<![CDATA[This research focuses on the development of an interactive educational Android application for animal classification based on their diet: herbivores, carnivores, and omnivores. The application leverages advances in deep learning and computer vision to analyze input images, distinguish morphological characteristics relevant to diet, and accurately identify and classify animal species. By adopting Agile methods, the project ensures flexibility in adapting to changing needs, enhances team collaboration, and enables iterative feature delivery with rapid feedback. The application is designed to engage users, particularly children and adolescents, through two main features: a "reading" menu that presents detailed and engaging visual information about animals and their dietary classifications, and an interactive "quiz" menu to test user understanding. This approach addresses the limitations food type data, and successfully trains and tests classification models. This success confirms the app's potential as an effective and engaging learning tool for introducing animal classification concepts, while of manual identification methods and the lack of relevant interactive educational tools in the current educational landscape. Test results indicate that the app performs well in managing animal and also supporting wildlife monitoring and conservation efforts by providing an accessible and user-friendly platform.]]>
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