Abstract—Rice is widely cultivated economical crop in the world. During cultivation the earliest and accurate diagnosis of the rice plant diseases able to reduce the damage, resulting environment protection and better return. In the work, an automated system has been developed to classify the leaf brown spot and the leaf blast diseases of rice plant based on the morphological changes of the plants caused by the diseases. Radial distribution of the hue from the center to the boundary of the spot images has been used as features to classify the diseases by Bayes’ and SVM Classifier. The system has been validated using 1000 test spot images of infected rice leaves collected from the field, gives 79.5% and 68.1% accuracies for Bayes’ and SVM Classifier based system respectively.
Index Terms—Rice leaf disease, Bayes classifier, leaf blast, brown spot, support vector machine, radial hue distribution.
S. Phadikar is with the National Institute of Standards and Technology, Boulder, CO 80305 USA (e-mail: author@ boulder.nist.gov).
J. Sil was with Rice University, Houston, TX 77005 USA. He is now with the Department of Physics, Colorado State University, Fort Collins, CO80523 USA (e-mail: firstname.lastname@example.org).
A. K. Das is with the Electrical Engineering Department, University of Colorado, Boulder, CO 80309 USA, on leave from the National Research Institute for Metals, Tsukuba, Japan (e-mail: email@example.com).
Cite: S. Phadikar, J. Sil, and A. K. Das, "Classification of Rice Leaf Diseases Based on Morphological Changes," International Journal of Information and Electronics Engineering vol. 2, no. 3, pp. 460-463, 2012.