Porcupine Detection and Mitigation in Kashmir Valley Orchards Using Edge Computing

Context Roughly 20% to 28% of the GDP of the Kashmiri agricultural sector are based on high‐value crops, such as apples, walnuts, saffron, and apricots. These face significant economic challenges due to crop damage caused by the Indian crested porcupine (Hystrix indica). Objective This research aims to study and develop an effective real‐time management solution for wildlife intrusion in orchards, overcoming the limitations of traditional deterrence methods. Methods : We propose a multilayered edge‐IoT architecture that combines motion, acoustic, and visual sensing with edge‐based machine learning and automated wildlife monitoring. The system leverages lightweight YOLOv8n models running on edge devices, secure communication frameworks, and intelligent monitoring with a user‐facing application for alerts and analytics. A comprehensive wildlife detection dataset was developed by integrating multiple public datasets and converting segmentation annotations into object‐detection bounding boxes. The final dataset contains 1758 images with 2249 annotated instances across three classes: porcupine, hedgehog, and others. Object detection is performed using YOLOv8n on resource‐constrained edge devices for efficient real‐time inference. Results Experimental results show that YOLOv8n achieves 93.9% mAP@0.5 and 86.4% mAP@0.5:0.95 with 84.0% precision and 89.9% recall on the test dataset. The porcupine class specifically achieves 99.5% mAP@0.5 and 95.4% mAP@0.5:0.95, demonstrating highly reliable porcupine detection performance. The lightweight YOLOv8n architecture achieves approximately 4.9 ms inference time per image on the validation host, and 81 ms per frame on a Jetson Nano edge node, enabling efficient edge deployment for real‐time orchard monitoring. All reported figures are obtained from real image data combined with simulated multi‐sensor confidence modeling; no result derives from a deployed orchard installation, and the scope of validation is stated explicitly. Conclusion Furthermore, the proposed framework establishes a foundation for future privacy‐preserving and Agentic AI‐enabled intelligent wildlife monitoring systems capable of adaptive decision‐making, autonomous monitoring, and collaborative edge intelligence under dynamic environmental conditions