Innovative Data-Driven Poultry Heat and Moisture Production Monitoring System

A new study published in Poultry Science magazine has developed an innovative data-driven poultry heat and moisture production (HMP) monitoring system, designed to improve measurement accuracy while reducing operational complexity and costs. The system, developed by researchers from Jiangsu Lihua Food Group Co. Ltd., utilizes a dynamic heat and moisture prediction (DHMP) model to accurately monitor HMP inside a poultry rearing chamber. Experimental data were collected under various heating and humidification power settings and ambient temperature conditions to train and validate the DHMP model. The results demonstrate the system's adaptability to ambient temperature variations and its ability to accurately predict heating and humidification power.

Key Takeaways:

• The indirect calorimetry method, widely used for poultry heat and moisture production measurement, has estimation uncertainties and high construction costs.
• The direct calorimetry method, despite its higher accuracy, is limited by complex equipment design and sensitivity to environmental variations.
• The innovative data-driven poultry HMP monitoring system developed in this study effectively overcomes the limitations of traditional calorimetry methods in terms of complexity and high costs.
• The system's dynamic heat and moisture prediction (DHMP) model was developed and validated using experimental data collected under various heating and humidification power settings and ambient temperature conditions.
• The system demonstrated good adaptability to ambient temperature variations across different heating power conditions.
• The mean absolute percentage errors for heating and humidification power predictions in validation datasets were 3.30% and 3.71%, respectively, with corresponding root mean square error values of 0.961 W and 0.389 g·h¹.
• Field experiments confirmed that the HMP values predicted by the system closely match those reported in the literature, supporting the reliability of the system.
• The total manufacturing cost of the system was reduced by approximately 50-80% compared with existing calorimetry methods.

Statistics:

• Mean absolute percentage errors for heating power prediction: 3.30%
• Mean absolute percentage errors for humidification power prediction: 3.71%
• Root mean square error for heating power prediction: 0.961 W
• Root mean square error for humidification power prediction: 0.389 g·h¹
• Reduction in manufacturing cost: 50-80%

Sources:

• Design and validation of a low-cost data-driven poultry heat and moisture production monitoring system. Poultry Science, 2025;104(11):105889.
• Zhi Zhang, Senzhong Deng, Yang Wang, Baoming Li, and Weichao Zheng. "Studies from Zhi Zhang et al Provide New Data on Poultry Farming (Design and validation of a low-cost data-driven poultry heat and moisture production monitoring system)." Computer Weekly News. October 15, 2025; p 661.