From Sensor to Strategy: Leveraging IoT and Real-Time Data Analytics for Poultry Disease Management

References

[1] Abdennabi Morchid, Rachid El Alami, Aeshah A. Raezah, Yassine Sabbar. (2024). Applications of internet of things (IoT) and sensors technology to increase food security and agricultural Sustainability: Benefits and challenges. Ain Shams Engineering Journal, Volume 15, Issue 3, 102509, ISSN 2090-4479. https://doi.org/10.1016/j.asej.2023.102509.

[2] Adha, Fatria & Md Johar, Md Gapar & Alkawaz, Mohammed & Hajamydeen, Asif Iqbal & Raya, Lilysuriazna. (2022). IoT based Conceptual Framework for Monitoring Poultry Farms. 277-282. 10.1109/ISCAIE54458.2022.9794471.

[3] Ali, Ahmad & Al-Zu'bi, Maha. (2023). Application of Artificial Intelligence in Monitoring of Animal Health and Welfare. Indian Journal of Animal Research. 10.18805/IJAR.BF-1698.

[4] Ambafi, J. G., Muhammad, . Y. H., Ahmad, . A. S., Ohize, . H. O., Dauda, . U. S. & Musa, . J. J. (0) (2025). A Review on Development of an IoT-Based System for Enhancing Yield in Poultry Farming. Confluence University Journal of Science and Technology, Online First: 27 Jun, 2025.

[5] Amenu K, McIntyre KM, Moje N, Knight-Jones T, Rushton J and Grace D (2023) Approaches for disease prioritization and decision-making in animal health, 2000–2021: a structured scoping review. Front. Vet. Sci. 10:1231711. doi: 10.3389/fvets.2023.1231711

[6] Amiri-Zarandi, M., Dara, R. A., Duncan, E., & Fraser, E. D. G. (2022). Big Data Privacy in Smart Farming: A Review. Sustainability, 14(15), 9120. https://doi.org/10.3390/su14159120

[7] Ana, Beatriz. (2023). Optimizing Lightweight Security Protocols for IoT Devices in Low- Power and Low-Bandwidth Environments: Challenges and Solutions.

[8] Arthi Amalraj, Hilde Van Meirhaeghe, Nele Caekebeke, Rhea Creve et al. (2024). Development and use of Biocheck.UGent™ scoring system to quantify biosecurity in conventional indoor (turkey, duck, breeder) and free-range (layer and broiler) poultry farms. Preventive Veterinary Medicine, Volume 230, 106288, ISSN 0167-5877. https://doi.org/10.1016/j.prevetmed.2024.106288.

[9] Atapattu, Anjana J. & Perera, Lalith & Nuwarapaksha, Tharindu & Udumann, Shashi S. & Dissanayaka, Nuwandhya. (2024). Challenges in Achieving Artificial Intelligence in Agriculture. 10.1007/978-981-97-5878-4_2.

[10] Aulia Fathurohman, Muhammad Aldi & Sumitra, Irfan & Daud, Andre. (2023). Integration of Wireless Sensor Network and IoT for Enhanced Broiler Closed-House Monitoring: A Case Study at Broiler Teaching Farm. 1-8. 10.1109/ICSPIS59665.2023.10402746.

[11] Awan, Wajahat & Ud Din, Ikram & Almogren, Ahmad & Rodrigues, Joel. (2024). Poultry Health Monitoring With Advanced Imaging: Toward Next-Generation Agricultural Applications in Consumer Electronics. IEEE Transactions on Consumer Electronics. PP. 1-1. 10.1109/TCE.2024.3409069.

[12] Badreddine, Miles & Bourennane, El-Bay & Boucherkha, Samia & Chikhi, Salim. (2020). A study of LoRaWAN protocol performance for IoT applications in smart agriculture. Computer Communications. 164. 148-157. 10.1016/j.comcom.2020.10.009.

[13] Banjoko, I. & Adedotun, K. & Raji, A.. (2025). IOT-Driven Health Monitoring Systems For Broiler And Noiler Chickens: A Smart Precision Farming Approach. International Journal of Agricultural and Veterinary Science. 10.70382/mejavs.v8i1.027.

[14] Bartolín-Arnau, Luis & Todoli-Ferrandis, David & Sempere-Payá, Víctor & Silvestre-Blanes, Javier & Santonja-Climent, Salvador. (2022). LoRaWAN Networks for Smart Applications in Rural Settings. IETE Technical Review. 40. 1-13. 10.1080/02564602.2022.2121773.

