Development of Application for Forest Insect Classification using CNN

Abstract

Insect identification has the disadvantage that it is difficult for non-experts to carry out due to the specificity of insects. Therefore, it is necessary for the general user to use auxiliary tools such as books to identify insects for education such as ecological learning. In recent years, researches using Deep Learning in fields such as object detection, behavior recognition, voice recognition as well as cancer detection in medical field have been actively conducted and show excellent results. In this paper, we developed a classification application that can be used in mobile phones with high automation and portability to solve the above insect classification problems. Experiments were conducted on 30 insect species selected for observable insects irrespective of environmental factors such as habitat and season, and the transform learning were applied to ResNet, which showed excellent performance in ILSVRC to classify forest insect. Our system achieved an average insect classification accuracy of 94%, an insect classification speed of 0.03 sec, and an insect photo transmission of 0.5 sec to output this information. This has proven that non-experts provide the appropriate performance to use.