MEGNet: A Multi-Scale Edge Geometry-Aware Network for Green Plum Detection in Picking Orchard Environment

In response to the challenges of large fruit-scale variation, dense target distribution, severe leaf occlusion, and complex backgrounds in green plum detection within orchards, this paper proposes a lightweight multi-scale edge geometry-aware network (MEGNet). First, the Green Plum Detection Dataset (GPD) is constructed to provide realistic orchard scene data for the task. Next, we enhance the model’s structure based on YOLO11n by designing an efficient multi-scale feature fusion attention module (EMFFA) to improve the expression of multi-scale fruit features. We also introduce a color-edge guided dual-discriminator feature enhancement module (CED) to strengthen feature discrimination in complex backgrounds. A coordinate attention ghost detection head (CAGDetect) is proposed to reduce model parameters and computational complexity. Additionally, a geometry-consistency modulated CIoU loss function (GC-CIoU) is introduced to improve target localization stability in occluded and dense scenes by incorporating a geometric consistency modulation mechanism. Experimental results show that on the GPD, MEGNet achieves a Precision of 93.9%, Recall of 86.2%, mAP50 of 93.2%, and mAP50:95 of 76.1%. The model’s Parameters are only 2.13 M, with FLOPs of 4.7 G. Compared to the baseline YOLO11n model, Precision, Recall, mAP50, and mAP50:95 are improved by 2.5%, 5.2%, 4.4%, and 4.6%, respectively. Additionally, deployment experiments on the Jetson Orin Nano embedded device demonstrate real-time detection speeds of 31–33 FPS. The proposed method provides an efficient and reliable solution for intelligent harvesting systems, orchard monitoring platforms, and agricultural robot vision perception.