InceptGI: a ConvNet-Based Classification Model for Identifying Goat Breeds in India

In this paper, an attempt has been made to develop a model to decide with precision the breed identity of individual goat by using its image. For image-based multi-class classification tasks, CNNs have been found to be the best tool. But selecting the most efficient CNN model for a particular classification scenario is a very difficult job. To find an optimal CNN model for goat breed prediction, we have compared two of the most popular pre-trained deep-learning-based CNN models (VGG-16 & Inception-v3) based on their performance. Both the models have been fine-tuned using transfer learning on the goat breed database. This goat breed database has been created from goat images of six different breeds, which have been captured from different organized registered goat farms in India and almost two thousand digital images of individual goat have been captured without imposing stress to animals. It has been observed that Inception-v3 has outperformed VGG-16 with higher accuracy and lower training time. To measure the prediction performance of this fine-tuned Inception-v3 model, it has been applied to a test set of pure breed goat images and standardized classification performance evaluation metrics have been used to evaluate the prediction results. From the results, it is established that the proposed method used in this paper is able to accurately classify (recognize) goat breeds with high accuracy. Finally, comparison has been made with prediction accuracies of different technologies used for identification of domestic animals.