Improved accuracy of pest detection using augmentation approach with Faster R-CNN

Abstract

In agriculture, Pests are decreasing agricultural productivity. Identifying a pest is a challenging process and subject to expert opinion. Nowadays, lots of work carried out for automatic pest detection. It becomes possible because of emerging Deep Learning’s object detection architectures. This paper shows the multi-class pest detection using Faster R-CNN architecture and compared the performance results of image augmentation with focused on the accuracy performance along with small dataset. We have used Horizontal Flip and 90 Degree Rotation augmentation parameters for solving class imbalance problem. We found that trained pest detection model with augmentation options can perform better with an accuracy of 91.02% using Faster R-CNN architecture.