Wearable Sensor-Based Birth Prediction System for Indigenous Cattle Breeds

Abstract

Calving is a critical stage in dairy cattle management, and delayed identification of labor can lead to calf mortality, maternal health complications, and economic loss. In many traditional farming systems, calving is monitored manually, requiring constant observation by farmers, which is time-consuming and often unreliable. Therefore, the early prediction of calving is essential to ensure timely assistance during parturition, improve calf survival rates, and enhance overall herd-management. However, conventional monitoring practices primarily rely on visual observation of behavioral changes, making early detection difficult and inefficient in large-scale dairy operations. To address these challenges, this study proposes the development of a wearable sensor-based birth prediction system for indigenous cattle breeds that enables continuous monitoring of physiological and behavioral indicators associated with imminent calving. The proposed device integrates a DS18B20 digital temperature sensor to monitor tail-base temperature variations and an MPU6050 accelerometer sensor to detect tail movement and activity patterns,which typically increase before labor. The sensor data were processed using an ESP32 microcontroller, which analyzed temperature drops and abnormal movement patterns that indicated the onset of the calving. When these indicators exceed the predefined threshold values, the system automatically activates a SIM800L GSM module to send instant SMS alerts to the farmer, enabling timely intervention and assistance. The system is designed as a compact, tail-mounted wearable device powered by a rechargeable lithium-ion battery, making it suitable for continuous monitoring in farm environments. Field observations indicated noticeable changes in temperature and tail activity before calving, validating the effectiveness of these physiological indicators. The developed prototype provides a low-cost, reliable, and farmer-friendly solution for predicting calving. This technology has the potential to reduce manual monitoring, improve reproductive management, enhance animal welfare, and support the advancement of smart livestock farming practices.

Introduction

To overcome these limitations, the study proposes a wearable sensor-based birth prediction system for dairy cattle. The system uses sensors to continuously monitor key indicators such as:

• Tail-base temperature (which drops before calving)
• Tail movement/activity (which increases before labor)

These sensors are connected to an ESP32 microcontroller, which processes the data in real time. When signs of imminent calving are detected, a GSM module (SIM800L) sends SMS alerts to farmers, enabling timely intervention without constant manual monitoring.

The system is designed as a low-cost, tail-mounted wearable device, making it practical and accessible for small and medium dairy farms. It follows a modular architecture with sensing, processing, communication, and power management components.

Key advantages include:

• Early and accurate calving prediction
• Non-invasive and animal-friendly monitoring
• Automated real-time alerts
• Reduced labor and improved farm efficiency

Results from testing show that the system reliably detects pre-calving conditions by combining temperature drop and increased movement, providing accurate and timely alerts.

Overall, the proposed system enhances livestock management, animal welfare, and farm productivity by replacing manual observation with an efficient, automated monitoring solution.

Conclusion

The development of the Wearable Sensor-Based Birth Prediction System for Indigenous Cattle Breeds successfully demonstrated an efficient, reliable, and affordable approach for the early prediction of calving in dairy cattle. By integrating a DS18B20 temperature sensor, MPU6050 accelerometer sensor, GSM communication module, and ESP32 microcontroller-based processing unit, the device enables continuous monitoring and real-time analysis of the physiological and behavioral indicators associated with the onset of labor. The automated alert system sends SMS notifications to farmers when abnormal conditions are detected, allowing for timely intervention and reducing risks during the calving process. The wearable and noninvasive design of the device ensures animal comfort while providing consistent and reliable monitoring results under practical farm conditions. Its compact structure, ease of installation, and low-cost components make it highly suitable for small- and medium-scale dairy farms, where continuous manual monitoring of cattle is difficult. Furthermore, the modular architecture of the system allows for future improvements, such as integration with mobile applications, cloud-based monitoring, and advanced data analysis techniques, to enhance livestock management efficiency. Overall, this project highlights the potential of sensor-based smart livestock monitoring systems for improving reproductive management in dairy farming. The successful implementation of this wearable device demonstrates a practical approach to modernizing traditional calving monitoring practices, enhancing animal welfare, reducing calf mortality, and supporting sustainable and productive dairy farm management.

References

[1] K. Rajendran & M. Varghese (2024), “Prediction of Calving Time in Dairy Cows Using Tail-Mounted Accelerometer Sensors”. [2] K. Bennett & L. Moore (2023), “Integration of Temperature and Activity Metrics for Parturition Prediction”. [3] Z. Li & H. Chang (2022), “Machine Learning Approaches for Predicting Calving Time in Dairy Herds”. [4] M. Schneider & E. Wolff (2022), “Sensorized Tail Bands for Detecting Early Labor in Cattle”. [5] A. Patel & S. Shah (2021), “Wearable Monitoring Solutions for Animal Welfare Assessment”. [6] P. Nelson & T. Carter (2021), “Battery Optimization Techniques for Continuous Wearable Monitoring”. [7] S. Nakamura & T. Fujita (2021), “Continuous Temperature Monitoring for Predicting Parturition in Dairy Cattle”. [8] A. Kumar & R. Mehta (2020), “IoT-Based Smart Monitoring System for Dairy Cow Reproductive Health”. [9] D. Mendez & H. Santos (2020), “Low-Power Embedded Systems for Wearable Animal Health Devices”. [10] W. Fischer & M. Klein (2020), “Environmental Impact on Sensor Accuracy in Wearable Livestock Devices”. [11] L. Romero & J. Castillo (2020), “Design and Evaluation of Wearable Straps for Cattle Monitoring Devices”. [12] R. Narayanan & V. Sundaresan (2019), “Behavioural Indicators of Impending Parturition in Indigenous Cattle Breeds”. [13] R. Ibrahim & F. Yusuf (2019), “Impact of Breed Characteristics on Wearable Sensor Performance”. [14] L. Harrison & P. McAllister (2019), “Wearable Multi-Sensor Systems for Livestock Behavioural Monitoring”. [15] J. O’Connor & S. Hughes (2018), “Activity Pattern Analysis of Dairy Cows Prior to Calving”. [16] T. Jackson & R. Powell (2018), “Smart GSM-Based Alert Systems in Livestock Farming”. [17] C. Peterson & L. Brown (2017), “Wireless Temperature Sensor Networks for Farm Animal Monitoring”. [18] [18] M. Johansson & K. Berglund (2016), “Physiological and Behavioural Synchronization in Cattle During Pre-Calving Period”. [19] D. Henderson & P. Wallace (2015), “Comparative Study of Vaginal, Rectal and Tail- Base Temperature Methods”. [20] S. Redmond & H. Daly (2014), “Assessment of Calving Difficulty and Its Economic Impact”.

Copyright

Copyright © 2026 Mr. N. Gunal, Mr. M. Manoj, Ms. G. Srimathi, Mr. Kelin S Johnson. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.