ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

植物表型平臺(tái)與圖像分析技術(shù)研究進(jìn)展與展望
CSTR:
作者:
作者單位:

作者簡(jiǎn)介:

通訊作者:

中圖分類號(hào):

基金項(xiàng)目:

國(guó)家自然科學(xué)基金項(xiàng)目(31371963),、江蘇省六大人才高峰項(xiàng)目(NY-058),、江蘇省青藍(lán)工程項(xiàng)目(蘇教201842),、江蘇省333工程項(xiàng)目(蘇人20186)、福建省林木種苗科技攻關(guān)六期項(xiàng)目(20192021)和江蘇高校優(yōu)勢(shì)學(xué)科建設(shè)工程項(xiàng)目


Research Progress and Prospect in Plant Phenotyping Platform and Image Analysis Technology
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 圖/表
  • |
  • 訪問(wèn)統(tǒng)計(jì)
  • |
  • 參考文獻(xiàn)
  • |
  • 相似文獻(xiàn)
  • |
  • 引證文獻(xiàn)
  • |
  • 資源附件
  • |
  • 文章評(píng)論
    摘要:

    近年來(lái),,植物基因組得到迅猛發(fā)展,,但因缺乏足夠的表型數(shù)據(jù)而限制了人類解析數(shù)量性狀遺傳學(xué)的能力,。通過(guò)開(kāi)發(fā)植物表型信息采集平臺(tái)和進(jìn)行圖像分析可以加以解決。高通量,、自動(dòng)化,、高分辨率的植物表型信息采集平臺(tái)與分析技術(shù)對(duì)于加快植物改良和育種、提高產(chǎn)量和抗病蟲(chóng)害能力至關(guān)重要,。將植物表型平臺(tái)信息采集平臺(tái)與分析技術(shù)用于解析基因組信息,,定量研究與生長(zhǎng)、產(chǎn)量和適應(yīng)生物或非生物脅迫相關(guān)的復(fù)雜性狀,,是建立植物生長(zhǎng)模型和采集農(nóng)作物高維,、豐富表型數(shù)據(jù)集的重要途徑,能夠滿足填補(bǔ)基因組信息與植物表型可塑性之間空白的需要,。闡述了基于光學(xué)成像的植物表型信息采集平臺(tái)與圖像分析技術(shù)的研究進(jìn)展,,從室內(nèi)、田間不同的使用環(huán)境出發(fā),,根據(jù)不同搭載方式,,總結(jié)分析了各表型平臺(tái)的功能和特點(diǎn)。最后,,分析了目前植物表型信息采集平臺(tái)與分析技術(shù)存在的瓶頸問(wèn)題,,提出了以下建議與展望:開(kāi)發(fā)植物表型信息采集平臺(tái)的多傳感器集成系統(tǒng);將植物生長(zhǎng)環(huán)境監(jiān)測(cè)模塊融入植物表型信息采集平臺(tái)中,;開(kāi)發(fā)針對(duì)林木的表型信息采集平臺(tái),;對(duì)傳感器獲取的表型數(shù)據(jù)進(jìn)行更好的集成與挖掘;采用無(wú)損原位根系信息采集技術(shù)得到植物地下部分的表型數(shù)據(jù),;構(gòu)建表型數(shù)據(jù)統(tǒng)一開(kāi)放的標(biāo)準(zhǔn),,進(jìn)行學(xué)科交叉的深度合作。

    Abstract:

    In recent years, the rapid development of plant genomes, but the lack of sufficient phenotypic data limits the ability of humans to analyze the genetics of quantitative traits. This problem can be effectively solved by developing a plant phenotypic monitoring platform. High-throughput, automated and high-resolution phenotyping platform is critical for accelerating crop improvement and breeding strategies for higher yield and disease tolerance. Plant phenotyping has been advancing at an accelerated rate as a response to the need to fill the gap between genomic information and the plasticity of the plant phenome. Domestic and international efforts have been made to develop phenotyping facilities, and these devices are actively contributing to the generation of high-dimensional, richly informative datasets about the phenotype of model and crop plants. The plant phenotypic monitoring platform integrates multiple sensors for quantitative research on complex traits related to growth, yield, and adaptation to biotic or abiotic stresses such as plant height, leaf number and area, root morphology, biomass, and fruit characteristics. The research progress of plant phenotypic monitoring technology and research status of platform at home and abroad was mainly introduced. The research progress of plant phenotypic information collection platform and technology was introduced, and the functions and characteristics of each were summarized and analyzed. Thus, various phenotypic platform based on indoor and field environments were presented together with applications of these platforms with different mounting modes. An overview of the most commonly used sensors that empower digital phenotyping and the information they provide were presented. Function and feature of each phenotype platform was also analyzed. Meanwhile, an in-depth analysis of image processing with its major issues was given, and the algorithms that were used or emerged as useful to obtain data out of images in an automatic fashion. In this review, the current and emerging methods of image acquisition and processing that allow image-based phenomics were covered. The main bottlenecks that still remained in the field was concluded and the application prospect of plant phenotypic monitoring technology and platform were expected, which pointed out the following challenges: developing plant phenotyping platform of multi-sensor integrated system, introducing plant growth environment monitoring module into plant phenotypic information collection platform, designing forest phenotypic information collection platform, conducting integration and mining phenotype data captured by sensors, collecting the phenotypic data of underground part by nondestructive in situ plant root measurement technology, building unified open standards for phenotypic data and prompting interdisciplinary cooperation.

    參考文獻(xiàn)
    相似文獻(xiàn)
    引證文獻(xiàn)
引用本文

張慧春,周宏平,鄭加強(qiáng),葛玉峰,李楊先.植物表型平臺(tái)與圖像分析技術(shù)研究進(jìn)展與展望[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2020,51(3):1-17. ZHANG Huichun, ZHOU Hongping, ZHENG Jiaqiang, GE Yufeng, LI Yangxian. Research Progress and Prospect in Plant Phenotyping Platform and Image Analysis Technology[J]. Transactions of the Chinese Society for Agricultural Machinery,2020,51(3):1-17.

復(fù)制
分享
文章指標(biāo)
  • 點(diǎn)擊次數(shù):
  • 下載次數(shù):
  • HTML閱讀次數(shù):
  • 引用次數(shù):
歷史
  • 收稿日期:2019-09-23
  • 最后修改日期:
  • 錄用日期:
  • 在線發(fā)布日期: 2020-03-10
  • 出版日期:
文章二維碼