Abstract
Heat detection of cattle in video is essential for dairy farm. A cow should be inseminated within a certain period of time in order for it to breed successfully. After it has given birth to a calf, it produces milk. This paper proposes the use of a set of discriminative features to detect cattle in heat, where the features were extracted from the behaviours of oestrus cow by a key-point analysis of locations of their body parts in a video. We evaluated our proposed features, in terms of the algorithm’s classification accuracy of identifying cow in heat, with several machine learning algorithms for two instances–using a global model and a number of cattle-specific models to execute the identification. It was found that Support Vector Machine with Radial Basis Function yielded a maximum accuracy of 90.0% for the global model and 92.0% for the cattle-specific models. These initial findings demonstrate that individual cows may have different oestrus behaviours, a fact that would benefit any dairy farmers. Our future development will be on a practical video monitoring and detection system of cows in heat in a dairy farm.
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Pasupa, K., Lodkaew, T. (2019). A New Approach to Automatic Heat Detection of Cattle in Video. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_35
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DOI: https://doi.org/10.1007/978-3-030-36802-9_35
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