Treffer: Application of Artificial Intelligence Technologies in Livestock Management.

Artificial Intelligence (AI) has become a transformative technology in livestock management within the evolving framework of precision agriculture. The integration of AI methods--including supervised and unsupervised machine learning, deep learning, smart sensor networks, and real-time analytics--enables data-driven, timely, and efficient decisions that enhance animal health, welfare, and productivity. AI systems reduce human error, lower labor costs, and automate complex biological and environmental analyses. Key applications include behavior monitoring through accelerometers and vision-based systems, early disease detection via biometric patterns, estrus prediction using movement and vocal cues, and personalized feeding strategies through predictive algorithms. AI also enables biometric identification of animals through facial and vocal recognition, improving traceability and welfare without invasive tagging. This study presents a comprehensive analysis of major AI subfields--Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANN), Computer Vision (CV), Robotics, and Natural Language Processing (NLP)--and their applications in livestock farming through empirical research and quantitative models. Special emphasis is placed on convolutional neural networks for diagnostics, reinforcement learning in feeding systems, and sensor fusion for behavior recognition. A practical Python-based simulation is introduced, utilizing a Multilayer Perceptron (MLP) neural network to predict daily milk yield from synthetic biometric data (heart rate, respiration rate, body and eye temperature) of 100 dairy cows. Model performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² metrics, demonstrating potential for real-time prediction in farm operations. AI technologies contribute to Agriculture 4.0 by promoting sustainability, automation, and data-centric decision-making, reshaping livestock farming into a more resilient, efficient, and welfare-oriented system. [ABSTRACT FROM AUTHOR]

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