Tomato Plant Diseases Classification Using Statistical Texture Feature and Color Feature

Abstract

Plant disease classification has been associated with the production of essential food crops and human society. In this paper, we classify tomato plant disease using two different features: texture and color. For a texture feature, we extract statistical texture information (shape, scale and location) of an image from Scale invariant Feature Transform (SIFT) feature. As a main contribution, a new approach is introduced to model the Scale Invariant Feature Transform (SIFT) texture feature by Johnson SB distribution for statistical texture information of an image. The moment method is used to estimate the parameters of Johnson SB distribution. The mathematical representation of SIFT feature is matrix representation and too complex to be applied in image classification. Therefore, we propose a new statistical feature to represent the image in few numbers of dimensions. For a color feature, we extract statistical color information of an image from RGB color channel. The color statistics feature is the combination of mean, standard deviation and moments from degree three to five for each RGB color channel. Our proposed feature is a combination of statistical texture and color features to classify tomato plant disease. The experimental performance on PlantVillage database is compared with state-of-art feature vectors to highlight the advantages of the proposed feature.