An Internet of Things Based Early Warning System for Monitoring Environmental Conditions and Health Risks in Nigerian Poultry Houses

Poultry houses in Nigeria operate in a climate that regularly carries birds beyond their comfort range, and on
most small and medium farms this is discovered only after the damage is done. This paper presents the design,
implementation and evaluation of an Internet of Things based early warning system that monitors temperature,
relative humidity and ammonia continuously at bird level and warns the farmer before conditions become harmful.
Sensing nodes built around an ESP32 microcontroller, a DHT22 and an MQ-137 sample every 120 s and transmit
over Wi-Fi to a web application developed in Laravel, holding readings in flash whenever the uplink fails.
Detection is arranged in three layers: an age-banded comfort envelope, a baseline learned separately for each
house and each hour of the day, and a least-squares trend projected 45 minutes ahead, the last of which states
the time remaining before a limit is reached. Over a 30-day evaluation of three instrumented pens carrying 4,700
birds, 64,800 readings were processed and 82 critical environmental episodes occurred. Node readings agreed
closely with calibrated reference instruments, with a mean bias of +0.157 °C for temperature and -0.063 ppm for
ammonia and 95 per cent limits of agreement of -0.47 to +0.78 °C. The three-layer system warned before the
critical limit was crossed in 98.8 per cent of episodes with a median lead time of 89 minutes, against 86.6 per cent
and 22 minutes for conventional threshold-only monitoring and no advance warning at all from inspection three
times a day; the median gain over threshold-only monitoring was 57 minutes (Wilcoxon signed-rank, p < 0.001).
Gating warnings against the learned baseline removed 50 per cent of the messages a bare threshold rule would
have sent, and every reading survived interruptions that left the uplink unavailable for 20.7 per cent of the period.
The work shows that affordable early warning is achievable under Nigerian conditions of intermittent power and
weak connectivity.

Anosike, F. U., Rekwot, G. Z., Owoshagba, O. B., Ahmed, S., & Atiku, J. A. (2020). Challenges of poultry
production in Nigeria: A review. Nigerian Journal of Animal Production, 45(1), 252–258.
https://doi.org/10.51791/njap.v45i1.335
Astill, J., Dara, R. A., Fraser, E. D. G., Roberts, B., & Sharif, S. (2020). Smart poultry management: Smart sensors,
big data, and the Internet of Things. Computers and Electronics in Agriculture, 170, Article 105291.
https://doi.org/10.1016/j.compag.2020.105291
Beker, A., Vanhooser, S. L., Swartzlander, J. H., & Teeter, R. G. (2004). Atmospheric ammonia concentration
effects on broiler growth and performance. Journal of Applied Poultry Research, 13(1), 5–9.
https://doi.org/10.1093/japr/13.1.5
Berckmans, D. (2017). General introduction to precision livestock farming. Animal Frontiers, 7(1), 6–11.
https://doi.org/10.2527/af.2017.0102
Bist, R. B., Subedi, S., Chai, L., & Yang, X. (2023). Ammonia emissions, impacts, and mitigation strategies for
poultry production: A critical review. Journal of Environmental Management, 328, Article 116919.
https://doi.org/10.1016/j.jenvman.2022.116919
Cassuce, D. C., Tinôco, I. D. F. F., Baêta, F. C., Zolnier, S., Cecon, P. R., & Vieira, M. D. F. A. (2013). Thermal
comfort temperature update for broiler chickens up to 21 days of age. Engenharia Agrícola, 33(1), 28–36.
https://doi.org/10.1590/S0100-69162013000100004
Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., & Bartonova, A. (2017).
Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?
Environment International, 99, 293–302. https://doi.org/10.1016/j.envint.2016.12.007
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection. ACM Computing Surveys, 41(3), 1–58.
https://doi.org/10.1145/1541880.1541882
Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score
and accuracy in binary classification evaluation. BMC Genomics, 21, Article 6.
https://doi.org/10.1186/s12864-019-6413-7
Chigwada, J., Mazunga, F., Nyamhere, C., Mazheke, V., & Taruvinga, N. (2022). Remote poultry management
system for small to medium scale producers using IoT. Scientific African, 18, Article e01398.
https://doi.org/10.1016/j.sciaf.2022.e01398
David, B., Moe, R., Michel, V., Lund, V., & Mejdell, C. (2015). Air quality in alternative housing systems may
have
an impact on laying hen welfare. Part I—Dust. Animals, 5(3), 495–511.
https://doi.org/10.3390/ani5030368
Gbadamosi, A. S. (2020). Cloud-based IoT monitoring system for poultry farming in Nigeria. Arid Zone Journal
of Engineering, Technology and Environment, 16(1), 100–108.
