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@article{143940,
        author = {K.L.KARTHEEK and T. ANIL RAJU},
        title = {Automatic Detection of Pathogenic Diseases on Leaves},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {3},
        number = {4},
        pages = {105-110},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=143940},
        abstract = {Automatic detection of pathogenic disease in plants is becoming an important research topic in the pattern recognition. It is essential for monitoring large fields of crops, and thus automatically detects the symptoms of diseases as soon as they appear on plant leaves. Many conventional methods are not effective for disease recognition in plants because of the complexity in processing; one way to overcome this problem is to characterize the pixel being classified by parameters measuring the spatial organization of the pixels in its neighborhood. In this study, textural parameters of one-step and two-step encoded images were calculated in the form of a vector. These parameters were subject to the probabilistic neural networks classifier to decide which class image belonging to. Results show that the encoding method is a proper way to compress an image without losing textural information. It reduces the size of data and thereby reducing the computation time characteristics.. The proposed algorithm’s efficiency can successfully detect. },
        keywords = { leaf images, color segmentation using k-means clustering, textural features, co-occurrence matrix.},
        month = {},
        }