AN IOT-BASED WEARABLE SENSOR SYSTEM FOR MONITORING THE HEALTH, RUMINATION, AND ESTRUS CYCLE OF DAIRY COWS IN INDONESIA
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Dairy Cows, Health Monitoring, Internet of Things, Precision Livestock Farming, Wearable Sensors
Abstract
The rapid development of Internet of Things (IoT) technology offers significant opportunities for improving livestock management, especially in dairy farming systems in developing countries like Indonesia. Traditional methods of monitoring dairy cow health, behavior, and estrus cycles rely on manual observation, which can be time-consuming, subjective, and inaccurate. These limitations lead to delayed disease detection, suboptimal reproductive performance, and reduced milk productivity. This study aims to design and evaluate an IoT-based wearable sensor system for continuous monitoring of dairy cow health, rumination patterns, and estrus cycles in Indonesian dairy farms. A research and development approach combined with field testing was employed. The system integrates wearable sensors attached to cows, collecting data on movement, body temperature, and rumination activity. Data is transmitted in real-time via IoT networks to a cloud platform for processing and visualization. System performance was assessed through accuracy testing, reliability analysis, and farmer feedback. The results show that the system effectively detects changes in rumination behavior, identifies early health issues, and predicts estrus cycles with high consistency compared to traditional methods. Farmers reported improved decision-making efficiency and reduced labor intensity. The IoT-based wearable sensor system demonstrates potential as an innovative solution for enhancing dairy cow health monitoring and reproductive management in Indonesia, supporting sustainable dairy farming practices.
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References
Alipio, M., & Villena, M. L. (2023). Intelligent wearable devices and biosensors for monitoring cattle health conditions: A review and classification. Smart Health, 27, 100369. https://doi.org/10.1016/j.smhl.2022.100369
Almeida, M., & Silva, S. (2025). Chapter 2—Novel technologies for monitoring small ruminant welfare. In G. Kannan (Ed.), Small Ruminant Welfare, Production and Sustainability (pp. 43–60). Academic Press. https://doi.org/10.1016/B978-0-443-22201-6.00002-5
Arshad, J., Irtisam, A., Arif, T., Rasheed, M. S., Chauhdary, S. T., Rahmani, M. K. I., & Almajalid, R. (2024). A federated learning model for intelligent cattle health monitoring system using body area sensors and IoT. Egyptian Informatics Journal, 27, 100488. https://doi.org/10.1016/j.eij.2024.100488
Asogan, A., Sazali, N., Veerendra, A. S., Samylingam, L., Aslfattahi, N., Kok, C. K., & Kadirgama, K. (2026). A review on the impact of AI-enabled thermal imaging and IoT sensor fusion on early detection of mastitis in dairy cattle. Biosensors and Bioelectronics: X, 28, 100735. https://doi.org/10.1016/j.biosx.2025.100735
Brenya, R., Zhu, J., & Sampene, A. K. (2023). Can agriculture technology improve food security in low- and middle-income nations? A systematic review. Sustainable Food Technology, 1(4), 484–499. https://doi.org/10.1039/d2fb00050d
Chelotti, J. O., Martinez-Rau, L. S., Ferrero, M., Vignolo, L. D., Galli, J. R., Planisich, A. M., Rufiner, H. L., & Giovanini, L. L. (2024). Livestock feeding behaviour: A review on automated systems for ruminant monitoring. Biosystems Engineering, 246, 150–177. https://doi.org/10.1016/j.biosystemseng.2024.08.003
Chen, T., Zheng, H., Chen, J., Zhang, Z., & Huang, X. (2024). Novel intelligent grazing strategy based on remote sensing, herd perception and UAVs monitoring. Computers and Electronics in Agriculture, 219, 108807. https://doi.org/10.1016/j.compag.2024.108807
