Indian Journal of Science and Technology
Year: 2020, Volume: 13, Issue: 32, Pages: 3295-3314
S Jeyalakshmi1*, R Radha2
1Research Scholar, Research Department of Computer Science, SDNB Vaishnav College for Women, Chromepet, Chennai, 600044, Tamil Nadu, India
2Associate Professor, Research Department of Computer Science, SDNB Vaishnav College for Women, Chromepet, Chennai, 600044, Tamil Nadu, India
email: [email protected]
Received Date:04 June 2020, Accepted Date:01 August 2020, Published Date:01 September 2020
Objectives: To make automatic classification of diseased potato and grape leaf from normal potato and grape leaf. Methods: Experimental sample size of 3000 and 4270 Potato and Grape leaf images were used respectively. The diseased and healthy leaf image samples were taken from PlantVillage dataset. The color features viz., average Red, Green, Blue and Hue intensities of Lesion region were calculated. Features namely Contrast, Dissimilarity, Homogeneity, Energy, Correlation, ASM, and Entropy were extracted from hue lesion region. Also, histogram features such as mean and standard deviation were extracted from hue infected region. Then, data normalization was done on feature set to bring all features into a common scale. Finally, Naïve Bayes, K Nearest Neighbor and Support Vector Machine Classifiers were applied on the above said feature sets. Findings: The Dataset was split in the ratio of 80% and 20% for training and test sets. The classifiers NB, KNN and SVM classified Potato leaves with an accuracy of 88.67%, 94.00% and 96.83% respectively and Grape leaves with an accuracy of 81.87%, 93.10% and 96.02% respectively. For both the species, SVM classifier gave the highest accuracy. Also, it was found that the proposed method performs well as compared with the related works in the literature. Novelty/Applications: An effective feature extraction method to classify grape and potato diseases was proposed in this research work. Also, it was found that the proposed method performs well as compared with the related works in the literature.
Keywords: RGB color space; HSV color space; histogram; color features; grey-level co-occurrence matrix; texture features
© 2020 Jeyalakshmi & Radha. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee).
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