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.

This study aims to develop an instance segmentation model for the joint segmentation of pomelo fruits and stems in complex natural orchard environments, with particular emphasis on slender, small-scale, and easily occluded stem targets. To this end, YOLO-MOD, an improved instance segmentation algorithm based on YOLOv11-seg, is proposed. Specifically, Omni-Dimensional Dynamic Convolution (ODConv) is introduced into the C3k2 module to enhance complex feature representation; a Multi-Scale Dilated Attention (MSDA) module is embedded to improve the multi-scale semantic perception of slender stem regions; and the original upsampling operator is replaced with DySample to strengthen fine-grained boundary recovery. Experimental results show that, compared with the original YOLOv11-seg, YOLO-MOD improves the Box mAP@50 and Mask mAP@50 by 2.9% and 3.9%, respectively. For the Stem class, the Box mAP@50 and Mask mAP@50 increase from 71.9% to 77.8% and from 68.4% to 76.2%, respectively. These results indicate that YOLO-MOD can achieve fine-grained segmentation of pomelo fruits and stems on the dataset used in this study. However, its generalization capability across different orchards, seasons, pomelo varieties, and fruit types still requires further evaluation, and its practical effectiveness in an integrated robotic harvesting system remains to be further validated.

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 proposes 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 outperforms the original YOLOv8 model in terms of Precision (P) and mean Average Precision (mAP) by 1.3% and 2.2%, respectively. Additionally, the model reduces 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 demonstrates significant robustness, achieving a detection accuracy of 87.3% 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.