Abstract

Insect monitoring techniques are often labor-intensive and need significant resources for identifying species after manual field traps. Insect traps are usually maintained every week, leading to a low temporal accuracy of information collected that impedes ecological analysis. This study introduces a handheld computer vision device to attract and detect real insects. The research explicitly proposes identifying and categorizing species by imaging live species drawn to a camera trapping. An Automatic Moth Trapping (AMT) equipped with light elemnets and a camera was developed to draw and observe insects throughout twilight and nocturnal periods. Moth Classification and Counting (MCC) utilizes Computer Vision (CV) and Deep Learning (DL) evaluation of collected pictures and monitors. It enumerates insect populations while identifying moth species. Over 48 nights, more than 250k photos were captured, averaging 5.6k daily. A tailored Convolutional Neural Networks (CNN) was developed on 2000 labeled photos of live insects across eight distinct categories. The suggested computer vision method and methodology have shown encouraging outcomes as an economical option for automated surveillance of insects.