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The productivity of agriculture significantly affects the Indian economy. To increase production in agriculture, plant disease identification should come first. The key to lowering agricultural product output and volume losses is early detection of unhealthy plants. A thorough examination of the plant's outward characteristics is required for the research of diseases that affect plants. For sustainable farming, keeping an eye on the health of the crops is crucial. At best, manually tracking plant disease outbreaks is challenging. The method calls for a tremendous amount of work, knowledge of plant diseases, and processing time.

Early detection is essential since delaying it can have a major negative influence on the output's quantity and quality. Utilizing an automated method will be helpful to monitor crops on large farms as soon as they are evident on the plant's leaves. It is possible to identify plant illnesses as a result by using image processing. The approach for detecting diseases entails a number of image processing phases, such as image capture, pre processing, segmentation, feature extraction, and classification. This study sought to identify plant illnesses using images of plant leaves. This study looked at techniques for identifying plant illnesses by


analysing pictures of the leaves. A few methods for choosing and extracting features in order to identify plant diseases were also covered. Neural networks were employed in this study to identify and categorise leaf diseases. This was done with the help of a piece ofhardware called the AGRI ROBOT.

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