PAPILLECT: AI & Deep Learning-Driven Butterfly Detection & Classification

Butterflies play a vital role in maintaining ecological balance and serve as important indicators of biodiversity and environmental health. However, the manual identification and classification of butterfly species can be challenging due to their diverse patterns, similar appearances, variations in wing colors, and environmental conditions. To address these challenges, this research proposes PAPILLECT, an Artificial Intelligence (AI) and Deep Learning-driven system for automated butterfly detection and classification. The proposed system utilizes computer vision techniques and deep learning models to identify butterflies from digital images and classify them into their respective species. Image preprocessing techniques are applied to improve image quality and enhance relevant features, while convolutional neural networks (CNNs) are used to learn distinctive characteristics such as wing patterns, colors, shapes, and textures. The system aims to provide accurate, efficient, and consistent butterfly identification with reduced human intervention. Furthermore, PAPILLECT can support biodiversity monitoring, ecological research, educational activities, and butterfly conservation initiatives by enabling faster analysis of butterfly images. The performance of the proposed approach can be evaluated using metrics such as accuracy, precision, recall, and F1-score. By integrating AI, computer vision, and deep learning, PAPILLECT offers a promising approach to automated butterfly species recognition and contributes to the development of intelligent tools for biodiversity assessment and conservation.