An Integrated Image Processing Approach for Diagnosis of Groundnut Plant Leaf Disease using ANN and GLCM
The plants are highly significiant for human life and animal life. Plants also suffer from illness (i.e., diseases) like humans and animals. Groundnut plant is more prone to diseases in the agriculture sector. Cercospora is the most common leaf disease in the groundnut. Entire plant gets infected by the diseases which include stem, root, flower and leaves. For controlling and managing the diseases human involvement is necessary as it is time consuming for classification and recognition of groundnut leaf diseases. The process is longer and costlier hence an automatic image processing method is adopted. In this paper, the images of groundnut leaves are collected and preprocessed by median filter. The preprocessed images are segmented by multi threshold based color segmentation. These segmented images are fed to feature extraction by Gray Level Co-ocurrance Matrix (GLCM) and feature selection by rough set approach and the leaf diseases are classified by ANN and SVM classifier. Finally the performance measures are made by comparing the accuracy and sensitivity of ANN and SVM classifiers to prove the effectiveness of ANN.
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