Major nocturnal Pest classification model using Faster RCNN architecture of Deep learning

Abstract

Agriculture research improves the quality and quantity of crops, but pests degrade them. Pesticides are used to prevent these pests from reproducing. However, excessive pesticide use is extremely detrimental to both production and the environment. As a result, initial pest detection is required. We analyzed the most frequently used methodologies in order to determine the most appropriate technique for the first diagnosis and early detection of significant nocturnal flying pests such as White Grub, Helicoverpa, and Spodoptera. We identified and analyzed three frequently used deep learning meta-architectures (Faster R-CNN, SSD Inception, and SSD Mobilenet) for object detection using a small Pest dataset. The faster RCNN meta-architecture outperforms other meta-architectures. To address the issue of class imbalance, we used image augmentation with a Faster RCNN meta-architecture. The proposed work demonstrates how to classify Nocturnal Pests using a Faster RCNN of Deep Learning with a better accuracy performance on a limited dataset and utilization as decision-making tool based on classified results.