Giavarina, D. (2015). Understanding Bland–Altman analysis. Biochemia Medica, 25(2), 141–151.
https://doi.org/10.11613/BM.2015.015
Purswell, J. L., Dozier, W. A., Olanrewaju, H. A., Davis, J. D., Xin, H., & Gates, R. S. (2012). Effect of
temperature-humidity index on live performance in broiler chickens grown from 49 to 63 days of age. 2012
IX
International
Livestock Environment Symposium (ILES IX), Paper ILES12–0265.
https://doi.org/10.13031/2013.41619
Kashiha, M., Pluk, A., Bahr, C., Vranken, E., & Berckmans, D. (2013). Development of an early warning system
for a broiler house using computer vision. Biosystems Engineering, 116(1), 36–45.
https://doi.org/10.1016/j.biosystemseng.2013.06.004
Lara, L., & Rostagno, M. (2013). Impact of heat stress on poultry production. Animals, 3(2), 356–369.
https://doi.org/10.3390/ani3020356
Lin, L. I. K. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45(1), 255
268. https://doi.org/10.2307/2532051
Marai, I. F. M., Habeeb, A. A. M., & Gad, A. E. (2002). Rabbits’ productive, reproductive and physiological
performance traits as affected by heat stress: A review. Livestock Production Science, 78(2), 71–90.
https://doi.org/10.1016/S0301-6226(02)00091-X
Bland, J. M., & Altman, D. (1986). Statistical methods for assessing agreement between two methods of clinical
measurement. The Lancet, 327(8476), 307–310. https://doi.org/10.1016/S0140-6736(86)90837-8
Matthews, B. W. (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme.
Biochimica et Biophysica Acta (BBA) - Protein Structure, 405(2), 442–451. https://doi.org/10.1016/0005
2795(75)90109-9
Miles, D. M., Branton, S. L., & Lott, B. D. (2004). Atmospheric ammonia is detrimental to the performance of
modern commercial broilers. Poultry Science, 83(10), 1650–1654. https://doi.org/10.1093/ps/83.10.1650
global
Nawab, A., Ibtisham, F., Li, G., Kieser, B., Wu, J., Liu, W., Zhao, Y., Nawab, Y., Li, K., Xiao, M., & An, L.
(2018). Heat stress in poultry production: Mitigation strategies to overcome the future challenges facing
the
poultry
industry.
Journal
https://doi.org/10.1016/j.jtherbio.2018.08.010
of
Thermal
Biology,
78,
131–139.
Norton, T., Chen, C., Larsen, M. L. V., & Berckmans, D. (2019). Precision livestock farming: Building “digital
representations” to bring the animals closer to the farmer. Animal, 13(12), 3009–3017.
https://doi.org/10.1017/S175173111900199X
Ojo, R. O., Ajayi, A. O., Owolabi, H. A., Oyedele, L. O., & Akanbi, L. A. (2022). Internet of Things and machine
learning techniques in poultry health and welfare management: A systematic literature review. Computers
and Electronics in Agriculture, 200, Article 107266. https://doi.org/10.1016/j.compag.2022.107266
Okinda, C., Nyalala, I., Korohou, T., Wang, J., Achieng, T., Wamalwa, P., Mang, T., & Shen, M. (2020). A review
on computer vision systems in monitoring of poultry: A welfare perspective. Artificial Intelligence in
Agriculture, 4, 184–208. https://doi.org/10.1016/j.aiia.2020.09.002
Orakwue, S. I., Al-Khafaji, H. M. R., & Chabuk, M. Z. (2022). IoT based smart monitoring system for efficient
poultry farming. Webology, 19(1), 4105–4112. https://doi.org/10.14704/web/v19i1/web19270
Popoola, I. O., Oshibanjo, D. O., Popoola, O. R., & Okuneye, T. A. (2019). Effect of dietary electrolyte balance
on water intake, litter moisture and production of broiler chickens. Open Journal of Animal Sciences, 9(4),
472–480. https://doi.org/10.4236/ojas.2019.94036
Rai, A. C., Kumar, P., Pilla, F., Skouloudis, A. N., Di Sabatino, S., Ratti, C., Yasar, A., & Rickerby, D. (2017).
End-user perspective of low-cost sensors for outdoor air pollution monitoring. Science of the Total
Environment, 607–608, 691–705. https://doi.org/10.1016/j.scitotenv.2017.06.266
Ritz, C. W., Fairchild, B. D., & Lacy, M. P. (2004). Implications of ammonia production and emissions from
commercial poultry facilities: A review. Journal of Applied Poultry Research, 13(4), 684–692.
https://doi.org/10.1093/japr/13.4.684
Sonaiya, E. B. (2007). Family poultry, food security and the impact of HPAI. World’s Poultry Science Journal,
63(1), 132–138. https://doi.org/10.1017/S0043933907001353
Tao, X., & Xin, H. (2003). Acute synergistic effects of air temperature, humidity, and velocity on homeostasis of
market-size broilers. Transactions of the ASAE, 46(2), 491–497. https://doi.org/10.13031/2013.12971
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83.
https://doi.org/10.2307/3001968