SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique

TitleSVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique
Publication TypeJournal Article
Year of Publication2016
AuthorsD.Pujari, J., R. Yakkundimath, and A. S. Byadgi
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueRegular Issue
Volume3
Number7
Date Published06/2016
Pagination6-14
Abstract

Computers have been used for mechanization and automation in different applications of agriculture/horticulture. The critical decision on the agricultural yield and plant protection is done with the development of expert system (decision support system) using computer vision techniques. One of the areas considered in the present work is the processing of images of plant diseases affecting agriculture/horticulture crops. The first symptoms of plant disease have to be correctly detected, identified, and quantified in the initial stages. The color and texture features have been used in order to work with the sample images of plant diseases. Algorithms for extraction of color and texture features have been developed, which are in turn used to train support vector machine (SVM) and artificial neural network (ANN) classifiers. The study has presented a reduced feature set based approach for recognition and classification of images of plant diseases. The results reveal that SVM classifier is more suitable for identification and classification of plant diseases affecting agriculture/horticulture crops.

KeywordsClassification, Experimentation, Feature Selection, Image Processing, Plan Disease
DOI10.9781/ijimai.2016.371
URLhttp://www.ijimai.org/journal/sites/default/files/files/2016/05/ijimai20163_7_1_pdf_75740.pdf
AttachmentSize
ijimai20163_7_1.pdf1.43 MB