Articulo Revista Indexada
SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique
Autor
D. Pujari, Jagadeesh
Yakkundimath, Rajesh
Syedhusain Byadgi, Abdulmunaf
Institución
Resumen
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.