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基于激光雷達(dá)的樹(shù)形靶標(biāo)冠層葉面積探測(cè)模型研究
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陜西省重大科技攻關(guān)項(xiàng)目(2020zdzx03-04-01)、國(guó)家自然科學(xué)基金項(xiàng)目(31971775)、重慶市技術(shù)創(chuàng)新與應(yīng)用發(fā)展專(zhuān)項(xiàng)(cstc2019jscx-gksbX0089)和國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2019YFE0125200)


Detection Model of Tree Canopy Leaf Area Based on LiDAR Technology
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    摘要:

    果園靶標(biāo)冠層葉面積有效探測(cè)是施藥量在線計(jì)算的基本依據(jù),。針對(duì)樹(shù)形靶標(biāo)稠密和稀疏2種冠層類(lèi)型,,搭建葉面積測(cè)量三維立體試驗(yàn)平臺(tái)和激光雷達(dá)(Light detection and ranging,LiDAR)探測(cè)移動(dòng)試驗(yàn)平臺(tái),構(gòu)建不同厚度和稠密度樹(shù)形靶標(biāo),,采用偏最小二乘回歸(Partial least squares regression,PLSR)算法與BP(Back propagation)神經(jīng)網(wǎng)絡(luò)算法建立了冠層葉面積探測(cè)模型,。試驗(yàn)結(jié)果表明:PLSR算法獲得稠密厚冠層、稀疏厚冠層,、稠密薄冠層和稀疏薄冠層葉面積探測(cè)模型的決定系數(shù)(R2)分別為:0.9626,、0.4130、0.8896,、0.2699,;BP神經(jīng)網(wǎng)絡(luò)算法獲得模型的R2依次為:0.9727、0.5302,、0.8993,、0.4290?;贚iDAR的冠層葉面積探測(cè)模型對(duì)稠密冠層探測(cè)精度較高,,R2不低于0.8896,對(duì)稀疏冠層探測(cè)精度較低,,不高于0.5302,,該探測(cè)方法可用于稠密冠層葉面積在線計(jì)算,指導(dǎo)果園精準(zhǔn)變量噴藥,。

    Abstract:

    The effective detection of leaf area of target canopy in the orchard is the basic for the online calculation of the pesticide application rate. A three-dimensional test platform for leaf area measurement and a light detection and ranging (LiDAR) detection mobile test platform were built. Tree targets of different thickness and density were constructed for the two canopy types of dense and sparse tree targets. Partial least squares regression (PLSR) algorithm and back propagation (BP) neural network algorithm were used for canopy leaf area detection model among the number of LiDAR point clouds data, canopy thickness and canopy leaf area. The experimental results showed that the coefficients of determination (R2) of the equations of dense thick canopy, sparse thick canopy, dense thin canopy and sparse thin canopy obtained by PLSR algorithm were 0.9626, 0.4130, 0.8896 and 0.2699, and the R2 obtained by BP neural network of the canopies were 0.9727, 0.5302, 0.8993 and 0.4290, respectively. Based on the LiDAR canopy leaf area detection model, the detection accuracy of the dense canopy was high, the value of R2 was not less than 0.8896, and the detection accuracy of the sparse canopy was relatively poor, which was not higher than 0.5302. Comparing the PLSR algorithm and the BP neural network algorithm, the latter can significantly improve the accuracy of the model, and the R2 value can be increased by 0.1591. The proposed three-dimensional space tree target canopy leaf area detection method can be used to calculate dense canopy leaf area online to guide orchard accurate variable spraying.

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谷趁趁,翟長(zhǎng)遠(yuǎn),陳立平,李琪,胡麗娜,楊福增.基于激光雷達(dá)的樹(shù)形靶標(biāo)冠層葉面積探測(cè)模型研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2021,52(11):278-286. GU Chenchen, ZHAI Changyuan, CHEN Liping, LI Qi, HU Li’na, YANG Fuzeng. Detection Model of Tree Canopy Leaf Area Based on LiDAR Technology[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(11):278-286.

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  • 收稿日期:2021-02-18
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  • 在線發(fā)布日期: 2021-11-10
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