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A New Approach to Automatic Heat Detection of Cattle in Video
Author(s)
Lodkaew, Thanawat
Date Issued
January 1, 2019
Type
Conference Paper
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.
Citation
Communications in Computer and Information Science, 1143 CCIS, 330-337, 2019