Optimised YOLO and MobileNet SSD models for animal intrusion detection in agriculture

Animal intrusion detection is critical for mitigating agricultural losses, yet real-world deployments remain challenging due to high computational demands and suboptimal hyperparameter configurations. This study presents a robust, multi-class detection framework optimised for real-world agricultural environments. The lightweight SSD Lite MobileNetV3 is benchmarked against the state-of-the-art YOLOv11 family (Nano, Small, and Medium) using a comprehensive, field-collected dataset. To overcome the performance bottlenecks with the default configuration, the framework uses Bayesian Optimisation with Hyperband (BOHB) for compute-efficient hyperparameter tuning. Experimental evaluation shows that BOHB enhances the performance. The optimised models achieved an exceptional F1-score of 0.90 for You Only Look Once version 11 medium (YOLO11m), 0.88 for You Only Look Once version 11 (YOLO11), 0.83 for You Only Look Once version 11 nano (YOLO11n), and 0.68 for Single Shot Detector Lite with MobileNetV3 (SSDLite MobileNetV3). The results show that the proposed framework delivers reliable detection, providing a scalable smart-farming solution to safeguard crops and reduce human-wildlife conflict.