Butterfly Species Classification Using CNN and VGG16

Abstract

Butterflies play a significant part in biodiversity, serving as markers of biological system wellbeing and subjects of environmental and preservation inquire about. With the developing decay of butterfly populaces around the world, robotized methods for precise classification of butterfly species have ended up fundamental for checking and conservation. This consider investigates the utilize of Convolutional Neural Systems (CNNs), with a centre on the VGG16 engineering, for butterfly species classification. Leveraging its profound design and little convolutional channels, VGG16 viably captures complicated designs in butterfly wing pictures, such as colour, surface, and auxiliary subtle elements. The dataset utilized incorporates high-resolution pictures handled through procedures like enlargement and normalization to upgrade show execution. Test comes about illustrate that VGG16 accomplishes tall exactness, outflanking conventional strategies by viably tending to challenges such as intra-class likeness and inter-class inconstancy. This approach contributes essentially to biological observing, helping preservation endeavours and progressing investigate in computer vision- based biodiversity analysis.