Pest Detection Using Image Denoising and Cascaded Unet Segmentation for Pest Images

Abstract

This study proposes a novel approach for pest detection in pest images using image denoising and cascaded UNET segmentation. The proposed approach involves the use of a hybrid neural network RESNET50 with CNN for image dataset training, optimized using Believed Adam Optimization. The images are then preprocessed using a superior MLP model for image denoising, which enhances the image quality and reduces the noise present in the image. The images are then passed through an adaptive UNET architecture for image segmentation, which is based on domain adaptation and semantic segmentation. The cascaded UNET segmentation improves the segmentation accuracy, and the domain adaptation ensures that the model can be applied to new datasets without requiring additional training. The proposed approach achieves a high accuracy rate of 98.5% in detecting pests images. This approach can be used in various applications related to pest detection and management, including agriculture and pest control.