Image Based Animal Type Classification for Cattle and Buffaloes

Abstract

Image-based Animal Classification has become an important application of Artificial Intelligence in agriculture and dairy farming. The project, Image Based Animal Type Classification for Cattles and Buffaloes, aims to automatically identify whether an uploaded animal image belongs to cattle or a buffalo using Deep Learning techniques. Manual identification often becomes difficult due to similar qualities, especially for new farmers, veterinary staff, and animal management organizations. Incorrect classification may lead to improper data recording, breeding decisions, and health monitoring. To overcome these challenges, this project utilizes Convolutional Neural Networks (CNN) for feature extraction and image classification. The system is developed using Python and Flask as the backend framework, while HTML, CSS, and Javascript provide an interactive web interface. Image preprocessing methods, including resizing, normalization, and data augmentation, enhance the model's robustness and improve its ability to handle variations in input images. The trained CNN model learns distinguishing visual features such as horn structure, skin texture, body shape, and facial features to accurately classify animals. The system provides fast, accurate, and automated classification with minimal human intervention. It reduces manual effort, improves livestock record management, and supports smart dairy farming practices. The developed web application allows users to upload images and instantly obtain prediction results