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

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Abstract

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.

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Acknowledgements

The authors are thankful to the AG & Food division of Information Technology Research Academy (ITRA), Media Lab Asia, New Delhi, India, for grant in aid for conducting the research. The authors gratefully acknowledged unconditional support from the Director, ICAR- CIRG Makhdoom, UP and the Joint Director of ICAR-IVRI Eastern Regional Station, Kolkata. For constant encouragement, constructive criticism and scientific input we all acknowledge with reverence the support from Dr. Amitabha Bandyopadhyay, Senior Consultant of ITRA Ag & Food. The Investigating team of the partner institutes–ICAR Research Complex for NEH region, Barapani and Indian Institute of Technology, Guwahati are duly acknowledged for their support. The authors are also thankful to Dr. Sourabh Kumar Das, Principal, Kalyani Government Engineering College for his continuous support.

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Authors and Affiliations

  1. Department of Information Technology, Kalyani Government Engineering College, Kalyani, Nadia, West Bengal, 741235, India

    Satyendra Nath Mandal, Pritam Ghosh, Kaushik Mukherjee, Sanket Dan, Subhranil Mustafi & Kunal Roy

  2. Department of Agronomy, Faculty of Agriculture, UBKV, Pundibari, Cooch Behar, West Bengal, 736165, India

    Dilip Kumar Hajra

  3. ICAR-National Research Centre on Pig, Rani, Guwahati, Assam, 781131, India

    Santanu Banik

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  1. Satyendra Nath Mandal
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  2. Pritam Ghosh
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Correspondence to Satyendra Nath Mandal.

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Mandal, S.N., Ghosh, P., Mukherjee, K. et al. InceptGI: a ConvNet-Based Classification Model for Identifying Goat Breeds in India. J. Inst. Eng. India Ser. B 101, 573–584 (2020). https://doi.org/10.1007/s40031-020-00471-8

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