP systems e.g. SASE+, Esper, etc. From the comparison,
the proposed model and language were more suitable for describing complex events in internet of things in farmlands. © 2015, Chinese Society
for Agricultural Machinery. All right reserved.
Number of references:17
Main heading:Farms
Controlled terms:Atoms - Computational linguistics - Computer circuits - Directed graphs - Internet - Internet of things - Reconfigurable hardware -
Specification languages - Specifications - Temporal logic - XML
Uncontrolled terms:Combination operators - Complex event processing - Complex event processing (CEP) - Event model - Spatial combinations - Spatial
relations - Temporal and spatial - XML-based languages
Classification code:721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory - 721.3 Computer Circuits -
723 Computer Software, Data Handling and Applications - 723.1.1 Computer Programming Languages - 821 Agricultural Equipment and Methods; Vegetation
and Pest Control - 902.2 Codes and Standards - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 931.3 Atomic and Molecular
Physics
DOI:10.6041/j.issn.1000-1298.2015.S0.026
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 36>
Accession number:20160301828039
Title:Extraction method of crop planted area based on GF-1 WFV image
Authors:Huang, Jianxi (1); Jia, Shiling (1); Wu, Hongfeng (2); Su, Wei (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China; (2) The Institute of
Scientific and Technical Information, Heilongjiang Academy of Land Reclamation Region, Harbin, China
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:253-259
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Obtaining planted area of crop has important significance for guaranteeing nation grain safety. The Farm NO. 597, located in Baoqing
County, Shuangyashan City, Heilongjiang Province was selected as an example to extract rice and maize planted area by taking WFV (Wide field view)
sensor carried on GF-1 satellite with the spatial resolution of 16 m as data source, using the image produced on October 30, 2014, and calculating
different characteristic bands. Firstly, the multi-characteristic data set was established based on the NDVI (Normalized difference vegetation
index) calculated from the source image and the first three principal components analyzed by PCA (Principal component transform). Then, using the
difference between different surface features in each characteristic band, the decision tree was built based on CART (Classification and regression
trees) to classify rice and maize. The results showed that the overall classification accuracy was 96.15% and the Kappa coefficient was 0.94.
Producer accuracy of rice was 98.41% and user accuracy was 97.64%. Producer accuracy of maize was 95.38% and user accuracy was 97.89%. This method
provides the reference value for crop type mapping using GF-1 data in other agricultural areas. © 2015, Chinese Society for Agricultural
Machinery. All right reserved.
Number of references:18
Main heading:Image processing
Controlled terms:Crops - Data mining - Decision trees - Extraction - Principal component analysis
Uncontrolled terms:Classification accuracy - Classification and regression tree - Extraction method - GF-1 - Multi characteristics - Normalized
difference vegetation index - Planted areas - Satellite images
Classification code:723.2 Data Processing and Image Processing - 802.3 Chemical Operations - 821.4 Agricultural Products - 922.2 Mathematical
Statistics - 961 Systems Science
DOI:10.6041/j.issn.1000-1298.2015.S0.041
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 37>
Accession number:20160301828044
Title:Statistical optimization method of massive spatio-temporal data for long time series land use
Authors:Gao, Yunbing (1, 2); Zhang, Yipeng (2, 3); Gao, Bingbo (2, 4); Pan, Yuchun (2, 5); Zhang, Xiaodong (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China; (2) Beijing Research Center
for Information Technology in Agriculture, Beijing, China; (3) National Engineering Research Center for Information Technology in Agriculture,
Beijing, China; (4) Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing, China; (5) Beijing Engineering Research Center of
Agricultural Internet of Things, Beijing, China
Corresponding author:Zhang, Xiaodong([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:290-296
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Statistics analyses of spatio-temporal land use data, such as historical review, flow analysis, change index analysis and trend analysis,
are important land management operations, and attract more and more attention from management and planning department. To overcome the difficulty in
statistics of annual land use type in any query region and the time consuming problem in long time series change flow analysis, the statistical
optimization method based on spatio-temporal variation model was proposed. For the former difficulty, the feature entities in the statistical region
and boundary were classified with the proposed method based on the principles of connectivity of graphics, and then the statistical optimization
algorithm of sequential snapshots was used to realize the statistics of time point status in any query area. For the latter problem, the spatio-
temporal network approximation judging was carried out with the method based on multi-commodity flow principle, to reduce time consuming and improve
the efficiency of long time series change flow analysis through reducing the number of spatial overlay analysis. Finally, the effectiveness and