Concepcion, R., Bonto, A., Agulto, R., Ann Bautista, M. G., Alipio, M., Bandala, A., Mohamad, R., Francisco, K., Baun, J. J., Seagan, C. G., Cadavero, L. S., & Basilla-Bongay, C. (2026). Chapter 33—Internet of Things integration and smart technologies in food systems. In T. Sarkar & A. Haldorai (Eds.), Artificial Intelligence in Food Science (pp. 627–650). Academic Press. https://doi.org/10.1016/B978-0-443-26468-9.00042-4
Distante, D., Albanello, C., Zaffar, H., Faralli, S., & Amalfitano, D. (2025). Artificial intelligence applied to precision livestock farming: A tertiary study. Smart Agricultural Technology, 11, 100889. https://doi.org/10.1016/j.atech.2025.100889
Hussein, J. B., Workneh, T. S., Kassim, A., Ntsowe, K., Melesse, S. F., & El-Mesery, H. S. (2025). A review on the impact of big data analytics in transforming agricultural practices, food processing, and preservation strategies. Applied Food Research, 5(2), 101234. https://doi.org/10.1016/j.afres.2025.101234
Jhilta, A., Jadhav, K., Singh, R., Negi, S., kaur, S., Sharma, N., & Verma, R. K. (2026). Advanced precision veterinary technologies and smart boluses: Innovations in drug delivery, health monitoring, and future perspectives. Journal of Drug Delivery Science and Technology, 115, 107563. https://doi.org/10.1016/j.jddst.2025.107563
Jiang, W., Hao, H., Wang, H., & Wang, L. (2025). Possible application of agricultural robotics in rabbit farming under smart animal husbandry. Journal of Cleaner Production, 501, 145301. https://doi.org/10.1016/j.jclepro.2025.145301
Kafle, M., Nabadawa Hewage, S. C., Bradtmueller, A., Downey, B. C., Tabler, T., & Zhao, Y. (2025). A systematic literature review of wearable sensor technologies used in poultry research. Computers and Electronics in Agriculture, 239, 111030. https://doi.org/10.1016/j.compag.2025.111030
Kaswan, S., Chandratre, G. A., Upadhyay, D., Sharma, A., Sreekala, S. M., Badgujar, P. C., Panda, P., & Ruchay, A. (2024). CHAPTER 4—Applications of sensors in livestock management?. In A. Tarafdar, A. Pandey, G. K. Gaur, M. Singh, & H. O. Pandey (Eds.), Engineering Applications in Livestock Production (pp. 63–92). Academic Press. https://doi.org/10.1016/B978-0-323-98385-3.00004-9
Khanashyam, A. C., Jagtap, S., Agrawal, T. K., Thorakkattu, P., Malav, O. P., Trollman, H., Hassoun, A., Ramesh, B., Manoj, V., Rathnakumar, K., Bekhit, A. E.-D. A., & Nirmal, N. (2025). Applications of artificial intelligence in the dairy Industry: From farm to product development. Computers and Electronics in Agriculture, 238, 110879. https://doi.org/10.1016/j.compag.2025.110879
Krishnendu, M. R., & Singh, S. (2025). Chapter Six—Wearable sensors for animal health and wellness monitoring. In K. Mahato & A. Pandya (Eds.), Progress in Molecular Biology and Translational Science (Vol. 216, pp. 139–183). Academic Press. https://doi.org/10.1016/bs.pmbts.2025.06.008
Liang, J., Yuan, Z., Luo, X., Qu, J., Qi, Y., & Wang, C. (2025). Application of non-invasive monitoring technology in intensive sheep farming: A review. Smart Agricultural Technology, 12, 101215. https://doi.org/10.1016/j.atech.2025.101215
Liu, E. Y., Wang, S., Zhang, B., Khan, N. A., Tang, S., Zhou, C., He, Z., Tan, Z., & Liu, Y. (2025). A machine learning framework for precision prediction of lactation performance in large dairy herds: Integrating dietary, environmental, and health risk factors. Computers and Electronics in Agriculture, 238, 110832. https://doi.org/10.1016/j.compag.2025.110832
Liu, Q., Lu, C., Lv, Q., & Lei, L. (2025). Emerging point-of-care testing technology for the detection of animal pathogenic microorganisms. Chemical Engineering Journal, 512, 162548. https://doi.org/10.1016/j.cej.2025.162548
Méndez, D. A., Fajardo, B., Sanjuan, S., Calabuig, J. M., Arnau, R., Villagrá, A., Calvet-Sanz, S., & Estelles, F. (2025). Development of goat behaviour prediction with accelerometer data: A machine learning and pre-processing approach. Computers and Electronics in Agriculture, 237, 110701. https://doi.org/10.1016/j.compag.2025.110701
