基于数字化植物表型平台(D3P)的田间小麦冠层光截获算法开发  

An algorithm for estimating field wheat canopy light interception based on Digital Plant Phenotyping Platform

作　　者：刘守阳 金时超 郭庆华[5,6] 朱艳 Fred Baret[1,2,3] Shouyang Liu;Shichao Jin;Qinghua Guo;Yan Zhu;Fred Baret(Plant Phenomics Research Centre,Nanjing Agricultural University,Nanjing 210095,China;INRAE,EMMAH-CAP TE,Avignon 84914,France;Jiangsu Collaborative Innovation Centre for Modern Crop Production,Nanjing Agricultural University,Nanjing 210095,China;Nanjing Engineering and Technology Centre for Information Agriculture,Nanjing Agricultural University/Engineering Research Centre for Smart Agriculture,Ministry of Education,Nanjing 210095,China;State Key L aboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences,Beijing 100093,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]南京农业大学作物表型组学交叉研究中心,江苏南京210095 [2]法国农业和环境科学研究院CAPTE实验室,法国阿维尼翁210095 [3]南京农业大学江苏省现代作物生产协同创新中心,江苏南京210095 [4]南京农业大学国家信息农业工程技术中心/教育部智慧农业工程研究中心,江苏南京210095 [5]中国科学院植物研究所植被与环境变化国家重点实验室,北京100093 [6]中国科学院大学,北京100049

出　　处：《智慧农业（中英文）》2020年第1期87-98,共12页Smart Agriculture

基　　金：南京农业大学高层次引进人才经费(440-804005)。

摘　　要：冠层光截获能力是反映作物品种间差异的重要功能性状,高通量表型冠层光截获对提高作物改良效率具有重要意义。本研究以小麦为研究目标,利用数字化植物表型平台(D3P)模拟生成了100种冠层结构不同的小麦品种在5个生育期的三维冠层场景,记录了从原始冠层结构中提取的绿色叶面积指数(GAI)、平均倾角(AIA)和散射光截获率(FIPAR_(dif))信息作为真实值,进一步利用上述三维小麦场景开展了虚拟的激光雷达(LiDAR)模拟实验,生成了对应的三维点云数据。基于模拟的点云数据提取了其高度分位数特征(H)和绿色分数特征(GF)。最后,利用人工神经网络(ANN)算法分别构建了从不同LiDAR点云特征(H、GF和H+GF)输入到FIPAR_(dif)、GAI和AIA的反演模型。结果表明,对于GAI、AIA和FIPAR_(dif),预测精度从高到低对应的点云特征输入为GF+H> H> GF。由此可见,H特征对提高目标表型特性的估算精度起到了重要作用。输入GF+H特征,在中等测量噪音(10%)情况下,FIPAR_(dif)和GAI的估算均获得了满意精度,R^2分别为0.95和0.98,而AIA的估算精度(R^2=0.20)还有待进一步提升。本研究基于D3P模拟数据开展,算法的实际表现还有待通过田间数据进一步验证。尽管如此,本研究验证了D3P协助表型算法开发的能力,展示了高通量LiDAR数据在估算田间冠层光截获和冠层结构方面的较高潜力。The capacity of canopy light interception is a key functional trait to distinguish the phenotypic variation over genotypes.High-throughput phenotyping canopy light interception in the field,therefore,would be of high interests for breeders to increase the efficiency of crop improvement.In this research,the Digital Plant Phenotyping Platform(D3P)was used to conduct in-silico phenotyping experiment with LiDAR scans over a wheat field.In this experiment virtual 3D wheat canopies were generated over 100 wheat genotypes for 5 growth stages,representing wide range of canopy structural variation.Accordingly,the actual value of traits targeted were calculated including GAI(green area index),AIA(average inclination angle)and FIPARdif(the fraction of intercepted diffuse photosynthetically activate radiation).Then,virtual LiDAR scanning were accomplished over all the treatments and exported as 3D point cloud.Two types of features were extracted from point cloud,including height quantiles(H)and green fractions(GF).Finally,an artificial neural network was trained to predict the traits targeted from different combinations of LiDAR features.Results show that the prediction accuracy varies with the selection of input features,following the rank as GF+H>H>GF.Regarding the three traits,we achieved satisfactory accuracy for FIPAR&f(R2=0.95)and GAI(R2=0.98)but not for AIA(R2=0.20).This highlights the importance of H feature with respect to the prediction accuracy.The results achieved here are based on in-silico experiments,further evaluation with field measurement would be necessary.Nontheless,as proof of concept,this work further demonstrates that D3P could greatly facilitate the algorithm development.Morever,it highlights the potential of LiDAR measurement in the high-throuhgput phenopyting of canopy light interpcetion and structural traits in the field.

关 键 词：冠层光截获 高通量表型 LIDAR 数字化植物表型平台(D3P) 小麦冠层 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]