Abstract
This paper focuses on automating the classification of in-house cattle behavior using collar tags equipped with tri-axial accelerometers to collect data on feeding and ruminating behaviors. The accelerometer data is divided into time intervals (10, 30, 60, and 180 s), and we extract 15 essential posture-related features to create a labeled dataset for behavior classification. We evaluate four machine learning algorithms (Random Forest, Extreme Gradient Boosting, Decision Tree, and Logistic Regression) on this dataset using leave-one-out cross-validation. The results indicate that shorter time intervals result in better prediction performance. Random Forest and Decision Tree algorithms perform well, striking a good balance between sensitivity and specificity. This proposed approach holds promise for real-time behavior classification and has the potential to benefit livestock management and enhance animal welfare.
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Martono, N.P., Sawado, R., Nonaka, I., Terada, F., Ohwada, H. (2023). Automated Cattle Behavior Classification Using Wearable Sensors and Machine Learning Approach. In: Wu, S., Yang, W., Amin, M.B., Kang, BH., Xu, G. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2023. Lecture Notes in Computer Science(), vol 14317. Springer, Singapore. https://doi.org/10.1007/978-981-99-7855-7_5
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DOI: https://doi.org/10.1007/978-981-99-7855-7_5
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