Ear Image-Based Individual Pig Identification By Using Statistical Parameters (Applied Biological Research, 2023)
One of the most popular methods for addressing the shortcomings of traditional  identification methods is biometric-based animal identification. The animal's  phenotypic characteristics, such as its muzzle, retina, ear venation pattern, and  iris, can be used in identification. The most important characteristic for pig  identification is the pattern of venation in the ears. However, it is somewhat  difficult to capture the entire venation web due to the thickness of ear skin. This  research attempted to identify the ‘Yorkshire’ pig using the images of their ears.  ience was also created to block out the external noise. In present  work, a total of 165 images from 5 distinct pigs were captured. The acquired  images were used to collect eight statistical features. Four machine learning s, including Support Vector Machine, Decision Tree, K-Nearest  dom Forest, were used to assess and categorize the obtained  statistical characteristics in order to forecast the specific pig. In comparison to  es, Random Forest classifier showed the best identification  accuracy (90.9%), followed by Support Vector Machine, Decision Tree, and K NearestNeighbor. The technique will help us analyze the vein patterns in pig  ears to identify specific pigs.
Keywords: Animal identification; decision tree; ear image; entropy; random forest