Detection and classification of plant diseases based on spatially kernelized FCM and support vector machine

In nature, plants are an essential resource that are crucial to preserving the ecosystem's equilibrium. Crop safety is significantly impacted by plat diseases. This paper proposes an automated technique for plant disease diagnosis and classification using machine learning. The created processing plan is divided into four primary sections. First, we use spatially kernelized fuzzy C-means (SKFCM) to segment the images and find the pixels that are primarily green in color. After initially applying a masking process to green pixels using specific threshold values calculated with Otsu's method, a significant portion of these pixels are further masked. In a subsequent stage, all of the pixels with zero values for red, green, and blue as well as those on the edges of the infected cluster were eliminated. The final step in the categorization process is the application of support vector machines (SVM). The experimental findings show that the suggested method is a reliable one for identifying illnesses in plant leaves. The effectiveness of the created algorithm allows it to accurately identify and categorize the diseases under investigation with a precision of 83% to 94%.