Ni, G., Jia, Y., Shi, Z., Chang, F., Miao, J., Wang, J., Ye, G., Wu, J., Yin, H., Jiang, W., Han, X., & Tang, W. (2026). A full end-to-end analytical framework for livestock behavior modeling and health assessment using wearable electronic recording system and machine learning. Smart Agricultural Technology, 13, 101686. https://doi.org/10.1016/j.atech.2025.101686
Nsabiyeze, A., Zhang, M., Li, J., Zhao, Q., & Zhang, X. (2025). Precision livestock farming for climate-resilient livestock management: A review of real-time monitoring and decision support systems. Journal of Cleaner Production, 524, 146454. https://doi.org/10.1016/j.jclepro.2025.146454
Papadopoulos, G., Papantonatou, M.-Z., Uyar, H., Kriezi, O., Mavrommatis, A., Psiroukis, V., Kasimati, A., Tsiplakou, E., & Fountas, S. (2025). Economic and environmental benefits of digital agricultural technological solutions in livestock farming: A review. Smart Agricultural Technology, 10, 100783. https://doi.org/10.1016/j.atech.2025.100783
Perea, A., Rahman, S., Chen, H., Cox, A., Nyamuryekung’e, S., Bakir, M., Cao, H., Estell, R., Bestelmeyer, B., Cibils, A. F., & Utsumi, S. A. (2025). Integrating LoRaWAN sensor networks and machine learning models to classify beef cattle behavior on arid rangelands of the southwestern United States. Smart Agricultural Technology, 11, 101002. https://doi.org/10.1016/j.atech.2025.101002
Shafi, F. B., Ahamed, Md. F., Khandakar, A., Rohouma, W., Ayari, M. A., & Reaz, M. B. I. (2025). Resource-constrained hybrid attention-driven approach for enhanced interpretability and scalability in multi-event livestock condition classification and monitoring. Results in Engineering, 28, 107391. https://doi.org/10.1016/j.rineng.2025.107391
Shahab, H., Iqbal, M., Sohaib, A., Rehman, A. ur, Bermak, A., & Munir, K. (2024). Design and implementation of an IoT-based monitoring system for early detection of lumpy skin disease in cattle. Smart Agricultural Technology, 9, 100609. https://doi.org/10.1016/j.atech.2024.100609
Song, D., Zou, F., Wang, L., & Wang, H. (2026). Applications and prospects of photoplethysmography technology in animal husbandry: A comprehensive review. Computers and Electronics in Agriculture, 240, 111164. https://doi.org/10.1016/j.compag.2025.111164
Thakur, R., Baghel, M., Bhoj, S., Jamwal, S., Chandratre, G. A., Vishaal, M., Badgujar, P. C., Pandey, H. O., & Tarafdar, A. (2024). CHAPTER 8—Digitalization of livestock farms through blockchain, big data, artificial intelligence, and Internet of Things?. In A. Tarafdar, A. Pandey, G. K. Gaur, M. Singh, & H. O. Pandey (Eds.), Engineering Applications in Livestock Production (pp. 179–206). Academic Press. https://doi.org/10.1016/B978-0-323-98385-3.00012-8
Wang, R., Li, Y., Tian, F., Liu, Y., Wang, Z., Yuan, C., & Lu, X. (2025). Estrus detection in dairy cows using advanced object tracking and behavioral analysis technologies. Computers and Electronics in Agriculture, 235, 110331. https://doi.org/10.1016/j.compag.2025.110331
Yin, M., Ma, R., Luo, H., Li, J., Zhao, Q., & Zhang, M. (2023). Non-contact sensing technology enables precision livestock farming in smart farms. Computers and Electronics in Agriculture, 212, 108171. https://doi.org/10.1016/j.compag.2023.108171
Zhang, M., Hong, D., Wu, J., Zhu, Y., Zhao, Q., Zhang, X., & Luo, H. (2025). Sheep-YOLO: improved and lightweight YOLOv8n for precise and intelligent recognition of fattening lambs’ behaviors and vitality statuses. Computers and Electronics in Agriculture, 236, 110413. https://doi.org/10.1016/j.compag.2025.110413
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Copyright (c) 2025 Nisreen Al-Sayid, Nour Ibrahim, Hassan Al-Attar
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