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AI-Powered Cow Detection in Complex Farm Environments

Animal welfare has become a critical issue in contemporary society, emphasizing our ethical responsibilities toward animals, particularly within livestock farming. The advent of Artificial Intelligence (AI) technologies, specifically computer vision, offers an innovative approach to monitoring and enhancing animal welfare. Cows, as essential contributors to sustainable agriculture, are central to this effort. However, existing cow detection algorithms face challenges in real-world farming environments, such as complex lighting, occlusions, pose variations, and background interference, hindering detection. Model generalization is crucial for adaptation across contexts beyond the training dataset. This study addresses these challenges using a diverse cow dataset from six environments, including indoor and outdoor scenarios. We propose a detection model combining YOLOv8 with the CBAM (Convolutional Block Attention Module) and assess its performance against baseline models, including Mask R-CNN, YOLOv5, and YOLOv8. Our findings show baseline models degrade in complex conditions, while our approach improves using CBAM. YOLOv8-CBAM outperformed YOLOv8 by 2.3% in mAP, achieving 95.2% precision and an mAP@0.5:0.95 of 82.6%, demonstrating superior accuracy. Contributions include (1) analyzing detection limitations, (2) proposing a robust model, and (3) benchmarking state-of-the-art algorithms. Applications include health monitoring, behavioral analysis, and tracking in smart farms, enabling precise detection in challenging settings. This study advances AI-driven livestock monitoring, improving animal welfare and smart agriculture.

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