Monitoring cattle behavior using deep learning:  An LSTM-based approach with accelerometer data: Monitoring cattle behavior using deep learning

Duc Nghia Tran; Manh Tuyen Vi; Binh Duong Tran; Minh Thang Hoang; Quang Huy Pham; Viet Manh Do; Prof.Dr Tan Tran Duc

Authors

Tran Duc Nghia Institute of Information Technology, Vietnam Academy of Science and Technology
Vi Manh Tuyen Faculty of Electrical and Electronic Engineering, Phenikaa University
Tran Binh Duong Vietnam Paper Corporation
Hoang Minh Thang MobiFone Corporation - MobiFone Northern Network Center
Pham Quang Huy Electric Power University
Do Viet Manh Institute of Information Technology, Vietnam Academy of Science and Technology
Prof.Dr Tan Tran Duc (Corresponding Author) Faculty of Electrical and Electronic Engineering, Phenikaa University

Keywords:

accelerometer, behavior classification, LSTM, monitoring

Abstract

Behavior data analysis is a crucial factor in the early detection of cow health issues, thereby optimizing farming processes and improving productivity in large-scale farms. Accelerometers, attached to the neck or legs of cows, collect movement data, providing a foundation for analyzing animal behavior. Previous studies have proposed cow behavior classification systems based on accelerometer data combined with machine learning algorithms. However, with the advancement of deep learning, the application of Long Short-Term Memory (LSTM) networks can significantly enhance classification performance. In this study, we utilize an LSTM network to classify four primary cow behaviors: Eating, Lying, Standing, and Walking. The LSTM model effectively processes time-series data by retaining essential information while filtering out unnecessary data. Experimental results demonstrate that the model achieves high classification performance, with an average accuracy of approximately 90% across all behaviors, outperforming traditional machine learning algorithms. This research can be implemented in smart farms, integrating with IoT technology to automate livestock monitoring and management efficiently.

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Monitoring cattle behavior using deep learning: An LSTM-based approach with accelerometer data