Winter Wheat Yield Estimation Based on Multi-Temporal and Multi-Sensor Remote Sensing Data Fusion

Yang Li, Bo Zhao, Jizhong Wang, Yanjun Li and Yanwei Yuan ()
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Yang Li: Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences Group Company Limited, Beijing 100083, China
Bo Zhao: Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences Group Company Limited, Beijing 100083, China
Jizhong Wang: School of Electromechanical and Vehicle Engineering, Weifang University, Weifang 261061, China
Yanjun Li: School of Electromechanical and Vehicle Engineering, Weifang University, Weifang 261061, China
Yanwei Yuan: Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences Group Company Limited, Beijing 100083, China

Agriculture, 2023, vol. 13, issue 12, 1-14

Abstract: Accurate yield estimation before the wheat harvest is very important for precision management, maintaining grain market stability, and ensuring national food security. In this study, to further improve the accuracy of winter wheat yield estimation, machine learning models, including GPR, SVR, and DT, were employed to construct yield estimation models based on the single and multiple growth periods, incorporating the color and multispectral vegetation indexes. The results showed the following: (1) Overall, the performance and accuracy of the yield estimation models based on machine learning were ranked as follows: GPR, SVR, DT. (2) The combination of color indexes and multispectral vegetation indexes effectively improved the yield estimation accuracy of winter wheat compared with the multispectral vegetation indexes and color indexes alone. The accuracy of the yield estimation models based on the multiple growth periods was also higher than that of the single growth period models. The model with multiple growth periods and multiple characteristics had the highest accuracy, with an R 2 of 0.83, an RMSE of 297.70 kg/hm 2 , and an rRMSE of 4.69%. (3) For the single growth period, the accuracy of the yield estimation models based on the color indexes was lower than that of the yield estimation models based on the multispectral vegetation indexes. For the multiple growth periods, the accuracy of the models constructed by the two types of indexes was very close, with R 2 of 0.80 and 0.80, RMSE of 330.37 kg/hm 2 and 328.95 kg/hm 2 , and rRMSE of 5.21% and 5.19%, respectively. This indicates that the low-cost RGB camera has good potential for crop yield estimation. Multi-temporal and multi-sensor remote sensing data fusion can further improve the accuracy of winter wheat yield estimation and provide methods and references for winter wheat yield estimation.

Keywords: UAV; remote sensing; multiple growth periods; vegetation index; color index; yield (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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