Due to the sophistication of the input images, detecting diseases in plants is now a big image processing challenge. To date, researchers have developed a number of algorithms to assist in this endeavour. Pre-processing, segmentation, feature extraction, and classification are all stages of plant disease detection. We used a textural feature-based technique in this analysis, after which the image was segmented and further classification was performed on the segmented images. The k-mean clustering algorithm is useful for segmenting images and grouping related data into clusters. We replaced the pre-existing SVM classifier with the nave bayes classifier to boost various parametric values such as accuracy, precision, and recall. We used MATLAB to execute our proposed work. In terms of accuracy, precision, and recall, our outcomes review outperforms current methods.
Please see the link:- https://www.ikprress.org/index.php/PCBMB/article/view/5605
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