Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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Estrus Detection in Sows Based on Texture Analysis of Pudendal Images and Neural Network Analysis

  • Seo, Kwang-Wook (Korean Intellectual Property Office) ;
  • Min, Byung-Ro (School of Life Science and Biotechnology, Sungkyunkwan University) ;
  • Kim, Dong-Woo (School of Life Science and Biotechnology, Sungkyunkwan University) ;
  • Fwa, Yoon-Il (School of Life Science and Biotechnology, Sungkyunkwan University) ;
  • Lee, Min-Young (School of Life Science and Biotechnology, Sungkyunkwan University) ;
  • Lee, Bong-Ki (School of Life Science and Biotechnology, Sungkyunkwan University) ;
  • Lee, Dae-Weon (School of Life Science and Biotechnology, Sungkyunkwan University)
  • Received : 2012.06.26
  • Accepted : 2012.08.30
  • Published : 2012.08.31
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Abstract

Worldwide trends in animal welfare have resulted in an increased interest in individual management of sows housed in groups within hog barns. Estrus detection has been shown to be one of the greatest determinants of sow productivity. Purpose: We conducted this study to develop a method that can automatically detect the estrus state of a sow by selecting optimal texture parameters from images of a sow's pudendum and by optimizing the number of neurons in the hidden layer of an artificial neural network. Methods: Texture parameters were analyzed according to changes in a sow's pudendum in estrus such as mucus secretion and expansion. Of the texture parameters, eight gray level co-occurrence matrix (GLCM) parameters were used for image analysis. The image states were classified into ten grades for each GLCM parameter, and an artificial neural network was formed using the values for each grade as inputs to discriminate the estrus state of sows. The number of hidden layer neurons in the artificial neural network is an important parameter in neural network design. Therefore, we determined the optimal number of hidden layer units using a trial and error method while increasing the number of neurons. Results: Fifteen hidden layers were determined to be optimal for use in the artificial neural network designed in this study. Thirty images of 10 sows were used for learning, and then 30 different images of 10 sows were used for verification. Conclusions: For learning, the back propagation neural network (BPN) algorithm was used to successful estimate six texture parameters (homogeneity, angular second moment, energy, maximum probability, entropy, and GLCM correlation). Based on the verification results, homogeneity was determined to be the most important texture parameter, and resulted in an estrus detection rate of 70%.

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References

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