Original Articles Open access

Dynamic calibration of broiler apparent temperature using real-time panting rate and machine learning: a pilot study

Authors

  • Yongmin Guo – articles in this journal College of Computer and Information Engineering, Tianjin Agricultural University, China.
  • Yali Ma – articles in this journal College of Computer and Information Engineering, Tianjin Agricultural University; Key Laboratory of Smart Farming, Ministry of Agriculture and Rural Affairs, Tianjin, China.
  • Changxi Chen – articles in this journal College of Computer and Information Engineering, Tianjin Agricultural University; Key Laboratory of Smart Farming, Ministry of Agriculture and Rural Affairs, Tianjin, China.
  • Xiangchao Kong – articles in this journal Corresponding author Email College of Computer and Information Engineering, Tianjin Agricultural University; Key Laboratory of Smart Farming, Ministry of Agriculture and Rural Affairs, Tianjin, China.
  • Deqi Hao – articles in this journal College of Computer and Information Engineering, Tianjin Agricultural University; Key Laboratory of Smart Farming, Ministry of Agriculture and Rural Affairs, Tianjin, China.
  • Sai Luo – articles in this journal College of Computer and Information Engineering, Tianjin Agricultural University; Key Laboratory of Smart Farming, Ministry of Agriculture and Rural Affairs, Tianjin, China.
Published
9 April 2026
Section
Original Articles
DOI
10.4081/jae.2026.2110

Abstract

To address the need for precise thermal environment assessment in intensive broiler farming, this study proposes a dynamic apparent temperature (AT) optimization model that incorporates real-time biological feedback, overcoming the limitations of static traditional AT models. A baseline “AT-P” curve was established using the flock's minute-level panting rate (P) as a core biological feedback indicator. This curve is based on 7,138 records collected from a commercial farm, which include temperature, humidity, air velocity, age, and synchronously captured panting rate data. This curve maps observed panting to an “equivalent AT” representing the actual thermal load, with the deviation from traditional AT defined as the systematic correction residual. An Enhanced Attention Gradient Boosting Machine (EAG) ensemble model is then introduced to dynamically predict this residual, taking multi-source environmental features, traditional AT, and real-time panting rate as inputs, and outputting a corrected, optimized AT. Experimental results demonstrate that the EAG model achieves optimal performance in residual prediction, with an R² of 0.8329 and a mean absolute error (MAE) of 0.6503, significantly outperforming single base models and other mainstream algorithms. By integrating a static physical model with dynamic group behavioral feedback for online self-calibration, this study provides a methodological foundation for developing an animal-centric “perception-response-optimization” intelligent environmental control system in smart farming.

Keywords

Supporting Agencies

National Key Research and Development Program of China, Ministry of Finance and the Ministry of Agriculture and Rural Affairs , Tianjin Agricultural University

