Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image - Saedan, Mohamad Aidiid Hafifi; Kassim, Murizah; Abd Aziz, Azalina Farina (International Journal of Interactive Mobile Technologies (iJIM), 2024)
This study presents the development of a mobile identification system that detects biological butterfly characteristics through deep learning by capturing images. The challenge identified is that butterfly identification and recognition are difficult tasks because there are too many species, and it is hard to classify the types of butterfly species. Butterflies are also difficult to differentiate from each other, and limited studies are done using computer database referrals for butterflies’ characterization. This study aims to develop an automated computer program to easily identify the species of butterflies. Deep learning in image processing is programmed, which can control the qualification, segmentation, and classification of images and automatically detect butterfly characterization. The design system consists of three stages: capture, feature extraction, and butterfly recognition. Then, multiple recognition clues such as shape, color, texture, and size are extracted and analyzed to analyze and recognize the butterfly. This approach is faster and less complex than the previous approach. The result successfully presents a convolutional neural network (CNN) to classify images after training and characterization. The graphics processing unit (GPU) that trains the image dataset presents 86% image accuracy in the allocated time. This research is significant in such a way that new butterfly species will be automatically collected and stored on the online server. The information could be treasured as a valuable butterfly database.