Advanced Classification of Butterfly Species through Image Recognition

Abstract

Butterflies play a vital role in ecosystems as pollinators and biodiversity indicators, yet accurately identifying species is challenging due to their morphological diversity. Traditionally, butterfly classification has relied on manual identification by experts, which is time-consuming, requires extensive knowledge, and is prone to human error. These limitations make large-scale butterfly monitoring difficult, affecting biodiversity studies and conservation efforts. This research proposes an automated image recognition system for advanced classification of butterfly species, utilizing to identify species based on visual features. By training the model on a large dataset of butterfly images, the system learns to detect subtle differences in wing patterns, colors, and shapes, enhancing classification accuracy beyond traditional methods. Unlike manual identification, the proposed system operates quickly, enabling real- time recognition and classification. This need for an automated approach arises from the demand for scalable, accurate biodiversity monitoring, especially in regions with high species diversity. The proposed system offers significant improvements in speed, accuracy, and accessibility, making it a valuable tool for ecologists, researchers, and citizen scientists engaged in butterfly studies, biodiversity monitoring, and conservation planning.