Mango Plant Leaf Disease Detection and Recognition using Neural Networks and K-Means Clustering Techniques

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Chandru Jathar, Janapati Venkata Krishna, Meghana G. R.

Abstract

Smart agriculture contributes a lot to the global economy. One of the serious problems that are faced by the agriculturist is identifying the plant disease. Many agricultural solutions are evolving to assist the farmers in improving their crop production. With the advancement of deep learning & neural network many models have emerged to identify the plant diseases. The proposed work presents identifying the illnesses in tapioca and mango leaves. The methodology divides the leaves and stem into two classes as infected and not infected. K-means clustering and Neural Networks are used to classify and group the plant diseases. The working model identifies the five plant related diseases such as early scorch, cottony mould, ashen mould, late scorch, and small whiteness.  The outcomes of the experiment suggest that the proposed methodology is beneficial and may greatly aid in the precise identification of leaf diseases with minimal computational work by providing about 95% of efficiency.

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