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

Author
Keywords
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.
Year of Publication
2016
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
3
Issue
Regular Issue
Number
7
Number of Pages
6-14
Date Published
06/2016
ISSN Number
1989-1660
Citation Key
URL
http://www.ijimai.org/journal/sites/default/files/files/2016/05/ijimai20163_7_1_pdf_75740.pdf
DOI
10.9781/ijimai.2016.371
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