AI-Driven Sunflower Disease Multiclassification: Merging Convolutional Neural Networks and Support Vector Machines

This research utilizes a novel Convolutional Neural Network (CNN) and Support Vector Machine (SVM) based model to predict the sunflower diseases. For training the proposed model, three convolutional layers, three max-pooling layers, and two fully connected layers were used, with the second fully connected layer includes SVM. The proposed model is trained with a dataset of different diseases that affect sunflowers. The results of the proposed research study have resulted in a F1 score of 83.45 and a total accuracy of 83.59%. For classifying each disease, accuracy value has been obtained in the range of 80.65% to 85.37%. According to the meta-analysis of the layer parameters, the second fully connected layer highly influences the model’s accuracy. The results indicate that combining CNN and SVM could be an efficient strategy for predicting diseases in sunflowers and would also assist the process of disease management and crop yield.