Principal component analysis in pig breeds identification

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.