Soft Computing Approaches for Predicting Shade-Seeking Behaviour in Dairy Cattle under Heat Stress: A Comparative Study of Random Forests and Neural Networks

• URL: http://arxiv.org/abs/2501.05494v2
• Date: Tue, 22 Jul 2025 14:50:26 GMT
• Title: Soft Computing Approaches for Predicting Shade-Seeking Behaviour in Dairy Cattle under Heat Stress: A Comparative Study of Random Forests and Neural Networks
• Authors: S. Sanjuan, D. A. Méndez, R. Arnau, J. M. Calabuig, X. Díaz de Otálora Aguirre, F. Estellés,
• Abstract summary: Heat stress is one of the main welfare and productivity problems faced by dairy cattle in Mediterranean climates.<n>We evaluate two soft computing algorithms -- Random Forests and Neural Networks -- trained on high-resolution behavioral and micro-climatic data.<n>The best Neural Network (3 hidden layers, 16 neurons each, learning rate = 10e-3) reaches an average RMSE of 14.78, while a Random Forest (10 trees, depth = 5) achieves 14.97 and offers best interpretability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heat stress is one of the main welfare and productivity problems faced by dairy cattle in Mediterranean climates. In this study, we approach the prediction of the daily shade-seeking count as a non-linear multivariate regression problem and evaluate two soft computing algorithms -- Random Forests and Neural Networks -- trained on high-resolution behavioral and micro-climatic data collected in a commercial farm in Titaguas (Valencia, Spain) during the 2023 summer season. The raw dataset (6907 daytime observations, 5-10 min resolution) includes the number of cows in the shade, ambient temperature and relative humidity. From these we derive three features: current Temperature--Humidity Index (THI), accumulated daytime THI, and mean night-time THI. To evaluate the models' performance a 5-fold cross-validation is also used. Results show that both soft computing models outperform a single Decision Tree baseline. The best Neural Network (3 hidden layers, 16 neurons each, learning rate = 10e-3) reaches an average RMSE of 14.78, while a Random Forest (10 trees, depth = 5) achieves 14.97 and offers best interpretability. Daily error distributions reveal a median RMSE of 13.84 and confirm that predictions deviate less than one hour from observed shade-seeking peaks. These results demonstrate the suitability of soft computing, data-driven approaches embedded in an applied-mathematical feature framework for modeling noisy biological phenomena, demonstrating their value as low-cost, real-time decision-support tools for precision livestock farming under heat-stress conditions.

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