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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Andrew, William, Sion Hannuna, Neill Campbell and Tilo Burghardt. 2016. Automatic individual Holstein Friesian cattle identification via selective local coat pattern matching in Rgb-d Imagery. 2016 IEEE International Conference on Image Processing (ICIP), 484–88. DOI:

Bajwa, Imran S and S Irfan Hyder. 2005. PCA based image classification of single-layered cloud types. Proceedings of the IEEE Symposium on Emerging Technologies, 2005., 365–69.

Borwarnginn, Punyanuch, Kittikhun Thongkanchorn, Sarattha Kanchanapreechakorn and Worapan Kusakunniran. 2019. Breakthrough conventional based approach for dog breed classification Using CNN with transfer learning. 11th International Conference on Information Technology and Electrical Engineering (ICITEE), 1–5. DOI:

Cai, Cheng and Jianqiao Li. 2013. Cattle face recognition using local binary pattern descriptor. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 1–4. DOI:

Dandil, Emre and Rukiye Polattimur. 2018. PCA-Based Animal Classification System. 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 1–5. Fuad, Md Tahmid Hasan, Awal Ahmed Fime, Delowar Sikder, Md Akil Raihan Iftee, Jakaria Rabbi, Mabrook S Al-Rakhami, Abdu Gumaei, Ovishake Sen, Mohtasim Fuad and Md Nazrul Islam. 2021. Recent advances in deep learning techniques for face recognition. IEEE Access 9: 99112–42. DOI:

Hsu, David. 2015. Using convolutional neural networks to classify dog breeds. CS231n: Convolutional Neural Networks for Visual Recognition [Course Webpage] 2. Huilgol, Purva. n.d. “Accuracy vs. F1-Score.”

Kamencay, Patrik, Tibor Trnovszky, Miroslav Benco, Robert Hudec, Peter Sykora and Andrej Satnik. 2016. Accurate wild animal recognition using PCA, LDA and LBPH. ELEKTRO, 62–67. DOI:

Kumar, Santosh, Amit Pandey, K Sai Ram Satwik, Sunil Kumar, Sanjay Kumar Singh, Amit Kumar Singh, and Anand Mohan. 2018. Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement 116: 1–17. DOI:

Kumar, Santosh, and Sanjay Kumar Singh. 2018. Monitoring of pet animal in smart cities using animal biometrics. Future Generation Computer Systems 83: 553–63. DOI:

Lahiri, Mayank, Chayant Tantipathananandh, Rosemary Warungu, Daniel I Rubenstein and Tanya Y Berger-Wolf. 2011. Biometric animal databases from field photographs: identification of individual zebra in the wild. Proceedings of the 1st ACM International Conference on Multimedia Retrieval, 1–8. DOI:

Mandal, Satyendra Nath, Pritam Ghosh, Kaushik Mukherjee, Sanket Dan, Subhranil Mustafi, Kunal Roy, Dilip Kumar Hajra, and Santanu Banik. 2020. InceptGI: A convnet-based classification model for identifying goat breeds in India. Journal of The Institution of Engineers (India): Series B, 1–12. DOI:

Neethirajan, Suresh and Bas Kemp. 2021. Digital Livestock Farming. Sensing and Bio-Sensing Research 32: 100408. DOI:

Pica, G, U Pica-Ciamarra, J Otte, and others. 2008. The Livestock Sector in the World Development Report 2008: Re-Assessing the Policy Priorities.

Prasong, Pusit and Kosin Chamnongthai. 2012. Face-recognition-based dog-breed classification using size and position of each local part, and Pca.” 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 1–5. DOI:

Ráduly, Zalán, Csaba Sulyok, Zsolt Vadászi and Attila Zölde. 2018. Dog Breed identification using deep learning. IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY), 271–76. DOI:

Ren, Yanli, Xiao Xu, Guorui Feng and Xinpeng Zhang. 2021. Non-interactive and secure outsourcing of PCA-based face recognition. Computers & Security 110: 102416. DOI:

Rishita, Middi Venkata Sai and Tanvir Ahmed Harris. 2018. Dog breed classifier using convolutional neural networks. International Conference on Networking, Embedded and Wireless Systems (ICNEWS), 1–7.

Rodarmel, Craig and Jie Shan. 2002. Principal component analysis for hyperspectral image classification. Surveying and Land Information Science 62(2): 115–22.





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.