Intelligent Classification of Soybean Threshing Mixtures Based on Edge Perception and CNN–Transformer Hybrid Architecture

To address the low monitoring accuracy of traditional methods caused by the complex composition and severe signal overlapping of threshed materials during soybean threshing, this study proposes a high-precision impact signal classification system based on edge perception and a hybrid deep learning architecture, serving as a foundational step for threshing loss monitoring. At the hardware level, a high-speed parallel sensing system was developed to achieve continuous acquisition and high-fidelity mapping of transient impact signals. At the algorithmic level, a CNN–Transformer hybrid network was constructed to effectively extract local signal features and capture long-term temporal dependencies, successfully decoupling complex collision dynamics. Bench tests demonstrate that the hybrid model achieves a comprehensive classification accuracy of 97.36% and F1-scores above 0.96 for soybean grains, stems, and pods, significantly outperforming single networks. Furthermore, feature visualization confirms that the model effectively extracted features strongly correlated with the intrinsic impact dynamics of different materials rather than simply fitting environmental noise. This study provides a highly robust algorithm foundation and bench-level engineering reference for the intelligent classification of soybean harvesting mixtures, laying the groundwork for actual loss estimation under real field conditions.