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Leaf Disease Classification using SVM Classifier in Cloud
Author(s) -
R. S.,
Kumar Parasuraman,
Peter Darwin
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a5344.119119
Subject(s) - segmentation , pattern recognition (psychology) , artificial intelligence , support vector machine , classifier (uml) , feature extraction , computer science , image segmentation
In this modern era the clinical laboratory have greater attention to produce an accurate result for every test particularly in the area of leaf disease. The leaf disease is very essential to detect. For the identification of leaf disease three phases are used. First phase is the segmentation and the segmentation used here is the Otsu’s threshold based segmentation. While using the Otsu’s threshold based segmentation we get better result when compared to the previous method. Second phase is the feature extraction here the feature is extracted using the ABCD feature. And the third or final phase is the classification. SVM classifier which is used to categorize the leaf disease separately. The simulations are done on MATLAB application.

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