文献类型： 外文期刊

第一作者： Li, Dapeng

作者： Li, Dapeng;Li, Fuwei;Han, Haixia;Liu, Wei;Li, Dapeng;Li, Fuwei;Han, Haixia;Liu, Wei;Yan, Geqi;Lin, Hai;Jiao, Hongchao

作者机构：

关键词： precision livestock management; animal welfare; thermal comfort; core body temperature; machine learning; optimization algorithm; SHAP value

期刊名称：ANIMALS （ 影响因子：2.7； 五年影响因子：3.2 ）

ISSN： 2076-2615

年卷期： 2024 年 14 卷 18 期

页码：

收录情况： SCI

摘要： Simple Summary In hot weather conditions, ensuring dairy cow comfort and preventing heat stress is crucial. This study applied machine learning techniques to predicting dairy cows' core body temperatures, and improved prediction accuracy through data preprocessing, feature engineering, and hyperparameter optimization. This facilitates timely actions, such as enhancing ventilation or implementing mist cooling, to maintain the health and productivity of the cows. By enhancing the accuracy and interpretability of predictions, the study provides a powerful tool for precision livestock management, contributing to improved animal welfare and enhanced economic farm efficiency.Abstract 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 (R2) value of 0.516, mean absolute error (MAE) of 0.239 degrees C, and root mean square error (RMSE) of 0.302 degrees C. Further analysis shows that the GWO-XGBoost model surpasses others in predictive accuracy with an R2 value of 0.540, RMSE as low as 0.294 degrees C, and MAE of just 0.232 degrees 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.

分类号：