Banana Leaf Disease Detection Using Glcm Based Feature Extraction And Classification Using Deep Convoluted Neural Networks (Dcnn)

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T. Mahendran and K. Seetharaman


In India about 70% of population rely on yield from agriculture. The plants, leaves have been affected by the disease caused by insects that transfers the infection to other plants in agriculture. During the infection of these diseases the production has been decreased in the farm. Therefore it is necessary to identify these infections at the earliest. The disease detection in banana has turned out be more perplexing in the farm. The banana plant disease detection through image processing becomes more efficient also it is highly essential for farmers in evaluating the plant growth without any manual support economically. This paper proposed leaf disease detection by feature extraction using GLCM and Deep convoluted neural network (DCNN) based classification. The dataset has been collected based on pre-historic dataset of cultivation field and this data has been processed. The features have been extracted using GLCM and the image has been classified using DCNN. Then finally the disease affected area has been identified by extracting features and this feature has been classified for enhancing the accuracy of detecting the disease so we use GLCM-DCNN. Then the simulation results shows the accuracy, recall, precision and f-1 score as the parameters of proposed method. This technique will detect the disease early and helps the farmer to induce pesticides to avoid spreading of disease.

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