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Thermal and humidity environments can dominate the pig growth, health status, and production performance in pig houses, including air temperature, relative humidity, and airflow velocity. The environment can be regulated to consider the interaction mechanism between housing conditions and pig thermal responses. A mechanistic and physiologically interpretable model is required to accurately simulate pig thermal responses under different thermal and humidity conditions. However, existing models of pig thermal response cannot fully meet the requirements of the intelligent control applications. In this study, a pig two-node heat exchange model (PTHM) was established using biological heat balance theory and thermodynamics. Heat exchange was also simulated among the core, the skin layer, and the surrounding environment. Metabolic heat was generated in the core layer and then transferred to the skin via tissue conduction and blood circulation. Part of the heat was dissipated to the environment as sensible respiratory heat loss. The remaining heat was stored within the body, leading to an increase in rectal temperature. Heat in the skin layer was transferred from the core via conductive transfer and blood-mediated convective transport. The heat was then dissipated to the surrounding environment via convective heat exchange and thermal radiation. A small fraction of heat was dissipated after skin evaporation. Environmental parameters were used as the model inputs, while the major physiological parameters were used as the outputs after simulations. A recognition framework of pig drinking behavior was developed using an improved YOLOv11 object detection architecture, particularly for the prediction accuracy and physiological interpretability of the model. A pig drinking detection model (PDDM) was further established to calculate drinking frequency using this framework. The drinking frequency was then introduced into the PTHM as a behavioral correction factor to regulate blood-mediated convective heat transfer and respiratory heat dissipation, thereby constructing a drinking behavior–corrected pig two-node heat exchange model (D-PTHM). A more realistic representation was obtained for the pig thermoregulation. The results showed that the air temperature was the dominant environmental factor on pig thermal physiological responses. The PTHM model also achieved coefficients of determination (R2) of 0.673, 0.685, and 0.615 for rectal temperature, heart rate, and respiratory rate, respectively. The mean absolute errors (MAE) were 0.320 °C, 7.020 bpm, and 0.916 bpm, while the root mean square errors (RMSE) were 0.412 °C, 9.120 bpm, and 1.635 bpm, respectively. A preliminary representation was obtained for the heat transfer pathway from the body core to the skin. Subsequently, the surrounding environment was offered a simplified representation of whole-body heat balance. The DCB-YOLO drinking detection model achieved a mean average precision (mAP) of 97.47%. The PDDM was used to reliably quantify the pig drinking frequency for behavioral correction of the heat exchange model. The prediction accuracy of D-PTHM was significantly improved after drinking behavior was introduced as a correction factor. The D-PTHM achieved higher R2 values of 0.831, 0.771, and 0.775 for the rectal temperature, heart rate, and respiratory rate, respectively. The MAEs were 0.247 °C, 3.358 bpm, and 0.580 bpm, while the RMSEs were 0.332 °C, 4.053 bpm, and 0.747 bpm, indicating the improved model stability and environmental adaptability. The drinking behavior significantly enhanced the mechanistic model to regulate the pig thermal field under different thermal and humidity conditions. This finding can provide a physiologically realistic model for precision environmental control in pig houses. More accurate environmental regulation can be used to improve animal welfare using pig physiological responses in sustainable and efficient livestock production.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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