基于深度学习的农业植物表型研究综述  

A survey on deep-learning-based plant phenotype research in agriculture

作　　者：翁杨 曾睿 吴陈铭 王猛[2] 王秀杰[2] 刘永进[1,3] WENG Yang;ZENG Rui;WU ChenMing;WANG Meng;WANG XiuJie;LIU YongJin(Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China;Institute of Genetics and Developmental Biology,Chinese Academy of Sciences,Beijing 100101,China;Key Laboratory of Pervasive Computing,Ministry of Education,Beijing 100084,China)

机构地区：[1]清华大学计算机科学与技术系,北京100084 [2]中国科学院遗传与发育生物学研究所,北京100101 [3]普适计算教育部重点实验室,北京100084

出　　处：《中国科学：生命科学》2019年第6期698-716,共19页Scientia Sinica(Vitae)

基　　金：国家自然科学基金(批准号:61725204,31471198);中国科学院遗传资源研发中心(南方)建设项目(批准号:BM2016034)资助

摘　　要：植物表型是指植物可测量的特征和性状,是植物受自身基因表达、环境影响相互作用的结果,也是决定农作物产量、品质和抗逆性等性状的重要因素.大多数植物表型信息可通过数字图像处理的方法获取和分析.随着基因组学研究的快速发展,传统植物表型研究方法在诸多方面已无法满足进一步研究的需要,高精度、高通量的植物表型获取技术成为植物表型研究的新兴热点方向.近年来深度学习在数字图像处理领域取得了突破性进展,在物体识别、分割等应用上,基于深度学习的图像处理在技术表现上远好于传统方法.在植物表型研究领域,如何使用深度学习技术研究植物表型已成为研究人员十分关注的一项研究问题.本综述从植物株型与生理参数获取、植物识别与杂草检测、病虫害检测以及产量预测四个方面,对近几年基于深度学习的植物表型检测方法进行概述,同时还分析了这些方法和传统机器学习方法的优劣,最后对基于深度学习的植物表型研究的未来趋势进行分析和展望.Plant phenotype refers to measurable traits of plants,which acts as an observable proxy between gene expression and environmental impact,and is also an important determinant for the yield,quality and stress resistance characteristics of crops.Most of the plant phenotypes can be acquired by digital imaging techniques and processed by image processing algorithms.In recent years,the rapid development of genomics advances the study of plant phenotyping in many aspects,especially in terms of high-precision and highthroughput.Traditional plant phenotype research cannot meet these requirements and revolutions are in urgent need.As a breakthrough in computer science,the emergence of deep learning approaches significantly expands the capability of traditional image processing.For instance,state-of-the-art results in identification and segmentation tasks have been achieved by deep-learningbased methods and the records are continually improved by their variants.It is an interesting topic to study how to incorporate deeplearning techniques into plant phenotype research,and various impactful methods have been proposed in the past few years.The objective of this survey is to provide an overview of the current progress of deep-learning-based plant phenotype research in agriculture.In this survey,we elaborate the work from four different aspects,(i)plant morphology and physiological information extraction,(ii)plant identification and weed detection,(iii)pest detection,and(iv)yield prediction.We also analyze the pros and cons of these methods compared to traditional approaches.The potential future trends of plant phenotyping research are discussed at the end of this survey.

关 键 词：植物表型 深度学习 卷积神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术] S184[农业科学—农业基础科学]