Pig Identification Using Deep Convolutional Neural Network Based on Different Age Range

Abstract

Purpose: In this study, the main objectives are to show the performance of deep convolutional neural network in identifying individual pig and investigate the accuracy level of CNN using four datasets made with pig’s face in different growing period. Methods: Firstly, the datasets were captured in an experimental pig barn at a different time. Secondly, the datasets were filtered similar images using the structural similarity index measure (SSIM) for data preparation. Finally, face image classification is performed by employing a deep convolutional neural network (DCNN) namely ZFNet model. Results: The results have shown that individual pig identification is outperformed while using the same age dataset in training and testing stage with an accuracy rate above 97%. Conclusions: The model performed better in a combined dataset which is a combination of all individual data. For future recommendation, it would be beneficial to perform the effectiveness on a large scale of pigs, and a network model should be considered unsupervised learning in case of ageing classification. ? 2021, The Korean Society for Agricultural Machinery.