A Ripeness Identification Method of Pitaya (Dragon Fruit) Based on the Improved YOLOv8 Model

To address the low accuracy of pitaya ripeness recognition caused by complex backgrounds and target occlusions in unstructured environments, this study proposed an improved YOLOv8n-based model for pitaya ripeness detection. The YOLOv8-MD model was built based on YOLOv8n by integrating the MobileNetV3-Small backbone network and incorporating the DIoU loss function. This design fused local low-dimensional information and global features of pitayas in field images, thereby enhancing the model’s capability to extract ripeness-related features and mitigating challenges associated with target occlusion and small-sized targets in unstructured settings. Experiment results demonstrated that in unstructured orchard environments, the proposed model achieves a precision of 94.2%, a recall of 87.5% and a mean average precision (mAP) of 95.4%, representing improvements of 7.4%, 7.0%, and 6.1% respectively over the baseline YOLOv8n model. Concurrently, the model’s parameter count was reduced to 2.77 M, and the total floating-point operations (FLOPs) were compressed to 6.4 GFLOPS, a 20.9% reduction compared to the baseline. Deployment testing on the Nvidia Orin Nano platform yielded an inference speed of 83.13 FPS, which was 9.2% to 41.1% faster than the traditional YOLO models. This research provided a valuable framework for the development of field-based ripeness detection models for pitayas and other fruits with similar characteristics.