This paper presents a novel system for monitoring cattle behavior and detecting estrus (heat) periods using sensor data and machine learning. We designed and deployed a low-cost Bluetooth-based neck collar equipped with accelerometer and gyroscope sensors to capture real-time behavioral data from real cows, which was synced to the cloud. A labeled dataset was created using synchronized CCTV footage to annotate behaviors such as feeding, rumination, lying, and others. We evaluated multiple machine learning models -- Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN) -- for behavior classification. Additionally, we implemented a Long Short-Term Memory (LSTM) model for estrus detection using behavioral patterns and anomaly detection. Our system achieved over 93% behavior classification accuracy and 96% estrus detection accuracy on a limited test set. The approach offers a scalable and accessible solution for precision livestock monitoring, especially in resource-constrained environments.
@article{arxiv.2506.16380,
title = {Classification of Cattle Behavior and Detection of Heat (Estrus) using Sensor Data},
author = {Druva Dhakshinamoorthy and Avikshit Jha and Sabyasachi Majumdar and Devdulal Ghosh and Ranjita Chakraborty and Hena Ray},
journal= {arXiv preprint arXiv:2506.16380},
year = {2025}
}
Comments
6 pages, 5 figures. Druva Dhakshinamoorthy and Avikshit Jha contributed equally as co-first authors. Work conducted during a summer internship at CDAC Kolkata by students of BITS Pilani