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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal on Recent and Innovation Trends in Computing and Communication
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
