An application of improved YOLOv8 in cherry recognition in natural environment

To overcome challenges related to occlusion and varying lighting conditions in cherry detection, we propose an enhanced YOLOv8-based model for cherry recognition. We have modified the YOLOv8 model’s ‘neck’ section by incorporating a BiFPN-based pyramidal network, which enhances feature fusion across various levels, thereby improving the model’s ability to identify partially obscured cherries. Additionally, to enhance adaptability to diverse lighting and to better detect small targets, we utilize an AFPN to refine the detection head, thereby improving the model’s performance across varying lighting scenarios. For training and validation purposes, we created a custom dataset of cherries that includes instances of occlusion and complex lighting. Our experimental findings indicate that the enhanced algorithm outperforms the original YOLOv8 network, with improvements in accuracy, recall, and mean Average Precision (mAP) by 4.2%, 2.6%, and 2.3%, respectively. Notably, the improved model demonstrates greater robustness in handling occluded cherries and complex lighting situations. The model achieves high recognition accuracy and robustness, offering valuable insights for future research in cherry recognition.