De-Noising of Apple Fruit Images using Dilated Convolution Neural Network model with Mixed Activation Function

Abstract

In the agricultural sector, fruit diseases contribute to substantial economic losses, emphasizing the need for effective disease detection methods. Image processing techniques play a crucial role in minimizing damage and financial losses. Pre-processing, a vital stage in image processing, involves noise elimination and quality enhancement, laying the groundwork for subsequent tasks such as segmentation, classification, and disease detection. This study focuses on, proposing a novel approach for denoising diseased apple fruit images using the Dilated Convolution Neural Network model with Mixed Activation Function (DCNMAF). Performance evaluation, utilizing metrics like PSNR and SSIM, highlights the superiority of the proposed model over existing methods. These advancements underscore the effectiveness of the proposed DCNMAF model, showcasing notable enhancements in both accuracy metrics.