A WEB APPLICATION CHATBOT FOR NATURE AND WILDLIFE SPECIES IDENTIFICATION USING PALIGEMMA AS THE VISION-LANGUAGE MODEL
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Universiti Malaysia Sarawak (UNIMAS)
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Biodiversity is essential for maintaining ecological balance, yet identifying and understanding different species can be challenging, especially for non-experts. With artificial intelligence (AI) transforming the way we tackle complex problems, this project explores how AI can make nature identification more accessible. It introduces a web application powered by PaliGemma, a state-of-the-art vision-language model developed by Google, designed to help users identify plants and animals simply by uploading images and prompting questions. The motivation behind this project is the need for an easy-to-use and efficient tool that supports researchers, educators, and the general public in learning about nature and wildlife. Traditional species identification methods often require specialized knowledge, making them difficult for many people to use. This project aims to bridge that gap by providing an AI-driven solution that delivers accurate species information, including names, classifications, and habitats. The main goals of this project are to develop a user-friendly web application, integrate PaliGemma for precise species recognition, and assess the system’s real-world effectiveness. While the project prioritizes accuracy, usability, and accessibility, it also acknowledges limitations such as integrating AI into a web platform, the model’s difficulty in recognizing rare species, and potential issues with training data and image quality. Following a modified waterfall approach with iteration between phases, the development process includes requirement analysis, system design, implementation, testing, and maintenance. The expected outcome is a reliable and interactive tool that not only simplifies species identification but also fosters curiosity and appreciation for biodiversity. By leveraging AI, this project makes learning about nature more engaging and accessible, contributing to the broader field of AI-driven conservation technology.