Textual Feature Analysis and Classification Method for the Plant Disease Detection

The technique used for the processing of digital data obtained from pictures is identified as image processing. Plants and crops are ruining because the excessive use of fertilizers and insecticides. The experts observe the plant disease with their naked eye and identify and detect the type of diseases plant is suffering from. In order to identify infections from input pictures, plant disease detection approach is implemented. An image processing approach is implemented in this research study. This approach is relied on the extraction of textural feature, segmentation and classification. The textural features are extracted from the picture with the help of GLCM algorithm. The input picture is segmented with the help of k-mean clustering algorithm. For classification, the Naïve bayes classification is used in this research. This leads to improve accuracy of detection and also leads to classify data into multiple classes. The results of the proposed algorithm are analyzed in terms of various parameters accuracy, precision, recall and execution time. The accuracy of proposed algorithm is increased upto 10 to 15 percent.