• Sakshi Jadhav
• Madhavi Jadhav
• Aryan Jagtap
• Pranita Katakdhond
• Prof. Alka Kumbhar

Efficient livestock management plays a vital role in modern agriculture, where accurate breed identification is essential for breeding, health monitoring, and conservation planning. Conventional ruminant breed identification techniques depend heavily on manual observation, which is time-consuming, subjective, and prone to human error. This paper proposes an automated image-based ruminant breed classification system using morphological feature extraction and machine learning techniques. The system follows a structured pipeline consisting of image acquisition, preprocessing, segmentation, feature extraction, and classification. Morphological features such as shape attributes, color distribution, and texture patterns are extracted using digital image processing methods. These features are used to train machine learning classifiers including Support Vector Machines, Random Forest, and Convolutional Neural Networks. Experimental evaluation demonstrates that deep learning-based classifiers achieve higher accuracy compared to traditional machine learning models. The proposed system provides a scalable, cost-effective, and objective solution for automated breed identification, supporting efficient livestock management and agricultural decision-making.

Machine Learning, Image Processing, Morphological Features, Ruminant Breed Classification, CNN

"Image-Based Morphological Feature Extraction for Ruminant Breed Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.a818-a819, January-2026, Available :http://www.jetir.org/papers/JETIR2601095.pdf

"Image-Based Morphological Feature Extraction for Ruminant Breed Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. ppa818-a819, January-2026, Available at : http://www.jetir.org/papers/JETIR2601095.pdf