IJMTES – FUZZY LOGIC BASED SUGARCANE LEAF DISEASE IDENTIFICATION AND CLASSIFICATION USING K-MEANS CLUSTERING AND NEURAL NETWORK

Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : P DharaniDevi | S Lalithasinegaunnamed

Volume 03 Issue 06 2016

ISSN no:  2348-3121

Page no: 231-235

Abstract – In Indian agriculture, sugarcane is the most important plant. The sugarcane plant plays a vital role in Indian economy. Sugarcane refers to any of several speciesof giant grass in the genus Saccharum that have been cultivated in the tropical climatesin South Asia and Southeast Asia since ancient times. The second largest country in the production of sugarcane is India. The main by-product of sugarcane plant is sugar and it is an essential food item in our day today life.The world produced about 168 million tonnes of sugar in 2011. The farmers are much interested in cultivating sugarcane plant in their farms to gain more profit. Generally, the sugarcane plant will be affected mainly due to lack of natural efficiencies such as sunlight, water and fertility of soil. The main affected area of the sugarcane plant is its leaves. About 15% of sugarcane leaf is infected by various disease, which reduces the quality and quantity of sugarcane production. This is due to various fungal and bacterial disease caused by viruses. This will leads the farmers to spend more time to identify the disease and the cost will be increased to prevent the disease. This paper aims to identify and classify the affected sugarcane leaf automatically at its early stage itself by using Image Processing techniques and MATLAB tools. Early detection and classification of plant disease is used to control these diseases and reduce the severity of infection. This experiment can predict more accurate result.

Keywords – Leaf image, K-means clustering, Feature extraction, Fuzzy logic, Neural network

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