[15] Bindushree G T and Sreedevi M T. (2020). Review on poultry automation using IoT and machine learning. World Journal of Advanced Research and Reviews, 08(03), 466–474 P Article DOI: https://doi.org/10.30574/wjarr.2020.8.3.0371

[16] Chambers, John & Iseoluwa, Tunde & Teslim, Badrudeen. (2024). The Role of Big Data in Predicting Animal Disease Outbreaks.

[17] Chrysanthos Maraveas, Muttukrishnan Rajarajan, Konstantinos G Arvanitis, Anna Vatsanidou. (2024). Cybersecurity threats and mitigation measures in agriculture 4.0 and 5.0. Smart Agricultural Technology, Volume 9, 100616, ISSN 2772-3755. https://doi.org/10.1016/j.atech.2024.100616.

[18] Chukwurah, Naomi & Ige, Adebimpe & Adebayo, Victor & Eyieyien, Osemeike. (2024). Frameworks for effective data governance: best practices, challenges, and implementation strategies across industries. Computer Science & IT Research Journal. 5. 1666-1679. 10.51594/csitrj.v5i7.1351.

[19] Cruz, E., Hidalgo-Rodriguez, M., Acosta-Reyes, A. M., Rangel, J. C., & Boniche, K. (2024). AI-Based Monitoring for Enhanced Poultry Flock Management. Agriculture, 14(12), 2187. https://doi.org/10.3390/agriculture14122187

[20] Dennis Junior Choruma, Tinashe Lindel Dirwai, Munyaradzi Junia Mutenje, Maysoun Mustafa, et al. (2024). Digitalisation in agriculture: A scoping review of technologies in practice, challenges, and opportunities for smallholder farmers in sub-saharan africa. Journal of Agriculture and Food Research, Volume 18, 101286, ISSN 2666-1543. https://doi.org/10.1016/j.jafr.2024.101286.

[21] FAO. (2021). Farm data management, sharing and services for agriculture development. Rome. https://doi.org/10.4060/ cb2840en

[22] Felician Andrew Kitole. (2025). Determinants of veterinary service adoption and its impact on livestock mortality in Tanzania. Preventive Veterinary Medicine, Volume 242, 106570, ISSN 0167-5877. https://doi.org/10.1016/j.prevetmed.2025.106570.

[23] Finistrosa, R., Mañoso, C., de Madrid, Á. P., & Romero, M. (2025). Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms. Applied Sciences, 15(9), 4856. https://doi.org/10.3390/app15094856

[24] Ganapathi, Padmavathi & D, Shanmugapriya & Arumugam, Roshni. (2022). Evaluation of Principal Component Analysis Variants to Assess Their Suitability for Mobile Malware Detection. 10.5772/intechopen.105418

[25] Godinho, A., Vicente, R., Silva, S., & Coelho, P. J. (2025). Wireless Environmental Monitoring and Control in Poultry Houses: A Conceptual Study. IoT, 6(2), 32. https://doi.org/10.3390/iot6020032

[26] Hassan A Aidaros, Eman M. Hafez, Halla E.K. El Bahgy. (2022). The effect of biosecurity measures application on the level of diseases risk in different poultry farms Benha University. Faculty of Veterinary Medicine Research. DOI: https://doi.org/10.21203/rs.3.rs-1544511/v1

[27] Hassan, Emad & Abdelaal, Ayman & Oshaba, Ahmed & El-Emary, Atef & Dessouky, M.I. & Abd El-Samie, Fathi. (2024). Optimizing bandwidth utilization and traffic control in ISP networks for enhanced smart agriculture. PLOS ONE. 19. e0300650. 10.1371/journal.pone.0300650.

[28] He, P., Chen, Z., Yu, H., Hayat, K., He, Y., Pan, J., & Lin, H. (2022). Research Progress in the Early Warning of Chicken Diseases by Monitoring Clinical Symptoms. Applied Sciences, 12(11), 5601. https://doi.org/10.3390/app12115601

[29] Helen Schomburg, Julia Malchow, Oliver Sanders, Jonas Knöll, Lars Schrader. (2023). Elevated platforms with integrated weighing beams allow automatic monitoring of usage and activity in broiler chickens. Smart Agricultural Technology, Volume 3, 100095, ISSN 2772-3755. https://doi.org/10.1016/j.atech.2022.100095.

