Small crop detection based on improved YOLOv8 algorithm for robotic pollination

Traditional object detection algorithm YOLOv8 faces challenges when robotic pollination needs to identify crops, such as misdetection and omission due to the small size of the crop, dense planting, vegetation cover and background confusion. To address these issues, an improved algorithm based on YOLOv8 is proposed to enhance detection performance. First, the small object detection layer is added to enhance the learning ability of feature information of small objects. Second, BiFPN is used to replace FPN and PAN for multi-scale feature fusion. Finally, two existing difficult sample mining methods, Loss Rank Mining (LRM) and focal loss (FL), are introduced to adjust the optimized loss function and achieve difficult sample mining to improve the detection of small crops. Comparative and ablation tests are carried out using tomato flower image datasets derived from a simulated autonomous pollination robot sensing system in order to verify the efficacy of the revised method. The results demonstrate that the improved YOLOv8 algorithm enhances the average accuracy by 9.787% and achieves more accurate detection of small crop.