feasibility of the proposed method were verified through case study using land use data of Qionghai City, Hainan Province from 2009 to 2012. ©
2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:12
Main heading:Time series analysis
Controlled terms:Algorithms - Forestry - Graph theory - Land use - Optimization - Statistics - Time series
Uncontrolled terms:Graph theory models - Land-use change - Multi-commodity flow - Spatio-temporal data - Spatio-temporal variation - Statistical
optimization - Statistical optimization algorithms - Statistics analysis
Classification code:403 Urban and Regional Planning and Development - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 921.5
Optimization Techniques - 922.2 Mathematical Statistics
DOI:10.6041/j.issn.1000-1298.2015.S0.046
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 38>
Accession number:20160301828046
Title:Spatial variability of soil temperature and moisture in northeast china
Authors:An, Xiaofei (1, 2); Meng, Zhijun (1, 2); Wang, Pei (1, 2); Fu, Weiqiang (1, 2); Guo, Jianhua (1, 2)
Author affiliation:(1) Beijing Research Center of Intelligent Equipment Agriculture, Beijing, China; (2) National Research Center of Intelligent
Equipment for Agriculture, Beijing, China
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:304-308
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to achieve the maize sowing time decision-making and improve the effective accumulated temperature of maize growth, it is needed
to understand the soil spatial variability characteristics. Totally 20 sets of wireless sensor network nodes were deployed in Zhaoguang Farm in
Heilongjiang Province for one month in 2015. In addition, two handheld mobile sensor nodes were chosen to increase the measurement number. According
to the method, soil temperature and moisture data were obtained from 5th to 29th in April with 240 m×240 m, 120 m×120 m, 60 m×60 m
and 30 m×30 m grids. The isotropic and anisotropic variation characteristics and distribution patterns of soil temperature and moisture were
analyzed based on statistics semivariance function theory and GIS space Kriging interpolation method. Experimental results showed that the
semivariance of soil temperature and moisture were suitable for the spherical model and exponential model, respectively. Both of them had strong
spatial autocorrelation. The distribution of soil temperature was block with autocorrelation distance of 51.56 m. And the distribution of soil
moisture was ribbon with autocorrelation distance of 154.16 m. The anisotropy of soil temperature and moisture variation was also significant. The
soil temperature variations in 45° and 90° directions were significantly greater than those in 0° and 135° directions. The soil
moisture determination coefficient (R<sup>2</sup>) was 0.77 with significant variations in 0° and 135° directions. The research results
provided a scientific guidance for the decision-making of maize seeding time and the determination of soil sampling distance. © 2015, Chinese
Society for Agricultural Machinery. All right reserved.
Number of references:15
Main heading:Soils
Controlled terms:Anisotropy - Autocorrelation - Decision making - Interpolation - Moisture - Moisture determination - Sensor nodes - Soil moisture -
Soil surveys - Temperature - Wireless sensor networks
Uncontrolled terms:Accumulated temperatures - Auto-correlation distance - Geo-statistics - Kriging interpolation methods - Soil temperature - Soil
temperature variations - Spatial and temporal variation - Variation characteristics
Classification code:483.1 Soils and Soil Mechanics - 641.1 Thermodynamics - 722 Computer Systems and Equipment - 722.3 Data Communication, Equipment
and Techniques - 912.2 Management - 921 Mathematics - 921.6 Numerical Methods - 931.2 Physical Properties of Gases, Liquids and Solids - 944.2
Moisture Measurements
DOI:10.6041/j.issn.1000-1298.2015.S0.048
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 39>
Accession number:20160301828029
Title:Target tracking and behavior detection method in piggery scenarios
Authors:Duan, Yuyao (1, 2); Ma, Li (1, 3); Liu, Gang (1, 2)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, China; (3)
College of Information Science and Technology, Agricultural University of Hebei, Baoding, China
Corresponding author:Liu, Gang([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:187-193
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to deal with various background situations, the complex lighting situations and the goals-background-mixing situations in piggery,
a new kind of tracking method which is based on traditional compressive tracking algorithm was proposed. Firstly, to reduce the tracking error, we
changed the search window to oval which is closer to the pig body. Secondly, to increase the stability of feature extraction and reduce drift, we
combined gray feature with texture feature, and improved the random measurement matrix of traditional compressive tracking algorithm. Lastly, the
piggery was divided in different areas. Based on the location of the target pigs we can analyze and assess its current behavior. Test results of
different video samples and tracking results show that this algorithm improves the accuracy significantly in the piggery scene. The mean value and
the variance of central point error in the proposed method were 25.44, those were 60.32%, 33.33%, 32.57% of the mean value of central point error in
the CT method, TUT method and Camshift method. The tracking rate and it reaches to 19.3 frame/s. © 2015, Chinese Society for Agricultural
Machinery. All right reserved.
