Abstract
In this paper, convolutional neural network object detection technology has been used to detect pig breeds with high precision from images captured through mobile cameras. The pretrained model is retrained on several images of 6 different pure breed pigs obtained from organized farms. The Faster R-CNN Inception-ResNet-v2 model has been used in transfer learning fashion for the above task. The training accuracy of this model is 100%, and the testing accuracy of this model is 91% with a confidence level of 94%. From the results achieved, it is noted that this model has produced better results compared to detection accuracy on other datasets like dog dataset, flower dataset, etc.
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Acknowledgements
The authors would like to thank ITRA-Digital India Corporation (formerly known as Media Lab Asia), Ref. No.: ITRA/15(188)/Ag&Food/ImageIDGP/01 dated 09/11/2016 for funding this research work. The authors would also like to thank Dr. A. Bandopadhyay, Senior consultant, ITRA Ag&Food, Dr. Santanu Banik, Principal scientist, Animal Breeding, NRC on Pig, Assam, Dr. Arnab Sen, Head, Animal Health, ICAR research complex for NEH, Barapani, Dr. Binay Singh, Scientist, ICAR-RC for NEH Region, Tripura Center, Agartala and Dr. Dilip Kumar Hazra, Assistant Professor, Dept. of Agronomy, faculty of agriculture, Uttar Banga Krishi Viswavidyala, Coochbihar, for helping us to implement this research work.
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Ghosh, P., Mustafi, S., Mukherjee, K., Dan, S., Roy, K., Mandal, S.N. (2021). Pig Breed Detection Using Faster R-CNN. In: Bhattacharjee, D., Kole, D.K., Dey, N., Basu, S., Plewczynski, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Advances in Intelligent Systems and Computing, vol 1255. Springer, Singapore. https://doi.org/10.1007/978-981-15-7834-2_19
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