Multiclass Support Vector Machine based Plant Leaf Diseases Identification from Color, Texture and Shape Features

摘要

In general, Indian economy highly depends on agricultural productivity. In the agricultural field, the identification and classification of leaf diseases play an important role. In developing countries, physical observation of plant leaf diseases can be prohibitively expensive due to the naked eye observation. The proposed research work has developed a framework to identify and classify different plant leaf diseases using K-means segmentation with a multiclass support vector machine (SVM) based classification. The proposed framework is implemented in four steps, step I performs the RGB to HSI colour transformation. In step-II, image segmentation using K-means clustering is performed. Next, colour, texture and shape features are extracted in step III. Finally, in step-IV, multiclass SVM is used for the extracted feature classification. Experimental results indicate that the proposed approach results in an improved detection and classification compared to other existing methods. Efficiency of the proposed algorithm recognizes the accuracy of leaf diseases at about 95.7%.