A Hybrid Approach for Leaf Disease Classification Using Machine Learning and Deep Learning

Natural remedies are less expensive, non-toxic, and associated with negative side effects. As a result, their demand is rising, particularly for herbal-based medicinal products, health products, nutritional supplements, and cosmetics. Threats from leaf diseases exist to the global agricultural industry's economic and production status. The need for farmers to protect agricultural products is reduced by the ability to find illness in leaves utilizing Deep learning (DL) and Machine learning (ML). Our approach involves a combinations method for the diagnosis of flora illness. In our suggested method RESNET-50 is employed for extracting the deep features and Random Vector Functional Link (RVFL) is employed for the classification. To look at the efficiency of the suggested RES-RVFL model, its categorizing performance is contrasted with Support Vector Machine (SVM), Decision Tree, Random Forest and K-Nearest-Neighbors (KNN). The findings showed that RVFL is very suitable for classifying leaf diseases, with a disease classification accuracy of about 94%. The fact that this result highlights the significance of early detection and naming of flora diseases for justifiable cultivation and food security is very positive. Our research has a solid foundation, thanks to the Plant Village dataset, and our findings add to the body of knowledge on applying deep learning and machine learning to identify plant diseases.