Cow Breed Classification System Using CNN

Shivsangram Kadam, Wagheshwar Bhoske, Aniket Gangode, Aahi Dhar and A. D. Dhawale

International Journal of Scientific Research in Artificial Intelligence and Machine Learning, 2026, vol. 2, issue 3, 238-248

Abstract: Accurate identification of cattle breeds is essential for livestock management, breeding programs, and agricultural productivity. Traditional breed identification methods rely on manual observation and expert knowledge, which are often time-consuming and prone to human error. This paper presents an automated Cow Breed Classification System based on Convolutional Neural Networks (CNN) for recognizing different cattle breeds from digital images. The proposed system utilizes image preprocessing techniques such as resizing, normalization, and enhancement to improve image quality before classification. A CNN model is trained on images belonging to multiple cow breeds such as Gir, Hariana, Jersey, Khillari, and Malvi. TensorFlow and Keras are used for model development, while OpenCV is employed for image preprocessing. Experimental results demonstrate that the proposed model can effectively classify cow breeds with high accuracy, reducing manual effort and supporting smart livestock management. The system provides a scalable and efficient solution for modern agriculture and precision farming applications.

Keywords: Deep Learning; Convolutional Neural Network; Cow Breed Classification; Image Processing; Computer Vision; Smart Farming; Livestock Management; TensorFlow (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML262314
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Persistent link: https://EconPapers.repec.org/RePEc:jbo:ijsrml:v2:y2026:i3:id:77

DOI: 10.32628/IJSRAIML262314

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