01810nas a2200241   4500000000100000000000100001008004100002260001200043653002100055653001900076653001700095653002200112653002000134100002300154700002400177700003400201245009000235856009800325300000900423490000600432520111600438022001401554       2016                            d  c06/201610aImage Processing10aClassification10aPlan Disease10aFeature Selection10aExperimentation1 aJagadeesh D.Pujari1 aRajesh Yakkundimath1 aAbdulmunaf. Syedhusain Byadgi00aSVM and ANN Based Classification of Plant  Diseases Using Feature Reduction Technique  uhttp://www.ijimai.org/journal/sites/default/files/files/2016/05/ijimai20163_7_1_pdf_75740.pdf  a6-140 v33 aComputers 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.  a1989-1660