Improved wheat ear recognition method based on GAM-YOLOv8n

In view of the irregular shape of wheat ears, susceptibility to environmental light, and the dense and occluded state in the field, traditional methods of wheat ear recognition suffer from low accuracy, high error rates, and inefficiency. This paper proposes a GAM-YOLOv8n deep learning model for wheat ear detection. First, the GAM (Global Attention Module) is proposed to improve the capability of capturing features of wheat ears in the farmland. Next, the Wise IoU loss function is used to replace the original loss function of CIoU for a more precise measurement of the similarity between the real and predicted bounding boxes of the wheat ears. The improved show that compared with the initial YOLOv8n network, the improved network performance increases the Precison (P) by 3.7%, detection precision (PR) by 1.1%, and mean Average Precision (mAP) by 2.7%.