2025/06/19 by Dhakshinamoorthy, Druva, Jha, Avikshit, Majumdar, Sabyasachi +3
Agricultural and Biological Sciences · Veterinary · #Animal Behavior and Welfare Studies #C.3 #Effects of Environmental Stressors on Livestock #FOS: Computer and information sciences #H.4.2 #I.2.10 #I.2.6 #I.5.1 #I.5.4 #J.2 #Machine Learning (cs.LG) #Reproductive Physiology in Livestock
paper · pdf · doi:10.48550/arxiv.2506.16380
openalex publication_date 2025/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.