Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning  

作　　者：Haiyu Tao Ruiheng Zhou Yining Tang Wanyu Li Xia Yao Tao Cheng Yan Zhu Weixing Cao Yongchao Tian 

机构地区：[1]National Engineering and Technology Center for Information Agriculture,Key Laboratory of Crop System Analysis and Decision Making,Ministry of Agriculture and Rural Affairs,Jiangsu Key Laboratory for Information Agriculture,Nanjing Agricultural University,Nanjing 210095,Jiangsu,China

出　　处：《The Crop Journal》2024年第3期927-937,共11页作物学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(32371990,31971784);the Earmarked Fund for Jiangsu Agricultural Industry Technology System(JATS(2022)168,JATS(2022)468);the Jiangsu Provincial Cooperative Promotion Plan of Major Agricultural Technologies(2021-ZYXT-01-1);the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_0783)。

摘　　要：The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

关 键 词：Wheat spike photosynthesis Yield-related phenotypic trait Spectral indices Machine learning Estimation 

分 类 号：S512.1[农业科学—作物学]