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Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images by Huilin Tao; Haikuan Feng; Liangji Xu; Mengke Miao; Guijun Yang; Xiaodong Yang; Lingling Fan is a Engineering article available to read on EtoBox.

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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 (HCSM) using three regression techniques, namely partial least squares regression (PLSR), an artificial neural network (ANN), and Random Forest (RF). The SIs, H, and HCSM 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) HCSM is strongly correlated with H (R2 = 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 HCSM as inputs (R2 = 0.77, RMSE = 648.90 kg/ha, NRMSE = 10.63%). Therefore, it can be concluded that PLSR allows th

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Author

Huilin Tao; Haikuan Feng; Liangji Xu; Mengke Miao; Guijun Yang; Xiaodong Yang; Lingling Fan

Publisher

Molecular Diversity Preservation International; MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: Molecular Diversity Preservation International (MDPI), 2001-; Publons (ISSN 1424-8220)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)