Li Dapeng, Yan Geqi, Li Fuwei, Lin Hai, Jiao Hongchao, Han Haixia, Liu Wei
Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China.
Shandong Provincial Key Laboratory of Livestock and Poultry Breeding, Jinan 250100, China.
Animals (Basel). 2024 Sep 20;14(18):2724. doi: 10.3390/ani14182724.
Heat stress poses a significant challenge to livestock farming, particularly affecting the health and productivity of high-yield dairy cows. This study develops a machine learning framework aimed at predicting the core body temperature (CBT) of dairy cows to enable more effective heat stress management and enhance animal welfare. The dataset includes 3005 records of physiological data from real-world production environments, encompassing environmental parameters, individual animal characteristics, and infrared temperature measurements. Employed machine learning algorithms include elastic net (EN), artificial neural networks (ANN), random forests (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and CatBoost, alongside several optimization algorithms such as Bayesian optimization (BO) and grey wolf optimizer (GWO) to refine model performance through hyperparameter tuning. Comparative analysis of various feature sets reveals that the feature set incorporating the average infrared temperature of the trunk (IRTave_TK) excels in CBT prediction, achieving a coefficient of determination (R) value of 0.516, mean absolute error (MAE) of 0.239 °C, and root mean square error (RMSE) of 0.302 °C. Further analysis shows that the GWO-XGBoost model surpasses others in predictive accuracy with an R value of 0.540, RMSE as low as 0.294 °C, and MAE of just 0.232 °C, and leads in computational efficiency with an optimization time of merely 2.41 s-approximately 4500 times faster than the highest accuracy model. Through SHAP (SHapley Additive exPlanations) analysis, IRTave_TK, time zone (TZ), days in lactation (DOL), and body posture (BP) are identified as the four most critical factors in predicting CBT, and the interaction effects of IRTave_TK with other features such as body posture and time periods are unveiled. This study provides technological support for livestock management, facilitating the development and optimization of predictive models to implement timely and effective interventions, thereby maintaining the health and productivity of dairy cows.
热应激对畜牧业构成了重大挑战，尤其影响高产奶牛的健康和生产力。本研究开发了一个机器学习框架，旨在预测奶牛的核心体温（CBT），以实现更有效的热应激管理并提高动物福利。该数据集包含来自实际生产环境的3005条生理数据记录，涵盖环境参数、个体动物特征和红外温度测量值。使用的机器学习算法包括弹性网络（EN）、人工神经网络（ANN）、随机森林（RF）、极端梯度提升（XGBoost）、轻量级梯度提升机（LightGBM）和CatBoost，以及一些优化算法，如贝叶斯优化（BO）和灰狼优化器（GWO），通过超参数调整来优化模型性能。对各种特征集的比较分析表明，包含躯干平均红外温度（IRTave_TK）的特征集在CBT预测方面表现出色，决定系数（R）值达到0.516，平均绝对误差（MAE）为0.239℃，均方根误差（RMSE）为0.302℃。进一步分析表明，GWO-XGBoost模型在预测准确性方面优于其他模型，R值为0.540，RMSE低至0.294℃，MAE仅为0.232℃，并且在计算效率方面领先，优化时间仅为2.41秒，比最高精度模型快约4500倍。通过SHAP（SHapley Additive exPlanations）分析，IRTave_TK、时区（TZ）、泌乳天数（DOL）和身体姿势（BP）被确定为预测CBT的四个最关键因素，并揭示了IRTave_TK与其他特征（如身体姿势和时间段）之间的相互作用效应。本研究为牲畜管理提供了技术支持，有助于预测模型的开发和优化，以实施及时有效的干预措施，从而维持奶牛的健康和生产力。
