YOLO11-PGM: High-Precision Lightweight Pomegranate Growth Monitoring Model for Smart Agriculture

As a vital cash crop, intelligent monitoring of pomegranate growth stages plays a crucial role in improving orchard management efficiency and yield. Current research on pomegranates primarily focuses on the detection and quality classification of diseases and pests. Furthermore, the difficulty in deploying complex models in practical scenarios hinders the widespread adoption of pomegranate monitoring technology. To address these challenges, this paper proposes a lightweight pomegranate growth stage detection model, YOLO11-PGM, based on YOLO11n. The model integrates several innovative designs, including a Multi-Scale Edge Enhancement (MSEE) module to mitigate the effects of leaf and fruit occlusion, a Slim Shared Convolutional Head (SSCH) to resolve feature inconsistency across different scales in the feature pyramid, and a High-level Screening Feature Pyramid Network (HSFPN) to replace the standard neck network and achieve a balance between accuracy and complexity. Experimental results demonstrate that YOLO11-PGM achieves an accuracy of 92.3%, a recall of 86.3%, and an mAP50 of 94.0% with only 1.63 M parameters, 4.8 G FLOPs, and a model size of 3.7 MB. It outperforms YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and YOLOv10s. Compared with the baseline YOLO11n, YOLO11-PGM improves the mAP50 by 2.6%, reduces the number of parameters by 36.9%, decreases computational complexity by 23.8%, and shrinks the model size by 32.7%. This model offers an effective solution for intelligent monitoring of pomegranate growth stages and provides valuable theoretical and technical references for orchard yield prediction, growth monitoring, planting management optimization, and the development of automated harvesting systems.