Classification of Cattle Behavior and Detection of Heat (Estrus) using Sensor Data

• URL: http://arxiv.org/abs/2506.16380v1
• Date: Thu, 19 Jun 2025 15:00:23 GMT
• Title: Classification of Cattle Behavior and Detection of Heat (Estrus) using Sensor Data
• Authors: Druva Dhakshinamoorthy, Avikshit Jha, Sabyasachi Majumdar, Devdulal Ghosh, Ranjita Chakraborty, Hena Ray,
• Abstract summary: 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.<n>A labeled dataset was created using synchronized CCTV footage to annotate behaviors such as feeding, rumination, lying, and others.<n>Our system achieved over 93% behavior classification accuracy and 96% estrus detection accuracy on a limited test set.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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