An optimal hybrid multiclass SVM for plant leaf disease detection using spatial Fuzzy C-Means model

In recent times, plant disease detection and diagnosing measures become a major concern in the agriculture field. Earlier identification of plant disease aids the farmers to take precautionary measures thereby preventing the spread of disease to other parts of plants. Based on the severity of the diseases, the plant may undergo an attack from mild to complete destruction. So, to avoid such destruction, plant diseases should be detected at the initial stage. Therefore, this paper proposes a novel hybrid random forest Multiclass SVM (HRF-MCSVM) design for plant foliar disease detection. To improve the computation accuracy, the image features are preprocessed and segmented using Spatial Fuzzy C-Means prior to the classification process. The Plant Village dataset used consists of a total of 54303 healthy and diseased leaf images. Finally, the performance metrics like accuracy, F-measure, specificity, sensitivity, and recall value were evaluated to determine the effectiveness of the system. The proposed HRF-MCSVM method is compared with a few existing techniques to determine its efficiency.