Abstract
The aim of this paper is to help farmers to identify disease without observing each and every mango plant by eyes. Farmers can use the pesticides according to the leaf disease identified. For identification of this leaf disease K-means clustering and GLCM (Gray level co-occurrence matrix) technique are used. Affected areas of the leaf are identified more accurately by using K-means clustering. GLCM is used to get the texture features which help for identifying the mango leaf disease. The signs of the diseases on the leaf should be identified at the early for the growth of productivity in mango plant, for this leaf disease identification is important.
Highlights
India is the largest producer of “mangoes” which means farmers require frequently monitoring of the large scale of mango crop field
This paper concentrates on anthracnose disease found in mango plant
By K means clustering and Gray level co-occurrence matrix (GLCM) technique the disease is identified at its initial stage
Summary
India is the largest producer of “mangoes” which means farmers require frequently monitoring of the large scale of mango crop field. It is important to identify the proper disease. This will reduce the cost of buying pesticides and eliminate the use of pesticides. This paper concentrates on anthracnose disease found in mango plant. 1. Anthracnose is most common serious diseases in mango plant. Anthracnose is most common serious diseases in mango plant This fungal disease cause the development of dark, sunken spot or lesions. This disease is seed borne and carry over on crop residue in the soil. This fungal is spread in water droplets and worse in warm, humid weather.[1]. K-means clustering is used and GLCM is used for the features extraction
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