AN INTEGRATED APPROACH FOR IMAGE CATALOGUING AND RECOGNITION OF ANIMALS USING MACHINE LEARNING

Abstract

Accurate cataloguing and recognition of animals play a crucial role in biodiversity monitoring, wildlife conservation, ecological research, and intelligent surveillance systems. Traditional methods of manual identification are time-consuming, error-prone, and often require expert intervention, which limits their scalability in large-scale applications. To address these challenges, this study proposes an integrated approach for image cataloguing and recognition of animals using machine learning techniques. The system combines automated image preprocessing, feature extraction, and classification to efficiently identify animal species from large and diverse image datasets. Convolutional Neural Networks (CNNs) and other supervised learning models are employed to learn distinctive visual patterns, while an optimized cataloguing framework organizes recognized images for effective storage and retrieval. Experimental evaluation demonstrates that the proposed method achieves high accuracy in recognizing multiple animal species, even under challenging conditions such as variations in lighting, background, and pose. The integration of cataloguing with recognition not only enhances classification performance but also facilitates efficient data management for ecological studies and smart monitoring systems. This research highlights the potential of machine learning-driven solutions in advancing automated animal identification, supporting conservation efforts, and enabling scalable, intelligent wildlife management systems.