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@article{154189,
        author = {Ms. Mercelin Francis and Dr. C. Deisy},
        title = {Image based Feature Extraction and Segmentation using k-means for Disease Diagnosis in Agricultural Plants},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {7},
        pages = {86-95},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154189},
        abstract = {This paper is intended to perform an analysis on various segmentation methods applied on an image, acquired from a crop field for effective detection of the object (leaf / disease spot). Traditionally disease diagnosis in agricultural plants is based on naked eye observations, which may vary depending on the individual experts experience and their visual perception. Thus traditional method is time consuming and is expensive. These problems faced can be resolved by developing automated or semi-automated systems to detect and classify diseases based on the symptoms found. Automated and semi-automated systems utilizes various steps in digital image processing namely image acquisition, preprocessing, segmentation, feature extraction and finally using an appropriate machine learning algorithm for classification. For effective classification, unique feature or a combination of features needs to be identified, which mainly rely on an effective segmentation algorithm, for extracting or clustering the similar pixels forming the target object (foreground) from the background. Analysis of various segmentation algorithms are performed highlighting its role in diagnosis and classification of any type of disease found in agricultural plants},
        keywords = {Image Preprocessing, Image Segmentation, Feature Exatraction, Image Classification},
        month = {},
        }