Number of references:17
Main heading:Target tracking
Controlled terms:Algorithms - Clutter (information theory) - Errors - Feature extraction - Tracking (position)
Uncontrolled terms:Behavior detection - Current behaviors - Piggery scenarios - Random measurement - Texture features - Tracking algorithm -
Tracking errors - Tracking method
Classification code:716.1 Information Theory and Signal Processing
DOI:10.6041/j.issn.1000-1298.2015.S0.031
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 40>
Accession number:20160301828012
Title:Design and experiment on silage pH value wireless monitor device
Authors:Meng, Fanjia (1); Ross, Fabian (2)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China; (2) Department of
Agricultural Engineering, University of Bonn, Bonn, Germany
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:79-83
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:pH value is one of the important parameters for evaluating the silage quality. A wireless pH value monitor device for silage was developed,
and it could obtain the pH value of silage at real-time during fermentation. After the calibration of the device, the verification experiments of
the actual measurement results were carried out in chopped maize and grass samples. The results showed that there is the linear correlation between
the measurement result and the accurate pH value, r is 0.9973 and 0.9957 (chopped maize, grass sample, pespectirely). The experiments were carried
out for monitoring the change of pH value during fermentation in chopped maize and grass silage respectively. The results showed that the original
pH value in chopped maize is 5.4, and then the pH value decreased sharply from 5.4 to 4.0 between 0.5~2 d. After that, the pH value decreased slowly
and became stable at 3.8 finally; the original pH value in grass is 5.9, and the pH value decreased from 6.0 to 5.2 between 0.8~3 d. Finally, the pH
value became stable at 5.1 in 7 d fermentation. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:12
Main heading:pH
Controlled terms:Fermentation
Uncontrolled terms:Actual measurements - Grass sample - Grass silages - Linear correlation - pH value - Real time - Silage
Classification code:801.1 Chemistry, General
DOI:10.6041/j.issn.1000-1298.2015.S0.014
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 41>
Accession number:20160301828016
Title:Rapid pretreament method for soil nitrate nitrogen detection based on ion-selective electrode
Authors:Kong, Pan (1); Zhang, Miao (1, 2); Ren, Haiyan (2); Li, Yanhua (1); Pu, Pan (1)
Author affiliation:(1) Key Laboratory on Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Key Laboratory of Agricultural Information Acquisition Technology, Minstry of Agriculture, China Agricultural
University, Beijing, China
Corresponding author:Zhang, Miao([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:102-107
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:The aim of the research was to improve the efficiency of soil pretreatment for ion-selective electrode (ISE) based soil nitrate-nitrogen
detection. Modern experimental apparatuses for soil sample pretreatment, such as microwave, high-speed centrifuge and high-speed vortex oscillator,
were validated to replace the traditional pretreatment tools. The single factor test was conducted. Four influencing factors, including microwave
time, shaking time, centrifugation rate and time, were selected and optimized. The analysis of variance was applied to analyze the results of
orthogonal experiments. The optimized parameters of ISE based soil rapid pretreatment process were obtained as microwave drying time of 9 min,
high-speed vortex time of 40 s, centrifugal speed of 1000 r/min and centrifugal time of 60 s. With the optimized parameters, the validation
experiment of 59 soil samples indicated that the mean relative error of the rapid pretreatment and root mean square error were 7.48% and 7.91 mg/L,
respectively. The rapid pretreatment time was less than 15 min for a sample. © 2015, Chinese Society for Agricultural Machinery. All right
reserved.
