Evolving Patterns: A CNN Approach to Butterfly Species Recognition

Identification of several types of butterflies determines both ecological study and conservation of biodiversity. Conventional classification methods based on hand identification are time-consuming, subjective, prone to mistakes because of the enormous variety of butterfly species and visual similarities among them. This research explores high accuracy automated butterfly classification using Convolutional Neural Network (CNN). Training the CNN model on a varied collection of butterfly photos using cutting-edge preprocessing methods including image scaling, normalisation, and data augmentation helped to improve model resilience. The architecture which comprised numerous convolutional, pooling, and fully connected layers optimized with the Adam algorithm. The model evaluated with accuracy of 94.9%, much above widely accepted norms using measurements of precision, recall, F1-score, and accuracy. Although this success, problems such intra class variability, class imbalance, and inter class similarities still exist. This paper highlights CNN’s outstanding accuracy in butterfly classification and offers an automated, fast method for ecological monitoring. Future studies could overcome these challenges and broaden the approach to identify newly discovered environments and species.