Detection method of broken grains and impurities in harvested soybeans using feature wavelength selection and MobileNetV4-Unet-SGCPNet hybrid network

To address the challenges of hyperspectral data redundancy, small-target segmentation difficulty, and insufficient model real-time performance in high-precision online detection of foreign matter and kernel breakage in machine-harvested soybeans, this study proposes a collaborative detection method based on "feature wavelength optimization + MobileNetV4-Unet-SGCPNet hybrid network". First, 18 key feature bands were screened from 400 - 1000 nm hyperspectral data using successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), constructing a multi-source feature spectral image dataset. Subsequently, a MobileNetV4-Unet-SGCPNet hybrid network was designed, with a lightweight MobileNetV4 as the encoder, combined with the symmetric encoder-decoder structure of Unet and the spatial detail-guided context propagation module (SGCP) to achieve high-precision segmentation of broken grains, complete grains, and impurities. Finally, pixel-wise voting was employed to fuse multi-band feature information, enhancing the model’s generalization capability. The results demonstrate that: on the test set, the model achieves an average intersection - over - union of 89.69 % for soybean component recognition, with a mean precision average of 94.55 %, a mean precision of 93.63 %, a frame rate of 4.77 FPS, a parameter count of only 2.86 MB, and a computational load of 35.78 GFLOPs. Compared to mainstream models, this method reduces parameters by 97.2 % and computational cost by 97.8 %, while the average intersection - over - union drops by only 6.2 %, with a frame rate improvement of over 5 times, striking a significant balance between detection accuracy and real-time performance. Cross-variety and cross-device validations further confirm that the model effectively adapts to morphological and spectral variations across different soybean varieties, exhibiting strong generalization ability. This study provides a core algorithmic foundation for online monitoring systems of intelligent harvester operation quality, offering critical support for enhancing the commercial value of machine-harvested soybeans and advancing the intelligence level of agricultural machinery.