用机器学习预测奶牛热应激时躲阴行为，提升牧场管理效率。
Soft Computing Approaches for Predicting Shade-Seeking Behaviour in Dairy Cattle under Heat Stress: A Comparative Study of Random Forests and Neural Networks
- 基于温度湿度指数等特征，用随机森林和神经网络建模奶牛躲阴行为。
- 最优神经网络平均误差14.78，预测峰值时间误差小于一小时。
- 结果可直接用于实时牧场决策，适合关注动物福利的养殖户。
热应激是地中海气候下奶牛面临的主要福利与生产问题。本研究将每日躲阴次数预测视为非线性多变量回归问题，基于西班牙巴伦西亚蒂塔瓜斯商业牧场2023年夏季采集的高分辨率行为与微气候数据（6907次白天观测，5-10分钟分辨率），评估了随机森林与神经网络两种软计算算法的表现。原始数据包含遮荫区牛只数量、环境温度与相对湿度，从中提取当前温湿指数（THI）、日累计THI及夜间均值THI三个特征。通过五折交叉验证评估模型性能。结果表明，两种模型均优于单棵决策树基线。最佳神经网络（3个隐藏层，每层16个神经元，学习率=10⁻³）平均均方根误差（RMSE）为14.78；随机森林（10棵树，深度=5）达14.97，且解释性更优。每日误差分布显示中位数RMSE为13.84，确认预测峰值偏差小于一小时。结果表明，嵌入应用数学特征框架的软计算数据驱动方法，适用于建模噪声生物现象，在热应激条件下具备低成本、实时决策支持价值，可作为精准畜牧管理工具。
原文摘要 · Abstract (English)
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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