WLP-YOLO: Edge-efficient UAV-based walnut detection for orchard monitoring via lightweight YOLOv8 and structured pruning

UAV-based orchard monitoring can support yield estimation and precision management, yet reliable detection of small and densely distributed fruits remains difficult in complex field scenes. Challenges include limited object pixels, cluttered canopy backgrounds, frequent occlusion, illumination variability, and the need for real-time inference on resource-constrained edge devices. We develop Walnut Lightweight-Pruned YOLO (WLP-YOLO), a task-specific and deployment-oriented detector derived from YOLOv8 for UAV walnut detection under edge-computing constraints. WLP-YOLO combines lightweight feature extraction, efficient multi-scale fusion, hardware-friendly structured channel pruning, and target-device inference to balance small-object detection performance and computational efficiency. On the fixed dataset split, WLP-YOLO increased mAP@0.5 from 0.811 to 0.834 while reducing the model to 2.23 M parameters and 6.8 GFLOPs. Although mAP@0.5:0.95 remained comparable to YOLOv8n (0.323 versus 0.322), small-object AP@0.5 increased from 0.554 to 0.693. Further structured pruning reduced the computational cost to 2.7 GFLOPs, with only a 0.2 percentage-point decrease in mAP@0.5. On the Jetson Xavier NX, the pruned model achieved a per-image latency of 29.1 ms under TensorRT-FP16 inference, corresponding to approximately 34.4 FPS. These results support a practical accuracy–efficiency trade-off for UAV-based walnut monitoring, while transfer to other tasks requires task-specific retraining and cross-domain validation.