*Result*: Wildlife Monitoring Using YOLOv8 and Edge-AI: A Real-Time Approach.

*Human-wildlife conflict (HWC) in biodiversity hotspots like Wayanad demands real-time, intelligent solutions that work reliably under field constraints. This paper presents an end-to-end wildlife detection pipeline using YOLOv8, built from the ground up with a hybrid dataset of over 15,000 images and 35 h of field video, covering species like elephants, leopards, and gaurs. The system was trained and optimized on NVIDIA A100 GPUs and deployed on edge devices like Jetson Nano and Xavier NX, achieving up to 93.9% mean average precision (mAP) @0.5 and 24.7 FPS in real-time conditions. Field-tested across multiple terrains and lighting conditions, the system sustained solar-powered operation, delivered alerts with sub-500 ms latency, and maintained a false positive rate under 8%. A comparative analysis with YOLOv4 and YOLOv5 confirmed YOLOv8's superior precision, efficiency, and edge-readiness. The study marks a shift from lab-bench AI to boots-onground deployment, showcasing how intelligent vision systems can enable proactive conservation and human safety in high-risk ecological zones. [ABSTRACT FROM AUTHOR]

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