Combining CNN and SVM for High-Precision Wheat Disease Classification using Leaf Image Analysis

This study presents a novel classification algorithm designed to accurately identify plant diseases using leaf image analysis. The model employs a hybrid architecture containing a CNN (Convolutional Neural Network) and an SVM (Support Vector Machine) classifier, featuring 2 convolutional layers and 2 max-pooling layers. A variety of datasets encompassing five distinct plant diseases have been utilized for assessment: Stripe Rust, Powdery Mildew, Leaf Rust, Septoria Leaf Blotch, and Fusarium Head Blight. For each disease category, the model can correctly classify a positive case with precision values ranging from 92.45% to 93.75%. Similarly, the recall values are consistent with a range from 91.84% to 94.12%, which were very good too and would be shown that no matter what was taken into account either ring or original brain scans researchers could obtain positive instances well enough using this model resources The model showcases a significant performance, with F1-Score (a balanced metric concerning precision and recall) having high values constant between 92.78% and 93.75%. Support Proportions indicate how much of the overall dataset can be attributed to a given disease class and Support Values show where there are occurrences in each proportion. Its great Weighted Average Accuracy of 93.25% shows that the model is trained on a wide range of plant diseases so it does not only work for one or two datasets but across all datasets. This endto-end macro-average and micro-average evaluation, again only shows all around effectiveness of the model. Results & conclusions: This study provides a highly accurate automated plant disease diagnostic model, which is of great significance for crop management and precision agriculture. If validated through follow-up studies, the findings could improve crop yields and sustainability as well as help develop remote sensing technology that rapidly identifies plant disease.