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Malvika Ranjan, Manasi Rajiv Weginwar, Neha Joshi, Prof. A.B. Ingole

Farmers experience great difficulties in changing from one disease control policy to another. Relying on pure naked-eye observation to detect and classify diseases can be expensive. Various plant diseases pose a great threat to the agricultural sector by reducing the life of the plants. The present work is aimed to develop a simple disease detection system for cotton diseases. The symptoms of the attacks are usually distinguished through the leaves, stems or fruit inspection. This proposed system discusses the effective way used in performing detection of plant diseases through leaf feature inspection. Leaf image is captured and proposed to determine the health status of cotton plant. Plant disease diagnosis is an art as well as science. The diagnosis process (i.e. recognition of symptoms and signs), is inherently visual and requires intuitive judgement as well as the use of scientific methods. The work begins with capturing the images. Color feature like HSV features are extracted from the result of segmentation and Artificial neural network (ANN) is then trained by choosing the feature values that could distinguish the healthy and diseased samples appropriately. Experimental results showed that classification performance by ANN taking feature set is better with an accuracy of 80%. The present work proposes a methodology for detecting cotton leaf diseases early and accurately, using diverse image processing techniques and artificial neural network (ANN).