Insect Identification System using Faster RCNN with ADAM optimizer Segmentation model

Abstract

 Researchers have been more interested in automated insect recognition in recent years, and many different approaches have been taken to studying the practical implications of this field. When it comes to designing pest management tactics and safeguarding beneficial insects, accurate identification of the insects at play is crucial. Insect target detection has always relied heavily on artificial identification methods; however, deep learning can automatically extract characteristics for detection, solving the issue of poor detection accuracy due to subjective considerations. Here, we present our work on an improved method of insect identification using segmentation-based visual cues. The bug images were segmented using Faster RCNN with the ADAM optimizer, and the features were extracted using InceptionV3. The findings show that our suggested model outperformed competing methods in terms of accuracy and performance.