Steganography using AES Algorithm matlab project S.To know what control factors to consider next year to overcome similar losses, it is of great significance to analyze what is being observed. However, detection of defects in the fruits using images is still problematic due to the natural variability of skin color in different types of fruits, high variance of defect types, and presence of stem/calyx. A lot of work has been done to automate the visual inspection of the fruits by machine vision with respect to size and color. An early detection of fruit diseases can aid in decreasing such losses and can stop further spread of diseases. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. The image processing based proposed approach is composed of the following steps in the first step K-Means clustering technique is used for the image segmentation, in the second step some features are extracted from the segmented image, and finally images are classified into one of the classes by using a Support Vector Machine. In this project, a solution for the detection and classification of fruit diseases is proposed and experimentally validated. Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide.
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