[30] Hossain, Md. Shafkat & Pramanik, Sourav & Rahman, Atikur & Ali, Shahin & Islam, Shekh Md. (2023). Non‐contact vital signs monitoring in broiler chickens. The Journal of Engineering. 2023. 10.1049/tje2.12320.

[31] Himel, G.M.S., Islam, M.M. (2025). A Smart Intelligence System for Hen Breed and Disease classification using Extra Tree classifier-based Ensemble Technique. Journal of Electrical Systems and Inf Technol 12, 2 (2025). https://doi.org/10.1186/s43067-025-00191-3

[32] İbrahim Yazici, Ibraheem Shayea, Jafri Din. (2023). A survey of applications of artificial intelligence and machine learning in future mobile networks-enabled systems. Engineering Science and Technology, an International Journal, Volume 44, 101455, ISSN 2215-0986. https://doi.org/10.1016/j.jestch.2023.101455

[33] IBM. (2023). Data integration. IBM Think. Retrieved July 23, 2025, from https://www.ibm.com/think/topics/data-integration

[34] IBM. (2024). Data ingestion. IBM Think. Retrieved July 23, 2025, from https://www.ibm.com/think/topics/data-ingestion

[35] Jasmin Kaur, Seyed Mehdi Hazrati Fard, Mohammad Amiri-Zarandi, Rozita Dara (2022). Protecting farmers' data privacy and confidentiality: Recommendations and considerations. POLICY AND PRACTICE REVIEWS article. Front. Sustain. Food Syst. Sec. Social Movements, Institutions and Governance, Volume 6 - 2022 | https://doi.org/10.3389/fsufs.2022.903230

[36] Jason Page. (2025). Data integration: Google BigQuery. Retrieved from Apix-Drive July 23, 2025, from https://apix-drive.com/en/blog/other/data-integration-google-bigquery

[37] Jebari, Hakim & Hayani Mechkouri, Meriem & Rekiek, Siham & Reklaoui, Kamal. (2023). Poultry-Edge-AI-IoT System for Real-Time Monitoring and Predicting by Using Artificial Intelligence. International Journal of Interactive Mobile Technologies (iJIM). 17. 149-170. 10.3991/ijim.v17i12.38095.

[38] Jin-Yong Noh, Kyu-Jik Kim, Sun-Hak Lee, Jun-Beom Kim, Deok-Hwan Kim, Sungsu Youk, Chang-Seon Song, Sang-Soep Nahm. (2021). Thermal Image Scanning for the Early Detection of Fever Induced by Highly Pathogenic Avian Influenza Virus Infection in Chickens and Ducks and Its Application in Farms. ORIGINAL RESEARCH article. Front. Vet. Sci. Sec. Veterinary Infectious Diseases, Volume 8 https://doi.org/10.3389/fvets.2021.616755

[39] Jung, M., Jang, H., Kwon, W., Seo, J., Park, S., Park, B., Park, J., Yu, D., & Lee, S. (2025). A Comparative Study of Customized Algorithms for Anomaly Detection in Industry-Specific Power Data. Energies, 18(14), 3720. https://doi.org/10.3390/en18143720

[40] IoT Now. (2024). How IoT is revolutionising livestock management. Retrieved July 22, 2025, from https://www.iot-now.com/2024/09/23/146493-how-iot-is-revolutionising-livestock-management

[41] Kamarul Aryffin, Hazeeqah Amny & Sahbudin, Murtadha Arif Bin & Ali Pitchay, Sakinah & Halim, Azni & Sahbudin, Ilfita. (2025). Technological trends in epidemic intelligence for infectious disease surveillance: a systematic literature review. PeerJ Computer Science. 11. 10.7717/peerj-cs.2874.

[42] Kanban Board. (n.d.). Manufacturing KPI dashboard. Retrieved July 24, 2025, from https://kanbanboard.co.uk/manufacturing-kpi-dashboard

[43] Kelly, Brian. (2024). The Impact of Edge Computing on Real-Time Data Processing. International Journal of Computing and Engineering. 5. 44-58. 10.47941/ijce.2042.

[44] Kobi, Joseph. (2024). Developing Dashboard Analytics and Visualization Tools for Effective Performance Management and Continuous Process Improvement. International Journal of Innovative Science and Research Technology (IJISRT). 9. 10.38124/ijisrt/IJISRT24MAY1147.

