Walnut Leaf Disease Identification with Multi-Level CNN Architecture

By combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) approaches, this study presents a new way to forecast which diseases would strike walnut leaves. The first of the study's four stages is the careful gathering and preparation of data to create a varied and consistent dataset. The next step is to train the model and extract features using a convolutional neural network (CNN) architecture with three max-pooling layers, one fully connected layer, and three convolutional layers. This architecture works in tandem with support vector machines (SVMs) to facilitate multi-class classification and extract complex patterns related to diseases. When optimizing and integrating models, keep in mind that combining CNN and SVM strengths can improve illness prediction accuracy. The model assessment and performance analysis concludes by evaluating the model's effectiveness in illness prediction using a variety of indicators and comparison studies.