New Perspectives on Enhancing Animal Welfare through Artificial Intelligence Integration in Livestock Production
DOI:
https://doi.org/10.24925/turjaf.v13i12.4308-4314.8142Keywords:
animal welfare, artificial intelligence, machine learning, technological transformation, sustainabilityAbstract
The development and use of artificial intelligence and machine learning-based technologies in animal production systems, along with monitoring animal behavior and assessing health status, significantly contribute to both animal welfare and production efficiency. Artificial intelligence algorithms include advanced techniques such as convolutional neural networks (CNN), support vector machines, decision trees, and ensemble methods. Sensor-based devices, wearable technologies, unmanned aerial vehicles (UAVs), and robotic systems make significant contributions to real-time and objective data collection and analysis. The use of these technologies enables continuous and objective assessment of animal health and welfare by monitoring environmental variables such as temperature, humidity, and ammonia. Furthermore, AI-supported multi-agent systems and information access-supported approaches provide a continuous, fast, and reliable data flow with repeatable accuracy for assessing animal health risks. For all these reasons, the use of AI-based technologies in animal production offers significant contributions to the sustainability of production economics, food security, and improved resource utilization. In this review, artificial intelligence-based technological applications of artificial intelligence methods such as image processing-based machine learning and deep learning that enable fast, high-accuracy and repeatable monitoring of herd projections such as behavior recognition, anomaly detection, animal health and reproductive management in farm animals are examined.
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