[45] Li N., Z. Ren, D. Li, L. Zeng. (2020). Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal, Volume 14, Issue 3, Pages 617-625, ISSN 1751-7311. https://doi.org/10.1017/S1751731119002155.

[46] Lin SY, Beltran-Alcrudo D, Awada L, Hamilton-West C, Lavarello Schettini A, Cáceres P, Tizzani P, Allepuz A, Casal J. (2023). Analysing WAHIS Animal Health Immediate Notifications to Understand Global Reporting Trends and Measure Early Warning Capacities (2005-2021). Transbound Emerg Dis. 2023 May 9;2023:6666672. doi: 10.1155/2023/6666672.

[47] Lingxi Cui, Huan Li, Ke Chen, Lidan Shou, And Gang Chen, The State Key Laboratory of Blockchain and Data Security, Zhejiang University, China. (2024). Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI An Extended Version https://arxiv.org/pdf/2407.21523

[48] Machuve, Dina & Nwankwo, Ezinne & Mduma, Neema & Mbelwa, Jimmy. (2022). Poultry diseases diagnostics models using deep learning. Frontiers in Artificial Intelligence. 5. 10.3389/frai.2022.733345.

[49] Manikandan, V., & Neethirajan, S. (2025). Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic Monitoring. Sensors, 25(9), 2912. https://doi.org/10.3390/s25092912

[50] Mărcuță, C., & MoldStud Research Team. (2024). Implementing remote monitoring systems for livestock - revolutionizing agriculture. MoldStud. Retrieved July 21, 2025, from https://moldstud.com/articles/p-implementing-remote-monitoring-systems-for-livestock-in-agriculture

[51] Maria Teresa Della Mura. (2024). IoT and AI in poultry farming. Retrieved July 24, 2025, from Tech4Future https://tech4future.info/en/iot-ai-poultry-farming

[52] Mattias Delpont, Luis G. Salazar, Jeroen Dewulf, et al., (2023). Monitoring biosecurity in poultry production: an overview of databases reporting biosecurity compliance from seven European countries. ORIGINAL RESEARCH article. Front. Vet. Sci., Sec. Veterinary Epidemiology and Economics, Volume 10 - 2023 | https://doi.org/10.3389/fvets.2023.1231377

[53] Matz, A. P., Fernandez-Prieto, J.-A., Cañada-Bago, J., & Birkel, U. (2020). A Systematic Analysis of Narrowband IoT Quality of Service. Sensors, 20(6), 1636. https://doi.org/10.3390/s20061636

[54] Merenda VR, Bodempudi VUC, Pairis-Garcia MD, Li G. (2024). Development and validation of machine-learning models for monitoring individual behaviors in group-housed broiler chickens. Poult Sci. 2024 Dec;103(12):104374. doi: 10.1016/j.psj.2024.104374.

[55] MetricStream. (2025). Compliance dashboard in 2025: A complete guide. Retrieved July 24, 2025, from https://www.metricstream.com/learn/compliance-dashboard.html

[56] Miller, Jackson & Shekhar, Suman. (2024). Cloud Computing in Smart Agriculture: Data Storage, Processing, and Analysis.

[57] Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., Łobodzińska, A., & Śnieg, M. (2025). The IoT and AI in Agriculture: The Time Is Now—A Systematic Review of Smart Sensing Technologies. Sensors, 25(12), 3583. https://doi.org/10.3390/s25123583

[58] Mohialdin, Abdallah & Elbarrany, Abdullah & Atia, Ayman. (2023). Chicken Behavior Analysis for Surveillance in Poultry Farms. International Journal of Advanced Computer Science and Applications. 14. 10.14569/IJACSA.2023.01403106.

[59] Muhammad Farhan Hidayat, Winda Nur Cahyo. (2023). Design and development of broiler farm dashboard: enhancing farm management decision-making and performance measurement. Jurnal Sistem dan Manajemen Industri Vol 7 No 1 June 2023, 82-94 http://dx.doi.org/10.30656/jsmi.v7i1.5968

[60] Natho P. S. Boonying, P. Bonguleaum, N. Tantidontanet, L. Chamuthai. (2025). An enhanced machine vision system for smart poultry farms using deep learning. Smart Agricultural Technology, Volume 12, 101083, ISSN 2772-3755. https://doi.org/10.1016/j.atech.2025.101083.

