YOLO-MCS: A Lightweight Loquat Object Detection Algorithm in Orchard Environments

To address the challenges faced by loquat detection algorithms in orchard settings—including complex backgrounds, severe branch and leaf occlusion, and inaccurate identification of densely clustered fruits—which lead to high computational complexity, insufficient real-time performance, and limited recognition accuracy, this study proposed a lightweight detection model based on the YOLO-MCS architecture. First, to address fruit occlusion by branches and leaves, the backbone network adopts the lightweight EfficientNet-b0 architecture. Leveraging its composite model scaling feature, this significantly reduces computational costs while balancing speed and accuracy. Second, to deal with inaccurate recognition of densely clustered fruits, the C2f module is enhanced. Spatial Channel Reconstruction Convolution (SCConv) optimizes and reconstructs the bottleneck structure of the C2f module, accelerating inference while improving the model’s multi-scale feature extraction capabilities. Finally, to overcome interference from complex natural backgrounds in loquat fruit detection, this study introduces the SimAm module during the initial detection phase. Its feature recalibration strategy enhances the model’s ability to focus on target regions. According to the experimental results, the improved YOLO-MCS model outperformed the original YOLOv8 model in terms of Precision (P) and mean Average Precision (mAP) by 1.3% and 2.2%, respectively. Additionally, the model reduced GFLOPs computation by 34.1% and Params by 43.3%. Furthermore, in tests under complex weather conditions and with interference factors such as leaf occlusion, branch occlusion, and fruit mutual occlusion, the YOLO-MCS model demonstrated significant robustness, achieving mAP of 89.9% in the loquat recognition task. The exceptional performance serves as a robust technical base on the development and research of intelligent systems for harvesting loquats.