Number of references:15
Main heading:Ion selective electrodes
Controlled terms:Centrifugation - Drying - Electrodes - Ions - Mean square error - Microwave oscillators - Nitrates - Nitrogen - Soil surveys -
Soils - Vortex flow
Uncontrolled terms:Experimental apparatus - Nitrate nitrogen - Optimized parameter - Orthogonal experiment - Pretreatment process - Root mean square
errors - Soil pretreatment - Variance analysis
Classification code:483.1 Soils and Soil Mechanics - 631.1 Fluid Flow, General - 713.2 Oscillators - 802.1 Chemical Plants and Equipment - 802.3
Chemical Operations - 804 Chemical Products Generally - 804.2 Inorganic Compounds - 922.2 Mathematical Statistics
DOI:10.6041/j.issn.1000-1298.2015.S0.018
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 42>
Accession number:20160301828018
Title:Design and experiment of equant-diameter roller screening machine for fresh tea leaves
Authors:Hu, Yongguang (1); Li, Jian'gang (1); Lu, Haiyan (2); Xiao, Hongru (3)
Author affiliation:(1) School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang, China; (2) Chinese Academy of Agricultural
Mechanization Sciences, Beijing, China; (3) Nanjing Research Institute for Agricultural Mechanization, Ministry of Agriculture, Nanjing, China
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:116-121
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to improve the screening effect of fresh tea leaves, an equant-diameter roller screening machine was developed with adjustable
structural and technical parameters. The main parameters of roller rotating speed, roller diameter and roller length were calculated, and the model
selection of driving motor was done. The optimal parameters combination of roller inclination, rotating speed and feeding rate was made based on the
orthogonal test. The treatments were arranged according to orthogonal table L<inf>25</inf>(5<sup>6</sup>), and the influence of each parameter on
screening rate was analyzed. The results showed that the sequence of each parameter's influence on total screening rate was roller inclination,
feeding rate and rotating speed, and screening rate reached the maximum when roller inclination was 6°, feeding rate was 1.5 kg/min and rotation
rate was 16 r/min. To double the screening productivity, feeding rate should be selected as 3.0 kg/min, while correspondingly total screening rate
was dropped by only 3.3% averagely. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:8
Main heading:Rollers (machine components)
Controlled terms:Feeding - Rotating machinery
Uncontrolled terms:Design experiments - Equant-diameter roller - Main parameters - Optimal parameter - Orthogonal table - Screening effect -
Screening machines - Tea leaves
Classification code:601.1 Mechanical Devices - 601.2 Machine Components - 691.2 Materials Handling Methods
DOI:10.6041/j.issn.1000-1298.2015.S0.020
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 43>
Accession number:20160301828009
Title:Numerical solution of kinematics model for leveling system of paddy field leveler based on Matlab
Authors:Zhao, Zuoxi (1, 2); Shi, Lei (1); Liu, Xiong (1); Ke, Xinrong (1); Zhao, Ouya (1); Jin, Jundong (1)
Author affiliation:(1) Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural
University, Guangzhou, China; (2) Southern Regional Collaborative Innovation Center for Grain and Oil Crops in China, Changsha, China
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:63-68
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Kinematics model of mechanical hydraulic system usually involves the speed, acceleration and geometric constraint, which will contain one
or two and even higher order differential equations. The number of differential-algebraic equations (DAEs) increases with the increase of system
complexity. In order to find a suitable control arithmetic, it is needed to figure out the relationship of different state variables. However, it's
always impossible to get analytical solution. Thus it is needed to find the numerical solution of DAEs system, especially when it has too many state
variables. Solving this problem with computer software is a common way; there are several helpful softwares, such as Matlab, Maple, Simulink and
Mathematica. The mathematical function provided by the Matlab ode45 was used to solve the kinematics model of leveling system of paddy field leveler
with sinusoidal input current. Firstly, the real paddy field leveler was simplified to kinematics model and showed in DAEs form based on theoretical
mechanics and hydraulic theory. Secondly, the DAEs were changed into ODEs (ordinary differential equations). Finally, the ode45 was used to get
numerical solution and show the relationship of state variables. The input current and output state variables were showed in figure. The method can
help to get accurate numerical solution for DAEs system, and it also can display the relationship between the random input with known equations and
other state variables. It will help to forecast the state variables at the next moment and design an efficient control algorithm for paddy field
leveler. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:16
Main heading:Numerical models
