Abstract
The research field of behavior monitoring and estrus detection in cows has predominantly concentrated on investigating isolated occurrences of mounting behavior, neglecting the analysis of dynamic behaviors exhibited during the estrus period, before and following mounting. To address these limitations, this paper proposes a framework that utilizes computer vision techniques to analyze visual data, classify behavioral features, track the duration of behaviors, and identify potential deviations from normal behavior based on historical data. The part of dynamic behavioral analysis, which encompasses the assessment of behavioral changes and historical behavior data to identify the optimal time window for Artificial Insemination (AI) and potential abnormalities in behaviors, is the main novelty of this framework. Based on our preliminary experiments conducted on a well-known public dataset, the behavior classification model achieves an overall accuracy of 80.7% in accurately classifying various behaviors, including standing, walking, lying down, and feeding. While the model demonstrates proficiency in identifying feeding, lying down, and standing behaviors, there is still room for improvement in accurately recognizing walking behavior. This research contributes to advancing behavior monitoring and estrus detection techniques, providing a way for improved AI practices in the cattle industry.
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Acknowledgements
The first author acknowledges the scholarship under the Thailand Advanced Institute of Science and Technology and Tokyo Institute of Technology (TAIST-Tokyo Tech) Program, awarded by the National Research Council of Thailand (NRCT), National Science and Technology Development Agency (NSTDA), and Sirindhorn International Institute of Technology (SIIT), Thammasat University.
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Kanjanarut, P., Pannakkong, W., Olapiriyakul, S., Sanglerdsinlapachai, N., Hasegawa, S. (2023). A Computer Vision-Based Framework for Behavior Monitoring and Estrus Detection Through Dynamic Behavioral Analysis. In: Honda, K., Le, B., Huynh, VN., Inuiguchi, M., Kohda, Y. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2023. Lecture Notes in Computer Science(), vol 14376. Springer, Cham. https://doi.org/10.1007/978-3-031-46781-3_11
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