Image Based Cattle and Buffalo Breed Recognition

Abstract

With cattle and buffalo populations exceeding 300 million, India stands as one of the largest livestock-holding nations globally. However, accurately identifying specific breeds continues to pose significant challenges for those working in agriculture—from individual farmers to veterinary professionals and livestock management experts. Current identification practices predominantly depend on visual assessment by experienced individuals, which can result in errors, inadequate pedigree documentation, and suboptimal breeding choices that ultimately increase inbreeding coefficients. This research paper examines contemporary developments, persistent obstacles, and real- world implementations of AI-powered breed identification systems that utilize deep learning, computer vision, and mobile-compatible deployment strategies. We analyze cutting-edge convolutional neural network (CNN) architectures, transfer learning methodologies, and feature extraction approaches that have been applied to livestock classification in published research [1]–[3]. The paper also explores the expanding significance of pedigree analytics, with particular emphasis on Coefficient of Inbreeding (COI) calculations, for enhancing overall herd wellness and production efficiency. By thoroughly examining available datasets, model effectiveness metrics, technical infrastructure needs, and practical deployment considerations, this survey demonstrates how an integrated AI platform—one that combines breed identification, pedigree monitoring, and inbreeding risk evaluation—can advance sustainable livestock practices across India. Furthermore, we analyze existing digital livestock management solutions including e- Gopala, MoooFarm, and Stellapps, assessing where they fall short and how contemporary AI techniques could enhance their capabilities. The study concludes by highlighting critical areas requiring additional research, such as insufficient annotated training data, difficulties distinguishing between similar breeds, and barriers to technology adoption in rural communities, while proposing pathways toward developing practical, farmer-oriented image-based breed recognition platforms that are both scalable and accessible.