Earticle

초록

영어

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.

목차

Abstract
1. INTRODUCTION
2. RELATED WORKS
2.1 Calf Diseases
2.2 GLCM Algorithm
2.3 Convolution Neural Networks
3. PROPOSAL MODEL
3.1 Method
3.2 Dataset
4. ANALYSIS
5. CONCLUSION
REFERENCES

키워드

저자

• Dong-Oun Choi [ Professor, Department of Computer Software Engineering, Wonkwang University, Korea ]
• Meng Huan [ PhD, Department of Information Management, Beijing University, China ]
• Yun-Jeong Kang [ Assistant Professor, Division of Liberal Arts, Wonkwang University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380