Work

Automated soybean quality detection system using deep learning

Year: 2025

Type: article

Abstract: Accurate quantification of impurity and crushing rates during soybean harvesting is critical for intelligent combine harvester optimization. This research presents an inte-grated detection system that synergizes mechanical monolayer sampling with an enhanced scSE-UNet model to address this challenge. A purpose-built imaging device was developed to capture standardized soybean kernel images through controlled monolayer deposition underuniform lighting conditions. The UNet architecture was enhanced by integrating a VGG16-based encoder for multi-scale feature extraction and scSE-block+ modules at decoder skip connections to amplify discriminative spatial-channel features, combined with a Dice-Focal hybrid loss function to mitigate class imbalance caused by kernel adhesion and complex backgrounds. Experimental validation achieved segmentation accuracies of 97.8% (whole kernels), 97.2% (broken kernels), and 96.3% (impurities), with a mean IoU of 93.9%. The system demonstrates operational efficiency with 0.153-second/image process- ing latency, delivering final detection accuracies of 89.8% for impurity rate and 88.2% for crushing rate, confirming its robustness for real-time harvester monitoring applications.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.101473