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Open AccessArticle

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

by Huilin Tao 1,2, Haikuan Feng 1,3,4,*, Liangji Xu 2, Mengke Miao 1,3, Guijun Yang 1,3,4, Xiaodong Yang 1,3,4 and Lingling Fan 1,3
1
Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture and Rural Affairs, China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2
School of Geodesy and Geomatics, Anhui University of Science and Technology, Huainan 232001, China
3
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
4
Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 1231; https://doi.org/10.3390/s20041231
Received: 13 February 2020 / Revised: 21 February 2020 / Accepted: 22 February 2020 / Published: 24 February 2020
(This article belongs to the Section Remote Sensors)
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 the accurate estimation of crop yield from hyperspectral remote sensing data, and the combination of the SIs and HCSM 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. View Full-Text
Keywords: regression technology; yield; hyperspectral image; extracted plant height HCSM; estimation model; winter wheat regression technology; yield; hyperspectral image; extracted plant height HCSM; estimation model; winter wheat
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Tao, H.; Feng, H.; Xu, L.; Miao, M.; Yang, G.; Yang, X.; Fan, L. Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images. Sensors 2020, 20, 1231.

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