产量(工程)
索引(排版)
估计
数学
精准农业
农学
均方误差
作物
回归分析
叶面积指数
统计
环境科学
农业
计算机科学
生物
生态学
工程类
万维网
冶金
材料科学
系统工程
作者
Yuan Liu,Chenwei Nie,Zhen Zhang,Zhen-lin WANG,Bo Ming,Junmin Xue,Hongye Yang,Honggen Xu,Lin Meng,Ningbo Cui,Wenbin Wu,Xiuliang Jin
标识
DOI:10.3389/fpls.2022.979103
摘要
Timely and accurate pre-harvest estimates of maize yield are vital for agricultural management. Although many remote sensing approaches have been developed to estimate maize yields, few have been tested under lodging conditions. Thus, the feasibility of existing approaches under lodging conditions and the influence of lodging on maize yield estimates both remain unclear. To address this situation, this study develops a lodging index to quantify the degree of lodging. The index is based on RGB and multispectral images obtained from a low-altitude unmanned aerial vehicle and proves to be an important predictor variable in a random forest regression (RFR) model for accurately estimating maize yield after lodging. The results show that (1) the lodging index accurately describes the degree of lodging of each maize plot, (2) the yield-estimation model that incorporates the lodging index provides slightly more accurate yield estimates than without the lodging index at three important growth stages of maize (tasseling, milking, denting), and (3) the RFR model with lodging index applied at the denting (R5) stage yields the best performance of the three growth stages, with R 2 = 0.859, a root mean square error (RMSE) of 1086.412 kg/ha, and a relative RMSE of 13.1%. This study thus provides valuable insight into the precise estimation of crop yield and demonstra\tes that incorporating a lodging stress-related variable into the model leads to accurate and robust estimates of crop grain yield.
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