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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64606
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
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.
Department of Computer Engineering
Ajeenkya D Y Patil School of Engineering Lohegaon, Pune
Department of Computer Engineering
Ajeenkya D Y Patil School of Engineering Lohegaon, Pune
Department of Computer Engineering
Ajeenkya D Y Patil School of Engineering Lohegaon, Pune
Department of Computer Engineering
Ajeenkya D Y Patil School of Engineering Lohegaon, Pune
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
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
[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
Publication Date
6/6/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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