Developing soybean yield prediction model based on multicolor space and texture features of UAV images

作者：Zhiming Yu, Qi Chen, Qiqing Shan, You Lv, Zhengmeng Chen, Yitong Zhou, Pei Zhang, Haidong Jiang, Weixing Cao · 发表于：Computers and Electronics in Agriculture · 年份：2025 · DOI：10.1016/j.compag.2025.110345 · 被引用次数：11 · 研究领域：Remote Sensing and Land Use、Remote Sensing in Agriculture、Smart Agriculture and AI

• Skew distribution parameters provided more leaf color information. • Canopy multistage image feature parameters expanded data dimension for prediction model. • BPNN model constructed by multiple parameters of multistage performed accuracy of 88.2%. In order to intelligently and non-destructively estimate soybean yield, the fusion of multicolor space and texture feature parameters were considered to construct a yield prediction model. In this study, the yield prediction model was developed through different stands formed by a management experiment of different planting densities and nitrogen application strategies, and validated through a variety test of 28 soybean varieties. Images of the soybean canopy were collected during the key period for yield (the florescence, podding, and grain-filling stages) by unmanned aerial vehicle (UAV). The multicolor space and texture feature parameters of the RGB images of soybean canopy were extracted for these three periods, and soybean yield prediction models were constructed for the florescence, podding, grain-filling, and multiple growth stages based on different parameters combinations, by using methods of multiple linear regression (SMLR), random forest (RF), and back propagation neural networks (BPNN). The results showed that the color space and texture features of the soybean canopy images exhibited significant differences and different trends during the florescence, podding, and grain-filling stages. Models built with the combinat...