Ma Na, Sun Yile, Li Chenfei, Liu Zonglin, Song Haiyan
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, China.
Dryland Farm Machinery Key Technology and Equipment Key Laboratory of Shanxi Province, Jinzhong, China.
Front Plant Sci. 2025 May 23;16:1580325. doi: 10.3389/fpls.2025.1580325. eCollection 2025.
To achieve fast detection of pear fruits in natural pear orchards and optimize path planning for harvesting robots, this study proposes the AHG-YOLO model for multi-category detection of pear fruit occlusion in complex orchard environments.
Using the Red Delicious pear as the research object, the pears are classified into three categories based on different occlusion statuses: non-occluded fruits (NO), fruits occluded by leaves/branches (OBL), and fruits in close contact with other fruits but not obstructed by leaves/branches (FCC). The YOLOv11n model is used as the base model for a lightweight design. First, the sampling method in the backbone and neck networks is replaced with ADown downsampling to capture higher-level image features, reducing floating-point operations and computational complexity. Next, shared weight parameters are introduced in the head network, and group convolution is applied to achieve a lightweight detection head. Finally, the boundary box loss function is changed to Generalized Intersection over Union (GIoU), improving the model's convergence speed and further enhancing detection performance.
Experimental results show that the AHG-YOLO model achieves 93.5% (FCC), 95.3% (NO), and 93.4% (OBL) in AP, with an mAP@0.5 of 94.1% across all categories. Compared to the base YOLOv11n network, precision, recall, mAP@0.5, and mAP@0.5:0.95 are improved by 2.5%, 3.6%, 2.3%, and 2.6%, respectively. The model size is only 5.1MB, with a 16.9% reduction in the number of parameters.
The improved model demonstrates enhanced suitability for deployment on pear-harvesting embedded devices, providing technical support for the path planning of fruit-picking robotic arms.
为实现自然梨园中梨果的快速检测并优化采摘机器人的路径规划，本研究提出了用于复杂果园环境中梨果遮挡多类别检测的AHG-YOLO模型。
以红富士梨为研究对象，根据不同遮挡状态将梨分为三类：未被遮挡的果实（NO）、被叶片/树枝遮挡的果实（OBL）以及与其他果实紧密接触但未被叶片/树枝阻挡的果实（FCC）。使用YOLOv11n模型作为基础模型进行轻量化设计。首先，将骨干网络和颈部网络中的采样方法替换为ADown下采样以捕获更高层次的图像特征，减少浮点运算和计算复杂度。其次，在头部网络中引入共享权重参数，并应用分组卷积以实现轻量化检测头。最后，将边界框损失函数改为广义交并比（GIoU），提高模型的收敛速度并进一步增强检测性能。
实验结果表明，AHG-YOLO模型在AP方面，FCC类达到93.5%，NO类达到95.3%，OBL类达到93.4%，所有类别的mAP@0.5为94.1%。与基础YOLOv11n网络相比，精度、召回率、mAP@0.5和mAP@0.5:0.95分别提高了2.5%、3.6%、2.3%和2.6%。模型大小仅为5.1MB，参数数量减少了16.9%。
改进后的模型显示出更适合部署在梨采摘嵌入式设备上，为水果采摘机器人手臂的路径规划提供了技术支持。
