International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 18, No. 2, 8 Apr. 2026
Cover page and Table of Contents: PDF (size: 1265KB)
IoT-Enabled Estrus Detection in Dairy Cattle Using Machine Learning Technique
PDF (1265KB), PP.121-139
Views: 0 Downloads: 0
Author(s)
Rhutuja Kshirsagar
1,*
Kamlesh Kalbande
1
Pooja Yerunkar
2
Akhilesh Lokhande
1
Krishnendu Mondal
1
Harsh Jagtap
1
Rahul Chunarkar
1
Index Terms
Internet of Things, Dairy Management, Estrus Detection, Machine Learning Algorithm, Google Firebase
Abstract
In the dairy industry, optimizing reproductive management is crucial for sustainable operations and enhancing animal welfare. The traditional manual detection methods usually miss many of the estrus incidences and hence have resulted in a 20-30% decline in conception rates and further massive economic losses.This paper presents an advanced framework integrating machine learning and Internet of Things (IoT) technologies to improve estruses detection in dairy cattle, thereby supporting efficient herd management and productivity. The proposed solution leverages a stacking model of Random Forest and Gradient Boosting Machine (GBM) algorithms to accurately identify estruses events, providing a reliable method for reproductive monitoring. The experimental evaluation yields accuracies of 92.1 % using RF, 92.3 % using GBM, and an improved 93.19 % when the stacking model is applied, along with improvements in precision of 94 and an F1-score of 94 %, reflecting its strength in complex behavioral pattern recognition. Rigorous evaluation across key performance metrics confirms the model’s high accuracy, underscoring its suitability for practical deployment. The system employs IoT-enabled smart collars equipped with temperature sensors, accelerometers, GPS, and RFID to gather real-time data on cattle health and reproductive status. By analyzing this data, the system delivers precise and timely insights into estruses cycles, enabling targeted breeding interventions and enhanced reproductive management. Data collected through the smart collars is securely stored in Google Firebase, facilitating efficient data archiving and rapid access via a user-friendly web application. The proposed integration of IoT, machine learning, and cloud computing presents a holistic, scalable, and economically viable solution for enhancing reproductive efficiency, animal welfare, and sustainable dairy management.
Cite This Paper
Rhutuja Kshirsagar, Kamlesh Kalbande, Pooja Yerunkar, Akhilesh Lokhande, Krishnendu Mondal, Harsh Jagtap, Rahul Chunarkar, "IoT-Enabled Estrus Detection in Dairy Cattle Using Machine Learning Technique", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.2, pp. 121-139, 2026. DOI:10.5815/ijieeb.2026.02.08
Reference
[1]Wang, J.; Bell, M.; Liu, X.; Liu, G. Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data. Animals 2020, 10, 1160. https://doi.org/10.3390/ani10071160J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
[2]Jahangir, R. (2022). Estrus Detection in Dairy Cows from Location and Acceleration Data using Machine Learning. Sukkur IBA Journal of Computing and Mathematical Sciences, 6(1), 26-41.
[3]Fauvel, K., Masson, V., Fromont, E., Faverdin, P., and Termier, A. (2019, July). Towards sustainable dairy management-a machine learning enhanced method for estrus detection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 3051-3059).R. Nicole, “Title of paper with only first word capitalized”, J. Name Stand. Abbrev., in press.
[4]Aryawan, Putu Oki Wiradita, et al. “Real-time estrus detection in cattle using deep learning-based pose estimation.” BIO Web of Conferences. Vol. 123. EDP Sciences, 2024.M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, 1989.
[5]Andersson, L. Mattias, et al. “Wearable wireless sensor for estrus detection in cows by conductivity and temperature measurements.” 2015 IEEE SENSORS. IEEE, 2015.
[6]Santos, Camila Alves dos, et al. “Automated systems for estrous and calving detection in dairy cattle.” AgriEngineering 4.2 (2022): 475-482.
[7]Perez Marquez, Hector J., Divakar J. Ambrose, and Clover J. Bench. “Behavioral changes to detect estrus using ear-sensor accelerometer compared to in-line milk progesterone in a commercial dairy herd.” Frontiers in Animal Science 4 (2023): 1149085.
[8]Kim, H., Oh, S., Ahn, S., and Choi, B. (2017). Real-time Temperature Monitoring to Enhance Estrus Detection in Cattle Utilizing Ingestible Bio-Sensors: Method and Case Studies: Method and Case Studies.
[9]Saputra, Mohammad Wahyu Denys, Moh Abdullah Anshori, and Azam Muzakhim Imammuddien. “Estrus Cycle Monitoring System in Cow Based on IoT.” Journal of Telecommunication Network (Jurnal Jaringan Telekomunikasi) 13.3 (2023): 265-273.
