AI-driven wild animal detection and tracking in agriculture fields using deep learning

The common area of agricultural activities and wildlife habitats presents several implications, one of the most pressing being the intrusion of wild animals into crop fields. To address the continued and growing challenge of discovering methods for the successful mitigation and protection of crops, a new method must be introduced. Current practices, such as using pheromones and monitoring by humans and trained dogs, yield suboptimal results due to inefficiency, labor-intensive processes, and environmental damage. This paper proposed a novel method based on deep learning technologies, namely, Unet and EfficientNet-B7 architectures, for a semantic segmentation of a wild boar in an agricultural habitat. The method provides a reliable means to detect animal intrusion by integrating it into automated monitoring operations. Whether identifying the segmentation point of a wild boar or utilizing feature-based detection with the proposed model, the application of advanced convolutional neural networks ensures the accuracy of these systems. This method demonstrates higher performance in wild boar identification and proposes newer and ecologically friendly methods of implementing non-invasive monitoring for wildlife conservation alongside agriculture.