Controlled terms:Algebra - Algorithms - Differential equations - Equations of state - Functions - Hydraulic equipment - Kinematics - Leveling
(machinery) - MATLAB - Numerical methods - Ordinary differential equations
Uncontrolled terms:Different state variables - Differential algebraic - Differential algebraic equations - Higher-order differential equation -
Kinematics modeling - Mathematical functions - Numerical solution - Paddy fields
Classification code:603.1 Machine Tools, General - 632.2 Hydraulic Equipment and Machinery - 921 Mathematics - 931.1 Mechanics
DOI:10.6041/j.issn.1000-1298.2015.S0.011
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 44>
Accession number:20160301828038
Title:Remote-sensing classification method of county-level agricultural crops using time-series NDVI
Authors:Zhang, Rongqun (1); Wang, Sheng'an (1); Gao, Wanlin (1); Sun, Weijian (1); Wang, Jianlun (1); Niu, Ling'an (2)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China; (2) College of Resources
and Environment, China Agricultural University, Beijing, China
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:246-252
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Getting all kinds of crop planting area information accurately is the Agricultural Information Management Department's main responsibility
in order to master the basis of crop production information in an efficient manner. A remote sensing classification method was used based on time-
series NDVI that is gathered by using Landsat8 satellite equipped with remote sensing technology. This remote sensing technology possessed a short
cycle, performed its analysis in a very speedy manner and used a strong microscope to closely analyze the area it has been assigned to. Based on the
analysis of the time-series spectrum character curve, crop type identification and acreage extraction can be effectively achieved. This helped to
overcome the confusing agricultural crops classification problem caused by “same object with different spectra” and “foreign body
with spectrum” by using a single temporary remote sensing image. In order to accurately ascertain the planting area for the various kinds of
crops for providing technical support, the best NDVI threshold range for the crops was studied and the various crop classification rules were
explored. The Quzhou County, Hebei Province was taken as the study area, and a distribution map of the study area was made based on this information
which was gathered in 2014.Throughout five time phases of Landsat satellite data gathered in 2014, a study on the classification of remote sensing
for planting area of winter wheat, summer maize, spring corn, cotton, and millet in the study area was conducted. Classification results can be
shown for 2014 with all kinds of crops in the study area, respectively: winter wheat is 27776.61 hm<sup>2</sup>, summer corn is 27776.61
hm<sup>2</sup>, spring corn is 2582.73 hm<sup>2</sup>, cotton is 6485.94 hm<sup>2</sup>, and millet is 277.65 hm<sup>2</sup>. Using the Kappa
coefficient and statistical data to verify the accuracy of this classification, the result shows that the winter wheat, summer corn, spring corn,
cotton and millet can be effectively identified, with an overall classification accuracy of 89.1667%, along with a Kappa coefficient of 0.857
4.Compared with the statistical data, the relative margin of error for individual crops is as follows: winter wheat-0.80%, summer corn-0.32%, spring
corn-3.15%, cotton-2.71%, millet 4.12%. This paper proves that mass crop planting areas can be precisely obtained from analyzing the time-series
data of remote sensing images with a medium spatial resolution. It also proves that this method can provide a technical basis for using remote
sensing to investigate crop planting areas at a county level. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:23
Main heading:Remote sensing
Controlled terms:Agriculture - Classification (of information) - Cotton - Crops - Cultivation - Decision trees - Image reconstruction - Information
management - Statistics - Time series - Time series analysis
Uncontrolled terms:Agricultural informations - Classification accuracy - Classification results - Crop classification - NDVI - Remote sensing
classification - Remote sensing images - Remote sensing technology
Classification code:716.1 Information Theory and Signal Processing - 821 Agricultural Equipment and Methods; Vegetation and Pest Control - 922.2
Mathematical Statistics - 961 Systems Science
DOI:10.6041/j.issn.1000-1298.2015.S0.040
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 45>
Accession number:20160301828020
Title:Rapid classification method of walnut kernel varieties based on near-infrared diffuse reflectance spectra
Authors:Ma, Wenqiang (1, 2); Zhang, Man (1); Li, Zhongxin (2)
Author affiliation:(1) Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural
University, Beijing, China; (2) Agricultural Mechanization Institute, Xinjiang Academy of Agricultural Sciences, Urumqi, China
Corresponding author:Zhang, Man([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:128-133
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:Walnut is an important dry fruit and woody oil crop in China, and it has significant meaning to establish a rapid, nondestructive testing