[10]R. Mathur and K. Kalbande, “Internet of Things (IoT) based En ergy Tracking and Bill Estimation System,” 2020 Fourth Interna tional Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2020, pp. 80-85, doi: 10.1109/I SMAC49090.2020.9243480
[11]S. Choudhary, K. Kalbande and N. Dhote, “IoT based Multi-point Pesticide Spraying Machine,” 2021 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2021, pp. 432-436, doi10.1109/ICICT50816.2021.9358585.
[12]K. Kalbande, S. Choudhary, A. Singru, I. Mukherjee and P. Bakshi, “Multi-Way Controlled Feedback Oriented Smart System for Agricul tural Application using Internet of Things,” 2021 5th International Con ference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2021, pp. 96-101, doi: 10.1109/ICOEI51242.2021.9452946.
[13]Kolhe, Pranita, Kamlesh Kalbande, and Atul Deshmukh. “Internet of Thing and Machine Learning Approach for Agricultural Application: A Review.” 2022 10th International Conference on Emerging Trends in En gineering and Technology-Signal and Information Processing (ICETET SIP-22). IEEE, 2022..
[14]A. P. Mukte, R. Pravin Jaiswal, S. A. Dambhare, U. Agrawal and R. Agrawal, “Smart Data Transfer For Data Monetization,” 2021 In ternational Conference on Computational Intelligence and Computing Applications (ICCICA), Nagpur, India, 2021, pp. 1-6, doi: 10.1109/IC CICA52458.2021.9697182.
[15]D. Shende, N. Wyawahare, L. Thakare and R. Agrawal, “Design Process for Adaptive Spraying of Pesticides Based on Mutual Plant Health De tection and Monitoring: A Review,” 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 2023, pp. 729-733, doi: 10.1109/ICAIS56108.2023.10073695.
[16]P. Malewar, R. Kadu, R. Kakde, N. P. Wyawahare and R. Agrawal, “A Review Paper on Data Sensing and Acquisition Complexity and Accuracy in Green House Monitoring Systems,” 2022 10th International Conference on Emerging Trends in Engineering and Technology- Signal and Information Processing (ICETET-SIP22), Nagpur, India, 2022, pp. 1-6, doi: 10.1109/ICETET-SIP2254415.2022.97917
[17]R. Khadilkar, G. Bobade, N. Chavhan, R. Agrawal and C. Dhule, “Anal yse Indications of Vital Parameters in Patients Using Business Intelli gence and Random Forest,” 2023 International Conference on Commu nication, Security and Artificial Intelligence (ICCSAI), Greater Noida, India, 2023, pp. 181-186, doi: 10.1109/ICCSAI59793.2023.10421310
[18]G. Bobade, C. Dhule, R. Khadilkar, R. Agrawal and N. C. Morris, “Design of Smart Irrigation System Based on MLA,” 2023 IEEE World Conference on Applied Intelligence and Computing (AIC), Sonbhadra, India, 2023, pp. 174-179, doi: 10.1109/AIC57670.2023.10263833.
[19]M. Jaipurkar, N. Ragit, C. Tambuskar and P. D. Saraf, “IPL Data Analysis and Visualization Using Microsoft Power BI Tool,” 2023 14th International Conference on Computing Communication and Net working Technologies (ICCCNT), Delhi, India, 2023, pp. 1-6, doi: 10.1109/ICCCNT56998.2023.10306783
[20]Lodkaew, Thanawat, Kitsuchart Pasupa, and Chu Kiong Loo. “CowXNet: An automated cow estrus detection system.” Expert Systems with Applications 211 (2023): 118550.
[21]Reith, S., and S. Hoy. “Behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle.” Animal 12.2 (2018): 398-407.
[22]Suthar, V. S., and A. J. Dhami. “Estrus Detection Methods in Buffalo.” Veterinary World 3.2 (2010).
[23]Palmer, Maeve A., Gabriela Olmos, Laura A. Boyle, and John F. Mee. “Estrus detection and estrus characteristics in housed and pastured Holstein–Friesian cows.” Theriogenology 74, no. 2 (2010): 255-264.
[24]Fesseha, H., Degu, T. (2020). Estrus detection, Estrus synchronization in cattle and it’s economic importance. Int. J. Vet. Res, 3(1), 1001.
[25]Arago, Nilo M., et al. “Automated estrus detection for dairy cattle through neural networks and bounding box corner analysis.” Interna tional Journal of Advanced Computer Science and Applications 11.9 (2020)