An algorithmic model for recognizing healthy wheat seeds based on YOLOv8

Seed quality is a key factor in wheat germination rate, total yield and other indicators, and is of great importance for food security. For a long time, wheat seed selection and breeding have been heavily dependent on manual experience selection, which is characterized by high labor costs, unstable detection accuracy and high subjectivity. In this paper, we propose a machine vision recognition algorithm model WSEED-YOLOV8 for healthy wheat seeds improved from YOLO V8, which significantly improves the model's ability to extract features for wheat seed categories and whether wheat seeds suffer from Sunn Pest (Eurygaster integriceps) damage by introducing Deformable Convolution V2 (DCNv2) and encapsulating it in the C2f_DCNv2 module instead of the second, third and fourth C2f modules in YOLO V8n. By introducing the Polarized Self Attention mechanism module, with a slight increase in the parameters, it reduces the omission of false detections in the case of dense stacking of wheat seeds in the same image; and by using the Shape Loss Shape-IoU function as the IoU calculation function of the bounding box, which improves the target detection ability in the case of tilted wheat placement. The experimental results show that the WSEED-YOLOV8 wheat healthy seed recognition algorithm model achieves a recognition precision of 95% on the same wheat seed dataset, which is 15.9% higher than the YOLO V8n baseline precision. An 11% improvement in recognition precision was achieved with a similar number of parameters as the YOLO V8s baseline.