[61] Navin K, Mukesh Krishnan M․ B. (2024). Fuzzy rule based classifier model for evidence based clinical decision support systems. Intelligent Systems with Applications, Volume 22, 200393, ISSN 2667-3053. https://doi.org/10.1016/j.iswa.2024.200393.

[62] Neethirajan, Suresh. (2023). The Significance and Ethics of Digital Livestock Farming. AgriEngineering. 5. 488-505. 10.3390/agriengineering5010032.

[63] Nétek, Rostislav. (2024). Map-Oriented Dashboards Online—From Minor Method to GIScience Trend. 10.1007/978-3-031-50848-6_3.

[64] Ojo Rasheed O., Anuoluwapo O. Ajayi, Hakeem A. Owolabi, Lukumon O. Oyedele, Lukman A. Akanbi. (2022). Internet of Things and Machine Learning techniques in poultry health and welfare management: A systematic literature review. Computers and Electronics in Agriculture, Volume 200, 107266, ISSN 0168-1699. https://doi.org/10.1016/j.compag.2022.107266.

[65] Orakwue, Stella Ifeoma & Al-Khafaji, Hamza Mohammed Ridha & Chabuk, Mays. (2022). IoT Based Smart Monitoring System for Efficient Poultry Farming. Webology. 19. 4105-4112. 10.14704/WEB/V19I1/WEB19270.

[66] Parisa Niloofar, Deena P. Francis, Sanja Lazarova-Molnar, Alexandru Vulpe, Marius-Constantin Vochin et al. (2021). Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges. Computers and Electronics in Agriculture, Volume 190, 106406, ISSN 0168-1699. https://doi.org/10.1016/j.compag.2021.106406.

[67] Pasandideh, Faezeh. (2020). IoT Based Smart Farming: Are the LPWAN Technologies Suitable for Remote Communication?. 10.1109/SmartIoT49966.2020.00048.

[68] Pavia G, Branda F, Ciccozzi A, Romano C, Locci C. (2024). Integrating Digital Health Solutions with Immunization Strategies: Improving Immunization Coverage and Monitoring in the Post-COVID-19 Era. Vaccines (Basel). doi: 10.3390/vaccines12080847. PMID: 39203973; PMCID: PMC11359052.

[69] Petukhova T, Spinato M, Rossi T, Guerin MT, Kelton D, Nelson-Smikle P, Barham M, Ojkic D, Poljak Z. (2023). Development of interactive dashboards for monitoring endemic animal pathogens in Ontario, Canada: Ontario interactive animal pathogen dashboards. J Vet Diagn Invest. 2023 Nov;35(6):727-736. doi: 10.1177/10406387231190076.

[70] Pitesky, Maurice & Gendreau, Joseph & Bond, Tristan & Carrasco-Medanic, Roberto. (2020). Data challenges and practical aspects of machine learning-based statistical methods for the analyses of poultry data to improve food safety and production efficiency. CAB Reviews Perspectives in Agriculture Veterinary Science Nutrition and Natural Resources. 15. 1-11. 10.1079/PAVSNNR202015049.

[71] Rahman, Mahbubur & Kohinoor, Md & Sami, Aftar Ahmad. (2023). Enhancing Poultry Farm Productivity Using IoT-Based Smart Farming Automation System. 1-6. 10.1109/ICCIT60459.2023.10441525.

[72] Rana, Vijay & Sharma, Sunny & THAKUR, KISHORE & Pandit, Anil & Mahajan, Sunita. (2023). Internet of Things in Livestock Farming: Implementation and Challenges. 10.21203/rs.3.rs-2559126/v1.

[73] RenovaCloud. (2025). An introduction to and evaluation of Apache Spark for big data architectures. Retrieved July 23, 2025, from https://renovacloud.com/en/an-introduction-to-and-evaluation-of-apache-spark-for-big-data-architectures/

[74] Retiti Diop Emane, C., Song, S., Lee, H., Choi, D., Lim, J., Bok, K., & Yoo, J. (2024). Anomaly Detection Based on GCNs and DBSCAN in a Large-Scale Graph. Electronics, 13(13), 2625. https://doi.org/10.3390/electronics13132625

[75] Riaboff L., L. Shalloo, A.F. Smeaton, S. Couvreur, A. Madouasse, M.T. Keane. (2022). Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data. Computers and Electronics in Agriculture, Volume 192, 106610, ISSN 0168-1699. https://doi.org/10.1016/j.compag.2021.106610.

