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

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

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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 consid-ering 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 phe-notypic trait(spike-leaf composite indicator,SLI)accounting for the contribution of the spike to photo-synthesis,(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 learn-ing algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar varia-tion 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 determina-tion,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.

Wheat spike photosynthesisYield-related phenotypic traitSpectral indicesMachine learningEstimation

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

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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

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaEarmarked Fund for Jiangsu Agricultural Industry Technology SystemEarmarked Fund for Jiangsu Agricultural Industry Technology SystemJiangsu Provincial Cooperative Promotion Plan of Major Agricultural TechnologiesPostgraduate Research & Practice Innovation Program of Jiangsu Province

3237199031971784JATS2022168JATS20224682021-ZYXT-01-1KYCX23_0783

2024

10.1016/j.cj.2024.04.003

作物学报(英文版)

作物学报(英文版)

CSTPCD
ISSN：
年,卷(期)：2024.12(3)
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