method for identification and classification of walnut kernel varieties in walnut processing industry. Near-infrared diffuse reflection
spectroscopies of 200 walnut samples of four species were adopted to establish models for rapid and nondestructive classification. The spectral
region of walnut samples was ranged from 3800 cm<sup>-1</sup>to 9600 cm<sup>-1</sup>. The spectra data of walnut were processed using the
multiplicative scatter correction (MSC) and the standard normalized variate (SNV) methods. Principal component analysis (PCA) was used to reduce the
dimensionality of spectra data. The cumulative contribution rate of the first five main components reached 99.21%, which were selected as variables
for modeling. Totally 100 walnut samples were selected as training set by random sampling method. The NIR classification model of walnut kernel
varieties was built based on support vector machine (SVM) method, and grid search method was used for searching the best parameter. The built model
was tested by the rest 100 walnut samples of four species, and the results showed that the correct recognition rate of the model reached 96%. The
analyzed results indicated that the NIR classification model could provide a feasible method for rapid and nondestructive identification of walnut
kernel varieties. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:22
Main heading:Infrared devices
Controlled terms:Near infrared spectroscopy - Nondestructive examination - Principal component analysis - Support vector machines - Testing
Uncontrolled terms:Diffuse reflection spectroscopy - Multiplicative scatter correction - Near infrared diffuse reflectance - Near infrared spectra -
Nondestructive identification - Nondestructive testing method - Random sampling method - Walnut kernel
Classification code:723 Computer Software, Data Handling and Applications - 922.2 Mathematical Statistics
DOI:10.6041/j.issn.1000-1298.2015.S0.022
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 46>
Accession number:20160301828041
Title:Research of remote sensing evaluation model library platform of ecological environment
Authors:Zhang, Yonghan (1); Sun, Ruizhi (1); Li, Lin (1); Li, Qian (1); Xu, Yunfei (2); Dai, Yizhou (1)
Author affiliation:(1) Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, China; (2) State Key
Laboratory of Remote Sensing Science, Institute of Remote Sensing & Digital Earth, Chinese Academy of Sciences, Beijing, China
Corresponding author:Sun, Ruizhi([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:267-273
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In the process of constructing an ecological environment evaluation system of remote sensing, due to the business-core development mode,
there is high degree of coupling between a variety of business models and the system, and the model reused could be a difficult problem. The
development platform restriction of the API library itself leads to the lack of invoking ability for multi-platform. At the mean time, under the
background of remote sensing of big data calculation, it is more difficult for the system to cope with the problem that multi-user concurrent
requests, long time delay which is caused by wide area coverage calculation and the high system resources occupied. In the view of above problems,
the paper puts forward an ecological environment evaluation model library that based on SOA and OpenStack. In terms of model reused and multi-
platform invoking problem, 20 kinds of commonly used remote sensing thematic evaluation algorithm models were unified packaging, deployed and
concurrent tuned as Web services. To solve the problem of model concurrent processing and large data computing, it utilized OpenStack to solve
dynamic load balancing and task allocation for multiple services. On the other side, the paper analyzed the practical problems of core metadata
interface design and encapsulates during the process of building a model library, and then provided new design idea. At the end, it developed
ecological environment production evaluation that based on Three-river Head Source of Qinghai Province as example, which proved a stable system
operation results. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:16
Main heading:Remote sensing
Controlled terms:Big data - Data handling - Distributed computer systems - Ecology - Metadata - Network management - Problem solving - Time delay -
Web services - Websites
Uncontrolled terms:Concurrency - Concurrent processing - Ecological environment evaluations - Ecological environments - Massive data - Model library
- Production evaluation - Remote sensing evaluation
Classification code:454.3 Ecology and Ecosystems - 713 Electronic Circuits - 722.4 Digital Computers and Systems - 723.2 Data Processing and Image
Processing
DOI:10.6041/j.issn.1000-1298.2015.S0.043
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 47>
Accession number:20160301828027
Title:Classification technique of chinese agricultural text information based on SVM
Authors:Wei, Fangfang (1); Duan, Qingling (1, 2); Xiao, Xiaoyan (1); Zhang, Lei (1)
