Smartlivestock: An Efficient CNN-Based Approach for Automated Cattle Breed Identification

MOKKALA KIRAN MONI,
Assistant Professor, Department of CSEAnnamacharya Institute of Technology And Sciences,
Tirupati-517520, A.Pkiranmonireddy@gmail.com
PRASADAM SUPRIYA,
UG Scholar, Department ofCSEAnnamacharya Institute of
Technology And Sciences,Tirupati-517520, A.Psupriyapvr@gmail.com
MANDLA YASWANTHRAHUL,
UG Scholar, Department of CSEAnnamacharya Institute of
Technology And Sciences,Tirupati-517520, A.P
yaswanthrahul143@gmail.com

TANDERI VISHNU PRIYA,
UG Scholar, Department of CSEAnnamacharya Institute of Technology And Sciences,
Tirupati-517520, A.Ppriyapriya125sss@gmail.com
BEEGALA VENKATAHARSHAVARDHAN,
UG Scholar, Department of CSEAnnamacharya Institute of Technology
And Sciences,Tirupati-517520, A.Pbharsha11567@gmail.com

Abstract -- Accurate cattle breed identification plays a vital role in modern livestock management by supporting genetic preservation, efficient breeding practices, and improved productivity. Although deep learning techniques have shown strong performance in image based classification tasks, many existing approachesprimarily emphasize accuracy under controlled conditions, with limited focus on real-world deployment. This paper presents a Visual Intelligence Framework forautomated cattle breed recognition using a transfer learning  approach based on the MobileNetV2convolutional neural network. The proposed system integrates image preprocessing, deep feature extraction,and a confidence-based prediction mechanism to  enhance robustness under varying environmental conditions such as lighting, background complexity, and pose variations. In addition to model performance, the framework prioritizes computational efficiency and deployment feasibility. A web-based interface isdeveloped to enable real-time interaction with the trained  model. Experimental results demonstrate that the system achieves reliable classification performance with low computational overhead, making it suitable for practical livestock monitoring applications. Keywords -- Cattle breed classification, deep learning,transfer learning,MobileNetV2,image-basedrecognition, smart livestock management, agricultural AI.