Leaf Disease Prediction Using Fast Enhanced Learning Method

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-9
Year of Publication : 2021
Authors : M.Thanjaivadivel, R.Suguna


MLA Style: M.Thanjaivadivel, R.Suguna "Leaf Disease Prediction Using Fast Enhanced Learning Method" International Journal of Engineering Trends and Technology 69.9(2021):34-44. 

APA Style: M.Thanjaivadivel, R.Suguna. Leaf Disease Prediction Using Fast Enhanced Learning Method  International Journal of Engineering Trends and Technology, 69(9),34-44.

More than 75% of world poverty can be reduced by the process of Agriculture. Based on the previous studies, due to the increase in plant disease, 40% of production in agriculture is in the loss. To prevent and control plant diseases, the key link is to detect the disease in the leaf parts. This paper takes tomato leaves with 10 classes, and apple leaves with 4 classes, grapes leave with 4 classes, Corn or maize leaf with 4 classes, and potato leaves with 3 classes as experimental data [18]. The Plant Village dataset available online has been collected for the purpose of training and testing the algorithms. For the process of training, the dataset has been classified into two major classes as healthy and unhealthy. And it was further classified into subclasses based on the category of disease. For the texture-based feature GLCM, haralick texture has been used, and the color histogram is used for color feature extraction, and for shape-based feature Hu moments, Zernike moments have been used. Then the proposed algorithm has been trained to predict the disease and the exact type of the disease the plant got affected with more accuracy. The corn disease has been tested with various existing algorithms like logistic regression, linear discriminant analysis, KNN, Classification and regression tree, Random Forest, Naïve Bayes with an accuracy of 78.0625,77.1875, 75. 5625, 77.75 ,85.3125, 55.375 and 78.125 respectively. Further, the images have been tested using the proposed algorithm as well, which gives an accuracy of 99.76%. Similarly tested for apple, grapes, potato, and corn or maize. When compared with the mentioned existing algorithms, the proposed extreme learning algorithm predicts the disease with more accuracy.

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Image Classification, Leaf Disease, CNN, KMeans clustering, NN Classifier, SVM