Comparison of Deep Learning Techniques for Classification of the Insects in Order Level With Mobile Software Application

Abstract

Insects are a class of the arthropod branch and the most crowded animal group in terms of species and taxonomy. Due to destruction and forest fires, some insect species could go extinct without being detected. Identifying new insects and having knowledge about insects in terms of biodiversity will contribute positively to the studies carried out, especially in entomology, agriculture, the pharmaceutical industry, medicine, robotics, and other branches. In this study, we produced a mobile-based decision support software with a deep learning model to classify and detect insects at the order level. We also presented the comparative analysis results of SSD MobileNET, YoloV4, and Faster R-CNN InceptionV3 deep learning methods and adapting processes for order-level insect classification. Our approach studies the suitability of existing models towards such an objective, and we conclude that Faster R-CNN InceptionV3 performs the best at classifying and detecting insects at the order level. In addition, we shared 25820 training and 1500 test data in the kaggle database in order to contribute studies to be carried out in this area. As a result, we believe that this research will be beneficial to entomologists, naturalists, and other researchers in related fields.

Keywords

• Artificial intelligence
• computers and information processing
• insect classification