[76] Richman, J. (2025). Google BigQuery ETL unleashed: 9 best practices in 2025. Estuary. Retrieved July 23, 2025, from https://estuary.dev/blog/google-bigquery-etl/

[77] Salamone, S. (2022). Apache Pulsar: Core technology for streaming applications. RTInsights. Retrieved July 23, 2025, from https://www.clouddatainsights.com/apache-pulsar-core-technology-for-streaming-applications

[78] Sanni, Babatunde. (2024). Real-time Data Streaming: Exploring real-time data streaming with tools like Apache Kafka to provide immediate insights and alerts.

[79] Santana, T. C., Guiselini, C., Pandorfi, H., Vigoderis, R. B., Barbosa Filho, J. A. D., Soares, R. G. F., Araújo, M. d. F., Gomes, N. F., Lima, L. D. d., & Santos, P. C. d. S. (2025). Ethics, Animal Welfare, and Artificial Intelligence in Livestock: A Bibliometric Review. AgriEngineering, 7(7), 202. https://doi.org/10.3390/agriengineering7070202

[80] Seungmin Lee, Jeong Soo Park, Ji Hye Hong, Hyowon Woo, Chang-hyun Lee, Ju Hwan Yoon, Ki-Baek Lee, Seok Chung, Dae Sung Yoon, Jeong Hoon Lee. (2025). Artificial intelligence in bacterial diagnostics and antimicrobial susceptibility testing: Current advances and future prospects. Biosensors and Bioelectronics, Volume 280, 117399, ISSN 0956-5663. https://doi.org/10.1016/j.bios.2025.117399.

[81] SGS Digicomply Editorial Team. (2023). USDA vs. FDA: A comparative study of food product regulation. Digicomply. Retrieved July 24, 2025, from https://www.digicomply.com/blog/usda-vs.-fda-a-comparative-study-of-food-product-regulation

[82] Shi J, Zeng X, Cui P, Yan C, Chen H. (2022). Alarming situation of emerging H5 and H7 avian influenza and effective control strategies. Emerg Microbes Infect. 2023 Dec;12(1):2155072. doi: 10.1080/22221751.2022.2155072. PMID: 36458831; PMCID: PMC9754034.

[83] Singh, Ashutosh & Singh, Namrata & Kumar, Narender. (2024). An overview of the literature on the use of AI and ML enabled IoT in the management of poultry health and welfare. 10.58357/jedcs.2024.v3.i3.6.

[84] Subedi, Deepak & Farhan, Muhammad Haris & Niraula, Abhisek & Shrestha, Prajjwal & Chandran, Deepak & Acharya, Krishna & Ahmad, Muhammad. (2024). Avian Influenza in Low and Middle-Income Countries (LMICs): Outbreaks, Vaccination Challenges, and Economic Impact. Pakistan Veterinary Journal. 44. 9-17. 10.29261/pakvetj/2024.139.

[85] Suyal, Manish & Sharma, Sanjay. (2024). A Review on Analysis of K-Means Clustering Machine Learning Algorithm based on Unsupervised Learning. Journal of Artificial Intelligence and Systems. 6. 85-95. 10.33969/AIS.2024060106.

[86] Terence, S., Immaculate, J., Raj, A., & Nadarajan, J. (2024). Systematic Review on Internet of Things in Smart Livestock Management Systems. Sustainability, 16(10), 4073. https://doi.org/10.3390/su16104073

[87] Tobin, D. (2025). SQL transformations for optimized ETL pipelines. Integrate.io. Retrieved July 23, 2025, from https://www.integrate.io/blog/sql-transformations-for-optimized-etl-pipelines/

[88] U.S. Department of Agriculture, Animal and Plant Health Inspection Service. (2024). Laws and regulations. Retrieved July 22, 2025, from https://www.aphis.usda.gov/laws-regs

[89] Uğur Özentürk, Zhengqi Chen, Lorenzo Jamone, Elisabetta Versace. (2024). Robotics for poultry farming: Challenges and opportunities. Computers and Electronics in Agriculture, Volume 226, 109411, ISSN 0168-1699. https://doi.org/10.1016/j.compag.2024.109411.

