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


  1. Al-Bashish, D., M. Braik and S. Bani-Ahmad, 2011. Detection and classification of leaf diseases using K -means based segmentation and neural-networks-based classification. Inform. Technol.
  2. Ali, S. A., Sulaiman, N., Mustapha, A. and Mustapha, N., (2009). K-means clustering to improve the accuracy of decision tree response classification. Inform. Technol. J., 8: 1256-1262. DOI: 10.3923/itj.2009.1256.1262
  3. H.Al-Hiary, S. Bani-Ahmad, M.Reyalat, M.Braik and Z.AlRahamneh, Fast and Accurate Detection and Classification of Plant Diseases, International Journal of Computer. Applications (0975-8887), Volume 17- No.1.March 2011
  4. Jayamala K. Patil, Raj Kumar, “Advances In Image Processing For Detection of Plant Diseases” JABAR, vol. 2(2), pp. 135-141, June-2011.
  5. S. Arivazhagan, R. Newlin Shebiah, S. Ananthi, S. Vishnu Varthini, “Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features”, CIGR, vol. 15(1), pp. 211-217, March 2013.
  6. Prof. Sanjay B. Dhaygude, Mr.Nitin P.Kumbhar, “Agricultural plant Leaf Disease Detection Using Image Processing” IJAREEIE, vol. 2(1), pp. 599-602, January 2013.
  7. Arti N.Rathod, Bhavesh A. Tanawala, Vatsal H.Shah, “Leaf Disease Detection Using Image Processing and Neural Network,”International Journal of Advance Engineering and Research Development, vol. 1, pp. 28-31, June 2014.
  8. Sunita I.Naik, Vivekanandreddy, S.S.Sannaki, “Plant Disease Diagnosis System for Improve Crop Yield,” International Journalof Innovations in Engineering and Technology, vol. 4, pp. 198-204, June 2014.