International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Classification of Livestock Diseases Using GLCM and Artificial Neural Networks

  • Choi, Dong-Oun (Department of Computer Software Engineering, Wonkwang University) ;
  • Huan, Meng (Department of Information Management, Beijing University) ;
  • Kang, Yun-Jeong (Division of Liberal Arts, Wonkwang University)
  • Received : 2022.10.02
  • Accepted : 2022.10.09
  • Published : 2022.11.30
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Abstract

In the naked eye observation, the health of livestock can be controlled by the range of activity, temperature, pulse, cough, snot, eye excrement, ears and feces. In order to confirm the health of livestock, this paper uses calf face image data to classify the health status by image shape, color and texture. A series of images that have been processed in advance and can judge the health status of calves were used in the study, including 177 images of normal calves and 130 images of abnormal calves. We used GLCM calculation and Convolutional Neural Networks to extract 6 texture attributes of GLCM from the dataset containing the health status of calves by detecting the image of calves and learning the composite image of Convolutional Neural Networks. In the research, the classification ability of GLCM-CNN shows a classification rate of 91.3%, and the subsequent research will be further applied to the texture attributes of GLCM. It is hoped that this study can help us master the health status of livestock that cannot be observed by the naked eye.

Keywords

Acknowledgement

This paper was supported by wonkwang University in 2022.

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