[90] United Nations. (2024). Avian flu reported in 108 countries across five continents, says UN health agency. UN News. Retrieved July 21, 2025, from https://news.un.org/en/story/2024/12/1158286

[91] Upadhyaya, A., & Joshi, R. (2024). Real-time data monitoring with AWS IoT Core and Imply Data. AWS Partner Network Blog. Retrieved July 23, 2025, from https://aws.amazon.com/blogs/apn/real-time-data-monitoring-with-aws-iot-core-and-imply-data/

[92] Usmani, Mohammad Faiz. (2021). MQTT Protocol for the IoT - Review Paper. 10.13140/RG.2.2.26065.10088.

[93] Vlaicu, P. A., Gras, M. A., Untea, A. E., Lefter, N. A., & Rotar, M. C. (2024). Advancing Livestock Technology: Intelligent Systemization for Enhanced Productivity, Welfare, and Sustainability. AgriEngineering, 6(2), 1479-1496. https://doi.org/10.3390/agriengineering6020084

[94] Vredenberg I, van Schaik G, Velkers FC, Fabri T, Spierenburg MAH, Germeraad EA, van der Poel WHM, Stegeman A. (2025). Assessing the Use of Different Surveillance Components to Detect Highly Pathogenic Avian Influenza Outbreaks in Poultry in the Netherlands in Low- and High-Risk Years. Transbound Emerg Dis. 2025 Jan 31;2025:7441785. doi: 10.1155/tbed/7441785. PMID: 40302752; PMCID: PMC12016776.

[95] Wang, B., Sun, Z., Jiang, X., Zeng, J., & Liu, R. (2023). Kalman Filter and Its Application in Data Assimilation. Atmosphere, 14(8), 1319. https://doi.org/10.3390/atmos14081319

[96] World Organisation for Animal Health. (n.d.). Foot and mouth disease. Retrieved July 22, 2025, from https://www.woah.org/en/disease/foot-and-mouth-disease/

[97] Wright, J. (2025). Best thermal cameras for agriculture & farming: Improve crop & livestock monitoring. Tester. Retrieved July 22, 2025, from https://www.tester.co.uk/advice/best-thermal-cameras-for-agriculture-farming-improve-crop-livestock-monitoring/

[98] Yadav, Sandeep. (2022). A Comparative Study of ARIMA, Prophet and LSTM for Time Series Prediction. Journal of Artificial Intelligence Machine Learning and Data Science. 1. 1813-1816. 10.51219/JAIMLD/sandeep-yadav/402.

@article{1710789,
      author = {Folasayo Ogundipe},
      title = {From Sensor to Strategy: Leveraging IoT and Real-Time Data Analytics for Poultry Disease Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {3},
      pages = {1998-2015},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710789.pdf},
      abstract = {The poultry industry faces progressive challenges in disease management due to the rapid spread of pathogens like avian influenza, posing significant threats to food security, public health, and economic stability. Traditional disease monitoring methods, reliant on manual inspections and delayed reporting, are often inadequate for timely interventions. This paper explores how Internet of Things (IoT) technologies and real-time data analytics can revolutionize poultry disease surveillance by enabling continuous, automated monitoring of environmental, physiological, and behavioral health indicators. Through predictive analytics and early warning systems, IoT-driven frameworks offer the potential to shift disease response from reactive to proactive, significantly reducing mortality rates, minimizing economic losses, and enhancing biosecurity. However, widespread adoption requires addressing critical challenges, including data privacy concerns, infrastructure limitations in rural areas, device interoperability, and the cost of implementation versus return on investment. The paper advocates for strategic partnerships between governments, technology providers, and the poultry sector, alongside policy frameworks that ensure ethical, scalable, and inclusive deployment of IoT systems. Future research should focus on developing AI models for disease variants, expanding surveillance to other zoonotic diseases, and creating affordable IoT solutions for smallholder farmers. Lastly, leveraging IoT and real-time analytics is important for building resilient, data-driven agricultural health systems that can safeguard food supply chains and public health in an increasingly interconnected world.},
      keywords = {Poultry Disease Management, Internet of Things (IoT), Real-Time Data Analytics, Avian Influenza, Predictive Analytics, Biosecurity, Livestock Health Surveillance, Data Privacy, Smart Farming, Food Security, Zoonotic Diseases, AI in Agriculture, Precision Livestock Farming, Smallholder Farmers, Agricultural Technology Adoption.},
      month = {September},
      doi = {https://doi.org/10.64388/IREV9I3-1710789-3276}
  }