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Apalowo OO, Ekunseitan DA, Fasina YO, 2024. Impact of heat stress on broiler chicken production. Poultry 3:107-128. DOI: https://doi.org/10.3390/poultry3020010
  2. Arias RA, Mader TL, Arias RA, 2023. Evaluation of four thermal comfort indices and their relationship with physiological variables in feedlot cattle. Animals (Basel) 13:1169. DOI: https://doi.org/10.3390/ani13071169
  3. Brassó LD, Komlósi I, Várszegi Z, 2025. Modern technologies for improving broiler production and welfare: a review. Animals (Basel) 15:493. DOI: https://doi.org/10.3390/ani15040493
  4. Cardoen T, de Carvalho PS, Antonissen G, Tuyttens FAM, Leroux S, Simoens P, 2025. Multi-camera detection and tracking for individual broiler monitoring. Comput Electron Agric 237:110435. DOI: https://doi.org/10.1016/j.compag.2025.110435
  5. Cheng Q, Wang H, Xu X, He T, Chen Z, 2024. Indoor thermal comfort sector: a review of detection and control methods for thermal environment in livestock buildings. Sustainability 16:1662. DOI: https://doi.org/10.3390/su16041662
  6. de Dear R, Brager GS, 1998. Developing an adaptive model of thermal comfort and preference. UC Berkeley, Center for the Built Environment. Available from: https://escholarship.org/uc/item/4qq2p9c6
  7. do Amaral Vercellino R, de Alencar Nääs I, de Moura DJ, 2025. Tracking heat stress in broilers: a thermographic analysis of anatomical sensitivity across growth stages. Animals (Basel) 15:2233. DOI: https://doi.org/10.3390/ani15152233
  8. Fan W, Peng H, Yang D, 2025. Review: The application and challenges of advanced detection technologies in poultry farming. Poultry Sci 104:105870. DOI: https://doi.org/10.1016/j.psj.2025.105870
  9. Hashemitabar SH, Hosseinian SA, 2024. The comparative effects of probiotics on growth, antioxidant indices and intestinal histomorphology of broilers under heat stress condition. Sci Rep 14:23471. DOI: https://doi.org/10.1038/s41598-024-66301-9
  10. Hernández-Sánchez RC, Martínez-Castañeda FE, Domínguez-Olvera DA, Trujillo-Ortega ME, Díaz-Sánchez VM, Sánchez-Ramírez E, et al., 2024. Systematic review and meta-analysis of thermal stress assessment in poultry using infrared thermography in specific body areas. Animals (Basel) 14:3171. DOI: https://doi.org/10.3390/ani14223171
  11. Italiya J, Ahmed AA, Abdel-Wareth AAA, Lohakare J, 2025. An AI-based system for monitoring laying hen behavior using computer vision for small-scale poultry farms. Agriculture 15:1963. DOI: https://doi.org/10.3390/agriculture15181963
  12. Khongthong S, Piewngam P, Roekngam N, Maliwan P, Kongpuckdee S, Jeenkeawpleam J, et al., 2025. Effects of dietary Bacillus subtilis 14823 on growth performance, gut barrier integrity and inflammatory response of broilers raised in a stressful tropical environment. Poultry Sci 104:104518. DOI: https://doi.org/10.1016/j.psj.2024.104518
  13. Ma W, Wang X, Tulpan D, Yang SX, Li Z, Zhao C, et al., 2025. Intelligent technologies in poultry farming: A review of smart breeding and precision production. Comput Electron Agric 239:111109. DOI: https://doi.org/10.1016/j.compag.2025.111109
  14. Ma Y, Guo Y, Gao B, Zheng P, Chen C, 2025. Development of a tool to detect open-mouthed respiration in caged broilers. Animals (Basel) 15:2732. DOI: https://doi.org/10.3390/ani15182732
  15. Mangan M, Siwek M, 2024. Strategies to combat heat stress in poultry production—A review. J Anim Physiol Anim Nutr 108:576-595. DOI: https://doi.org/10.1111/jpn.13916
  16. Neethirajan S, 2022. Automated tracking systems for the assessment of farmed poultry. Animals (Basel) 12:232. DOI: https://doi.org/10.3390/ani12030232
  17. Olayiwola SF, Adedokun SA, 2025. Heat stress in poultry: The role of nutritional supplements in alleviating heat stress and enhancing gut health in poultry. Front Vet Sci 12:1691532. DOI: https://doi.org/10.3389/fvets.2025.1691532
  18. Oso OM, Mejia-Abaunza N, Bodempudi VUC, Chen X, Chen C, Aggrey SE, et al., 2025. Automatic analysis of high, medium, and low activities of broilers with heat stress operations via image processing and machine learning. Poultry Sci 104:104954. DOI: https://doi.org/10.1016/j.psj.2025.104954
  19. Pečjak Pal M, Leskovec J, Levart A, Pirman T, Salobir J, Rezar V, 2024. Comparison of high n-3 PUFA levels and cyclic heat stress effects on carcass characteristics, meat quality, and oxidative stability of breast meat of broilers fed low- and high-antioxidant diets. Animals (Basel) 14:3314. DOI: https://doi.org/10.3390/ani14223314
  20. Prates JAM, 2025. Heat stress effects on animal health and performance in monogastric livestock: physiological responses, molecular mechanisms, and management interventions. Vet Sci 12:49. DOI: https://doi.org/10.3390/vetsci12050429
  21. Wang X, Chao X, Zhang W, Zhang X, Wu J, Ye C, et al. 2025. Heat stress influences immunity through dusp1 and hspa5 mediated antigen presentation in chickens. Animals (Basel) 15:1141. DOI: https://doi.org/10.3390/ani15081141
  22. Yehia M, Askri A, Achour A, Prus JMA, Ouellet V, Alnahhas N, 2025. Rectal temperature and heat transfer dynamics in the eye, face, and breast of broiler chickens exposed to moderate heat stress. Poultry Sci 104:104748. DOI: https://doi.org/10.1016/j.psj.2024.104748
  23. Zheng P, Zhang W, Gao B, Ma Y, Chen C, 2025. Multi-step apparent temperature prediction in broiler houses using a hybrid SE-TCN–transformer model with Kalman filtering. Sensors (Basel) 25:614. DOI: https://doi.org/10.3390/s25196124

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