Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping:A Review
作者机构：Horticulture SectionSchool of Integrative Plant ScienceCornell AgriTechCornell UniversityUSA School of Electrical and Computer EngineeringCollege of EngineeringThe University of GeorgiaUSA Phenomics and Plant Robotics CenterThe University of GeorgiaUSA
出 版 物：《Plant Phenomics》 (植物表型组学（英文）)
年 卷 期：2020年第2卷第1期
页 面：73-94页
核心收录：
学科分类：0710[理学-生物学] 071001[理学-植物学] 07[理学] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：The project was supported by the National Robotics Initiative(NIFA grant no.2017-67021-25928)
摘 要：Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs,understanding plantenvironment interactions,and managing agricultural *** the past five years,imaging approaches have shown great potential for high-throughput plant phenotyping,resulting in more attention paid to imaging-based plant *** this increased amount of image data,it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and *** goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks(CNNs)in plant phenotyping *** specifically review the use of various CNN architecture for plant stress evaluation,plant development,and postharvest quality *** systematically organize the studies based on technical developments resulting from imaging classification,object detection,and image segmentation,thereby identifying state-of-the-art solutions for certain phenotyping ***,we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.
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