AI BASED BUTTERFLIES CLASSIFICATION SYSTEM

The diversity and intricate patterns of butterflies make them fascinating subjects for biological research and conservation efforts. In this paper, we present an AI-based butterflies classification system utilizing deep learning techniques for taxonomic identification. Our system harnesses convolutional neural networks (CNNs) trained on a large dataset of butterfly images to accurately classify different butterfly species based on their unique morphological features. We evaluate the performance of our model using standard metrics and demonstrate its effectiveness in accurately identifying various butterfly species. Overall, Butterflies are important from aesthetic, ecosystem, educational, health, economic, scientific and intrinsic value in Malaysia. Thus, a study of butterfly species identification using image processing technique and Convolution Neural Network (CNN) is proposed. Four species of butterflies which that are commonly found in Asia which is Black Veined Tiger, Chocolate Grass Yellow, Grey Pansy and Plain Lacewing used in thisresearch.