Principal component analysis in pig breeds identification

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  • SANKET DAN Kalyani Government Engineering College, Kalyani, Nadia, West Bengal 741 235 India
  • SATYENDRA NATH MANDAL Kalyani Government Engineering College, Kalyani, Nadia, West Bengal 741 235 India
  • PRITAM GHOSH Kalyani Government Engineering College, Kalyani, Nadia, West Bengal 741 235 India
  • SUBHRANIL MUSTAFI Kalyani Government Engineering College, Kalyani, Nadia, West Bengal 741 235 India
  • SANTANU BANIK ICAR-National Research Centre on Pig, Rani, Guwahati, Assam


Breed identification, Confusion matrix, Euclidean distance, Image space, Principal components


Maintaining the purity of pig breeds is an essential task for their economic value. The traditional breed identification methods through coat colour are prone to error due to huge intra-breed variation. This paper uses principal component Analysis (PCA) to classify the pig breeds using their images. Individual images of five different pure breeds were captured from organized farms in India under both controlled and uncontrolled environments. Three different image sets were created, containing images in the controlled, uncontrolled, and mixed environment image sets. With 80:20 training to testing datasets, 93% accuracy was found in the proposed method of principal component analysis. Finally, two performance-based comparative analyses of our method were done with PCA-based methods and other renowned techniques used for animal breed identification, wherein our PCA method outperformed others in both comparative scenarios.


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How to Cite

DAN, S., MANDAL, S. N., GHOSH, P., MUSTAFI, S., & BANIK, S. (2023). Principal component analysis in pig breeds identification. The Indian Journal of Animal Sciences, 93(04), 401–405.