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

文献类型： 外文期刊

作者： Tao, Huilin 1 ; Feng, Haikuan 1 ; Xu, Liangji 2 ; Miao, Mengke 1 ; Yang, Guijun 1 ; Yang, Xiaodong 1 ; Fan, Lingling 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Anhui Univ Sci & Technol, Sch Geodesy & Geomat, Huainan 232001, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Beijing Engn Res Ctr Agr Internet Things, Beijing 100097, Peoples R China

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

期刊名称：SENSORS （ 影响因子：3.576； 五年影响因子：3.735 ）

ISSN：

年卷期： 2020 年 20 卷 4 期

页码：

收录情况： SCI

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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