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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM64606

Image-Based Animal Type Classification for Cattle and Buffaloes

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64606

Volume:

10 |

6 |

Jun 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64606

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AP

Avadhut Patil

Department of Computer Engineering

Ajeenkya D Y Patil School of Engineering Lohegaon, Pune

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AM

Avinash Markad

Department of Computer Engineering

Ajeenkya D Y Patil School of Engineering Lohegaon, Pune

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DT

Dnyaneshwar Tupe

Department of Computer Engineering

Ajeenkya D Y Patil School of Engineering Lohegaon, Pune

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Mentors

DNM

Dr. Nilesh Mali

Department of Computer Engineering

Ajeenkya D Y Patil School of Engineering Lohegaon, Pune

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Abstract

With rapid advancements in image acquisition technologies and the increasing use of digital livestock monitoring systems, the need for automated and reliable animal classification has grown. Traditional animal identification is usually done through manual visual checking, which takes a time and can often lead to mistakes or inconsistent results. To solve this problem, the present work describes a deep learning strategy which enables automatic categorize animals as cattle or buffalo using image‑based Convolutional Neural Networks. It extracts key visual features such as facial structure, horn curvature, ear orientation, and body contours. A comprehensive dataset was collected and preprocessed using normalization, resizing, and augmentation techniques to improve generalization. Experimental results show high accuracy, demonstrating the feasibility of automated livestock classification for real‑world dairy and agricultural environments. The system supports smart farm management by offering fast, scalable, and non‑invasive classification capabilities

KEYWORDS

Animal Classification Convolutional Neural Network ResNet50 Deep Learning Computer vision Cattle-Buffalo Identification Image Recognition.

Cite This Article

Avadhut Patil, Avinash Markad, Dnyaneshwar Tupe, Dr. Nilesh Mali, "Image-Based Animal Type Classification for Cattle and Buffaloes," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-7, Jun 2026. https://doi.org/10.55041/IJSREM64606

Full Article/Conclusion

This research confirms that deep learning can be effectively applied for automatic classification of cattle and buffalo using image-based CNN models. The proposed system provides high accuracy, fast prediction speed, and scalable performance, which makes it useful for real-time dairy and agricultural environments. The system can support dairy farms, veterinary clinics, research organizations, and livestock monitoring authorities by reducing manual effort and minimizing human errors. Overall, this automated classification approach improves livestock management and provides a modern solution for smart farming application

References

[1]   Norouzzadeh, M. S. et al., 'Automatic wildlife recognition using deep learning,' PNAS, 2018. [2]   Khosla, A. et al., 'Fine‑grained visual classification using deep networks,' IEEE CVPR, 2015 [3]   Sharma, S. and Jain, M., 'Livestock image recognition using CNN,' IEEE Access, 2022 [4]   Wang, L., 'Machine learning models for animal classification,' ICML, 2021 [5]   Singh, A. and Yadav, R., “Automatic Cattle Identification using YOLOv5 and Mosaic Augmentation: A Comparative Analysis,” arXiv preprint, 2022. [6]   Sarker, M. S. and Rahman, M. M., “A meta- analysis study on body weight prediction method for beef cattle based on digital image processing,” [7]   Rahman, M. M. et al., “Computer- Vision- Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey,” MDPI Sensors, 2024 [8]   Norouzzadeh, M. S. et al., “Automatic wildlife recognition using deep learning,” PNAS, 2018 [9]   Rodrigues, P. et al., “Cattle biometric identification using muzzle pattern images,” Biosystems Engineering, 2019

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Publication Date

6/6/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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