Research into and Application of Lightweight Models Based on Model Pruning and Knowledge Distillation in Desert Grassland Plant Recognition

Accurate plant recognition in desert grasslands is essential for ecological monitoring, yet existing models face critical limitations: poor generalization in complex natural environments and excessive computational demands for mobile deployment. This study proposes YOLOv11-PKD, a lightweight model integrating structured pruning and knowledge distillation for efficient desert grassland plant identification. First, we develop YOLOv11-STC, a high-capacity teacher model incorporating the SPPCSPC module for multi-scale feature extraction, Triplet Attention for spatial refinement, and a GSConv-based Slim Neck for optimized feature fusion. This architecture achieves 88.3% mAP50 on the DGPlant48 dataset, outperforming the baseline YOLOv11n by 6.8%. To enable edge deployment, we apply channel pruning guided by BatchNorm scaling factors, compressing the model by 19.75% in PParameters and 20% in GFLOPS (YOLOv11-Pruned: 79.5% mAP50, 4.7 MB). Subsequently, L2-based knowledge distillation recovers performance, yielding YOLOv11-PKD with 87.9% mAP50—approaching teacher-level accuracy—while maintaining 5.0 MB size, 2.150 M parameters, and 5.5 GFLOPS. The model is successfully deployed via a mobile application, achieving ~1 s response times for field-based plant identification. This work demonstrates a practical balance between accuracy and efficiency for resource-constrained ecological monitoring.