Segmentation Is Not the Purpose: A Wheat Impurity Regression Network Integrating Semantic Segmentation

Real-time and accurate acquisition of the wheat impurity rate is a key technology for realizing intelligent cleaning operations, and it directly influences the quality of wheat harvest. This study proposes a novel impurity rate regression network named Segmentation is Not The Purpose (SNTP). SNTP integrates a semantic segmentation network and an impurity rate regression network into a single neural architecture and replaces the DeepLabV3+ backbone with MobileNetV4, which serves as the segmentation branch of SNTP. Furthermore, a Transformer block is introduced into the regression branch to enable global feature extraction, and a Generalized Categorical Regression head is designed based on Distribution Focal Loss to improve regression accuracy. The SNTP model ultimately achieves an MIoU of 77.7%, an MPA of 83.3%, an MAE of 0.045, and an MSE of 0.005 on the validation set, with only 9.51M parameters and 17.98 GMACs of computation, successfully solving the overfitting problem in impurity rate regression networks and achieving high regression accuracy. SNTP is easy to optimize, requires no additional prior knowledge, and the performance of the SNTP model is unaffected by camera mounting height, making it exceptionally versatile for deployment and enabling real-time impurity rate detection, which is the key technology for intelligent cleaning.