Winter Wheat Yield Estimation with Color Index Fusion Texture Feature

Fuqin Yang, Yang Liu, Jiayu Yan, Lixiao Guo, Jianxin Tan, Xiangfei Meng, Yibo Xiao and Haikuan Feng ()
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Fuqin Yang: College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, China
Yang Liu: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Jiayu Yan: College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, China
Lixiao Guo: College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, China
Jianxin Tan: College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, China
Xiangfei Meng: College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, China
Yibo Xiao: College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, China
Haikuan Feng: Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Agriculture, 2024, vol. 14, issue 4, 1-16

Abstract: The rapid and accurate estimation of crop yield is of great importance for large-scale agricultural production and national food security. Using winter wheat as the research object, the effects of color indexes, texture feature and fusion index on yield estimation were investigated based on unmanned aerial vehicle (UAV) high-definition digital images, which can provide a reliable technical means for the high-precision yield estimation of winter wheat. In total, 22 visible color indexes were extracted using UAV high-resolution digital images, and a total of 24 texture features in red, green, and blue bands extracted by ENVI 5.3 were correlated with yield, while color indexes and texture features with high correlation and fusion indexes were selected to establish yield estimation models for flagging, flowering and filling stages using partial least squares regression (PLSR) and random forest (RF). The yield estimation model constructed with color indexes at the flagging and flowering stages, along with texture characteristics and fusion indexes at the filling stage, had the best accuracy, with R 2 values of 0.70, 0.71 and 0.76 and RMSE values of 808.95 kg/hm 2 , 794.77 kg/hm 2 and 728.85 kg/hm 2 , respectively. The accuracy of winter wheat yield estimation using PLSR at the flagging, flowering, and filling stages was better than that of RF winter wheat estimation, and the accuracy of winter wheat yield estimation using the fusion feature index was better than that of color and texture feature indexes; the distribution maps of yield results are in good agreement with those of the actual test fields. Thus, this study can provide a scientific reference for estimating winter wheat yield based on UAV digital images and provide a reference for agricultural farm management.

Keywords: UAV; color index; fusion texture; partial least squares; random forest (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: 2024
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