Author affiliation:(1) College of Information and Electrical Engineering, China Agricultural University, Beijing, China; (2) Beijing Engineering
Research Center of Agricultural Internet of Things, Beijing, China
Corresponding author:Duan, Qingling([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:174-179
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to provide personalized services for agricultural information recommendation, it was needed to organize and classify information
efficiently. According to the characteristics of agricultural texts, a Chinese agricultural text classification model was proposed based on linear
support vector machine (SVM). Firstly, an agriculture-domain-based dictionary was built. Secondly, a feature vector was extracted and the weight for
each feature in a vector was selected. Lastly, a text classification model was established. The model was tested on 1071 documents which were
belonged to four classes: planting, forestry, animal husbandry and fisheries. The results showed that the accuracy was 96.5% and the recall rate was
96.4%. Both of their performances were higher than those of the ones using other classification methods, such as the Bayesian, decision tree, KNN,
SMO algorithm and neural network. The model was applied to the platform for agricultural internet of things (IOT) industry integrated information
service. The performance showed that the method can automatically classify Chinese agricultural text information and the response time met the
system requirements. © 2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:11
Main heading:Classification (of information)
Controlled terms:Agricultural machinery - Agriculture - Algorithms - Data mining - Decision trees - Image retrieval - Information services - Support
vector machines - Text processing - Vectors
Uncontrolled terms:Agricultural informations - Classification technique - Information integration - Integrated information services - Internet of
Things (IOT) - Linear Support Vector Machines - Text classification - Text classification models
Classification code:723 Computer Software, Data Handling and Applications - 723.2 Data Processing and Image Processing - 821 Agricultural Equipment
and Methods; Vegetation and Pest Control - 821.1 Agricultural Machinery and Equipment - 903 Information Science - 921.1 Algebra - 961 Systems
Science
DOI:10.6041/j.issn.1000-1298.2015.S0.029
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
<RECORD 48>
Accession number:20160301828010
Title:Design of on-line monitoring and fault early warning system for peanut combined harvester
Authors:Wang, Fengzhu (1); Zhang, Junning (1); Li, Ruichuan (2); Wei, Liguo (1); Han, Xiang (3); Liu, Yangchun (1)
Author affiliation:(1) Chinese Academy of Agricultural Mechanization Sciences, Beijing, China; (2) Shandong Wuzheng Group Co., Ltd., Rizhao, China;
(3) Rural Development Center of Beijing Municipal Commission of Science and Technology, Beijing, China
Corresponding author:Zhang, Junning([email protected])
Source title:Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery
Abbreviated source title:Nongye Jixie Xuebao
Volume:46
Issue date:December 30, 2015
Publication year:2015
Pages:69-73
Language:Chinese
ISSN:10001298
CODEN:NUYCA3
Document type:Journal article (JA)
Publisher:Chinese Society of Agricultural Machinery
Abstract:In order to improve the automatic operation degree in traditional peanut combined harvester, the online conditions measurement method for
key working parts, such as picking roller, sorting screen, clamping shaft, clamping shaft and rotating components, was designed on the basis of
4HBLZ-2 one-row small self-propelled peanut combined harvester. Using LabView, an on-line operation monitoring system of peanut combined harvester
was developed, with which the machine control state, harvesting operation mode, engine parameter, harvesting trajectory and working conditions of
main parts could be real-timely monitored. The self-diagnosis and fault early warning model was established with the application of multi-sensor
information fusion algorithm, which could offer alarm message for drivers under abnormal conditions, such as blocking of picking roller and chain
break. Tests result showed that the time of automatic fault diagnosis was less than 2 min and the accuracy of fault detection was up to 90%. The
designed system had met the functional requirements and precision needs of peanut combined harvester real-time monitoring in field operation. ©
2015, Chinese Society for Agricultural Machinery. All right reserved.
Number of references:14
Main heading:Fault detection
Controlled terms:Electric fault currents - Failure analysis - Harvesters - Oilseeds
Uncontrolled terms:Automatic fault diagnosis - Early warning - Fault early warnings - Functional requirement - Harvesting operations - Multi-sensor
information fusion - Operation monitoring - Peanut
Classification code:701.1 Electricity: Basic Concepts and Phenomena - 821.1 Agricultural Machinery and Equipment - 821.4 Agricultural Products
DOI:10.6041/j.issn.1000-1298.2015.S0.012
Database:Compendex
Compilation and indexing terms, Copyright 2016 Elsevier Inc.Compendex references:YES
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