Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images

编号 030023805

推送时间 20200511

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

标题 Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images

来源期刊 SENSORS

期 第238期

发表时间 20200224

关键词 regression technology;  yield;  hyperspectral image;  extracted plant height H-CSM;  estimation model;  winter wheat; 

摘要 Crop yield is related to national food security and economic performance, and it is therefore important to estimate this parameter quickly and accurately. In this work, we estimate the yield of winter wheat using the spectral indices (SIs), ground-measured plant height (H), and the plant height extracted from UAV-based hyperspectral images (H-CSM) using three regression techniques, namely partial least squares regression (PLSR), an artificial neural network (ANN), and Random?Forest?(RF). The SIs, H, and H-CSM were used as input values, and then the PLSR, ANN, and RF were trained using regression techniques. The three different regression techniques were used for modeling and verification to test the stability of the yield estimation. The results showed that: (1) H-CSM is strongly correlated with H (R-2 = 0.97); (2) of the regression techniques, the best yield prediction was obtained using PLSR, followed closely by ANN, while RF had the worst prediction performance; and (3) the best prediction results were obtained using PLSR and training using a combination of the SIs and H-CSM as inputs (R-2 = 0.77, RMSE = 648.90 kg/ha, NRMSE = 10.63%). Therefore, it can be concluded that PLSR allows the accurate estimation of crop yield from hyperspectral remote sensing data, and the combination of the SIs and H-CSM allows the most accurate yield estimation. The results of this study indicate that the crop plant height extracted from UAV-based hyperspectral measurements can improve yield estimation, and that the comparative analysis of PLSR, ANN, and RF regression techniques can provide a reference for agricultural management.

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