Plant Disease Detection using ResNet9

Leaf diseases significantly impede plant growth by affecting the foliage.Timely and accurate detection of these diseases can mitigate further harm to the plants.A comprehensive approach requiring significant expertise, effort, and focus is essential for identifying plant leaf diseases through leaf imagery.Research in deep learning (DL) holds substantial promise for enhancing diagnostic accuracy.Recent advancements in deep learning have facilitated improvements in the precision and efficacy of systems designed to identify and evaluate plant leaf diseases.This paper presents an innovative deep learning methodology for disease classification.Our study explores the effectiveness of using the ResNet classifier to analyze characteristics such as color, texture, and the geometric arrangement of plant leaves from images.Various efficiency metrics, which surpass those of existing methodologies, were utilized to assess the accuracy of our approach.These metrics not only aid in the analysis but also support the proposed methodology.The disease detection process includes stages such as image segmentation, classification, noise reduction, and image compilation.