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Winter Wheat Yield Estimation with Color Index Fusion Texture Feature

文献类型： 外文期刊

作者： Yang, Fuqin 1 ; Liu, Yang 2 ; Yan, Jiayu 1 ; Guo, Lixiao 1 ; Tan, Jianxin 1 ; Meng, Xiangfei 1 ; Xiao, Yibo 1 ; Feng, Haikuan 2 ;

作者机构： 1.Henan Univ Engn, Coll Civil Engn, Zhengzhou 451191, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

3.Nanjing Agr Univ, Natl Engn & Technol Ctr Informat Agr, Nanjing 210095, Peoples R China

关键词： UAV; color index; fusion texture; partial least squares; random forest

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）

ISSN：

年卷期： 2024 年 14 卷 4 期

页码：

收录情况： SCI

摘要： 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 R2 values of 0.70, 0.71 and 0.76 and RMSE values of 808.95 kg/hm2, 794.77 kg/hm2 and 728.85 kg/hm2, 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.

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