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

TL;DR

This paper introduces a low-cost sensor-based system utilizing machine learning to monitor cattle behavior and accurately detect estrus, enhancing precision livestock management in resource-limited settings.

Contribution

It presents a novel Bluetooth-enabled sensor collar and a combined machine learning approach for behavior classification and estrus detection in cattle.

Findings

Over 93% behavior classification accuracy

96% estrus detection accuracy

Scalable solution for resource-constrained environments

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.