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Image-Based Identification of Animal Breeds Using Deep Learning

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Deep Learning for Unmanned Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 984))

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Abstract

Accurate and reliable breed identification of domestic animals from images is one of the most promising but challenging tasks in intelligent livestock management. Traditional methods for animal breed identification are very costly and time consuming. Therefore, there is a need for a faster and cheaper technique for animal breed identification, which can be used by anyone without much technical knowhow. Deep Learning based animal breed classification from images can be used to solve this problem. Recent developments in deep Convolutional Neural Network (CNN) has drastically improved the accuracy of image recognition systems, but choosing the optimal model for the required task is very important for best performance. In this study, the performance of nine different deep CNN-based models have been analyzed to find the optimal model which can precisely determine the breed identity of individual animals from its image. All nine CNN models have been separately trained end-to-end on Pig Breed Dataset and Goat Breed Dataset using a set of identical hyperparameters. From the results obtained it has been established that MobileNetV2 is the best deep-CNN model for Goat Breed Classification with 95.00% prediction accuracy and InceptionV3 is the best model for pig breed classification with 100.00% prediction accuracy. Breed classification performance of goat and pig obtained in this study have been compared with other techniques used for animal breed classification. Comparison results show that our CNN-based technique has performed on par with all other methods. With these encouraging results, it can be confidently stated that deep CNN-based models can be used for solving the animal breed classification problem with high accuracy and can be used as ready to use technology for intelligent livestock management.

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Abbreviations

BoW:

Bag of Words

SIFT:

Scale-invariant feature transform

SVM:

Support vector machine

ScSPM:

Sparse coding spatial pyramid matching

cLBP:

Cell-structured local binary patterns

DCNN:

Deep convolutional neural network

LDA:

Linear discriminant analysis

CNN:

Convolutional neural network

KNN:

K-nearest neighbors

MP-CNN:

Multi part convolutional neural network

SC-MPEM:

Supervised clustering using multi part CNN

RNN:

Recurrent neural network

FCAN:

Fully convolutional attention networks

FOAF:

Fused one-vesus-all features

MA-CNN:

Multi-attention convolutional neural network

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Acknowledgements

The authors would like to thank ITRA (Digital India Corporation, formerly Medialab Asia), MeitY, Govt. of India, for funding. The authors are grateful to Dr. Amitabha Bandyopadhyay (Senior Consultant, ITRA Ag & Food) and Dr. Dilip Kumar Hazra (Assistant Professor, Department of Agronomy, Uttar Banga Krishi Viswa Vidyalaya) for constant encouragement, constructive criticism, and scientific input. The authors gratefully acknowledge the unconditional support from the Director, ICAR- CIRG Makhdoom, UP; the Joint Director of ICAR-IVRI Eastern Regional Station, Kolkata; ICAR National Research Centre on Pig, Rani, Assam; ICAR Research Complex for NEH Region, Umiam, Meghalaya and ICAR Research Complex for NEH Region, Tripura Centre, Tripura for permitting us to access their organized pig farms. 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, 741235, India

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

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

    Santanu Banik

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  1. Pritam Ghosh
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  2. Subhranil Mustafi
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Correspondence to Pritam Ghosh .

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  1. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Anis Koubaa

  2. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

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Ghosh, P. et al. (2021). Image-Based Identification of Animal Breeds Using Deep Learning. In: Koubaa, A., Azar, A.T. (eds) Deep Learning for Unmanned Systems. Studies in Computational Intelligence, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-77939-9_12

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