Automatic grading evaluation of winter wheat lodging based on deep learning

作者：Hecang Zang, Xinqi Su, Yanjing Wang, Guoqiang Li, Jie Zhang, Guoqing Zheng, Weiguo Hu, Hualei Shen · 发表于：Frontiers in Plant Science · 年份：2024 · DOI：10.3389/fpls.2024.1284861 · 被引用次数：10 · 研究领域：Smart Agriculture and AI、Remote Sensing in Agriculture、Crop Yield and Soil Fertility

Lodging is a crucial factor that limits wheat yield and quality in wheat breeding. Therefore, accurate and timely determination of winter wheat lodging grading is of great practical importance for agricultural insurance companies to assess agricultural losses and good seed selection. However, using artificial fields to investigate the inclination angle and lodging area of winter wheat lodging in actual production is time-consuming, laborious, subjective, and unreliable in measuring results. This study addresses these issues by designing a classification-semantic segmentation multitasking neural network model MLP_U-Net, which can accurately estimate the inclination angle and lodging area of winter wheat lodging. This model can also comprehensively, qualitatively, and quantitatively evaluate the grading of winter wheat lodging. The model is based on U-Net architecture and improves the shift MLP module structure to achieve network refinement and segmentation for complex tasks. The model utilizes a common encoder to enhance its robustness, improve classification accuracy, and strengthen the segmentation network, considering the correlation between lodging degree and lodging area parameters. This study used 82 winter wheat varieties sourced from the regional experiment of national winter wheat in the Huang-Huai-Hai southern area of the water land group at the Henan Modern Agriculture Research and Development Base. The base is located in Xinxiang City, Henan Province. Winter wheat ...