International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 12 |Dec 2025
p-ISSN: 2395-0072
www.irjet.net
Image based breed recognition system for Cattle and buffalo using deep learning Rajendra Khule1, Komal Wakodikar2, Manthan Domde3, Kunalika Landge4, Vaishali Bisen5, Harsh Bhanarkar6 1Assistant professor, Dept of Electronics and Telecommunication, KDK College of Engineering, Maharashtra, India 23456UG student, Dept of Electronics and Telecommunication, KDK College of Engineering, Maharashtra, India
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Abstract - Image-based recognition of cattle and buffalo breeds has become a key aspect of modern livestock management,
supporting activities such as automated monitoring, genetic improvement programs, disease surveillance, and digital dairy operations. Traditional breed identification depends on visual assessment of external traits like coat pattern, horn structure, and body shape; however, these observations are often subjective and may vary across evaluators. Recent advancements in deep learning—especially Convolutional Neural Networks (CNNs)—have transformed livestock classification by enabling automatic feature extraction, improved robustness to environmental changes, and high accuracy even in natural farm conditions. This review compiles significant research developments in cattle and buffalo breed identification, examining CNN architectures, lightweight models, biometric-based recognition, multi-view approaches, and fusion techniques. Particular attention is given to MobileNetV2 for its computational efficiency, reduced parameter count, and strong performance on limited datasets. The paper also discusses contributions involving YOLO-based detection frameworks, Vision Transformers (ViT), and hybrid CNN–SVM strategies. Overall findings indicate that lightweight CNN architectures deliver superior results in real-time field scenarios, making them well suited for practical deployment on farms. The review concludes by outlining existing challenges and highlighting future research opportunities for building scalable, reliable, and intelligent livestock recognition systems.
1. INTRODUCTION Accurate identification of livestock breeds is essential for effective dairy management, genetic improvement programs, disease monitoring, and animal traceability. Traditionally, breed recognition has depended on observing visible traits such as coat colour, horn configuration, and overall body morphology. However, These manual methods are often subjective and can be unreliable under real farm conditions. Factors like inconsistent lighting; variations in posture, background clutter, and animal movement further decrease the accuracy of human-based identification. With advancements in computer vision, deep learning has become a highly effective approach for automating livestock recognition. Convolutional Neural Networks (CNNs) can learn and extract meaningful features from images without manual intervention, enabling strong classification performance even in challenging environments. Modern lightweight architectures— including Mobile Net, MobileNetV2, and Efficient Net-Lite—are particularly valuable because they support real-time operation on mobile and edge devices, making them well suited for deployment in rural and resource-constrained settings. Researchers have also investigated biometric techniques such as muzzle pattern analysis and multi-view image learning to enhance recognition accuracy. Despite these developments, several challenges persist, including limited availability of large, diverse datasets—especially for buffalo breeds—and the need for models capable of generalizing effectively across uncontrolled farm conditions. This review summarizes current deep learning techniques used for cattle and buffalo breed classification, emphasizes the strengths of lightweight CNN models like MobileNetV2, and highlights existing research gaps to guide future advancements in developing scalable, efficient, and robust livestock recognition systems.
2. LITERATURE REVIEW A. Buffalo Breed Classification Using Improved CNN Pan et al. [1] Developed a self-activated CNN for Neli- Ravi and Khundi buffalo breeds, achieving ~93% accuracy on a small dataset. This demonstrated CNN effectiveness under data-constrained conditions.
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