Abstract: Agriculture plays an especial role in the Indian economy. Since it is one of the major contributors to Gross Domestic Product (GDP) and national income of the country, decrease in the productivity of the crops leads to a major loss. To identify the plant diseases in a short span of time with greater accuracy remains as a major challenge. To overcome this, we provide a software solution for the automatic analysis and detection of plant leaf diseases using Support Vector Machine (SVM) and image processing techniques like image acquisition, image pre-processing, image segmentation, feature extraction and classification with the help of MATLAB. Initially the images of various leaves are fed and then varied image processing techniques are applied to the acquired images to extract useful features and for further analysis and classification. Here specifically we concentrate on diseases like Alternaria alternata, Cercospora leaf spot and Bacterial blight and Anthrocnose. In addition with the detection of the disease, infected area and affected region percentage is also measured. 

Keywords: Feature Extraction, Gray Level Co-occurrence Matrix (GLCM), K-means clustering algorithm, Image Segmentation, Support Vector Machine (SVM) classification

| DOI: 10.17148/IARJSET.2020.7425