Classification of grapefruit peel diseases using color texture feature analysis

Abstract

Technologies that can efficiently identify citrus diseases would assure fruit quality and safety and minimize losses for citrus industry. This research was aimed to investigate the potential of using color texture features for detecting citrus peel diseases. A color imaging system was developed to acquire RGB images from grapefruits with normal and five common diseased peel conditions (i.e., canker, copper burn, greasy spot, melanose, and wind scar). A total of 39 image texture features were determined from the transformed hue (H), saturation (S), and intensity (I) region-of-interest images using the color co-occurrence method for each fruit sample. Algorithms for selecting useful texture features were developed based on a stepwise discriminant analysis, and 14, 9, and 11 texture features were selected for three color combinations of HSI, HS, and I, respectively. Classification models were constructed using the reduced texture feature sets through a discriminant function based on a measure of the generalized squared distance. The model using 14 selected HSI texture features achieved the best classification accuracy (96.7%), which suggested that it would be best to use a reduced hue, saturation and intensity texture feature set to differentiate citrus peel diseases. Average classification accuracy and standard deviation were 96.0% and 2.3%, respectively, for a stability test of the classification model, indicating that the model is robust for classifying new fruit samples according to their peel conditions. This research demonstrated that color imaging and texture feature analysis could be used for classifying citrus peel diseases under the controlled laboratory lighting conditions.
Keywords: Citrus, disease detection, machine vision, color co-occurrence method, texture features, discriminant analysis
DOI: 10.3965/j.issn.1934-6344.2009.03.041-050

Citation: Dae Gwan Kim, Thomas F. Burks, Jianwei Qin, Duke M. Bulanon. Classification of grapefruit peel diseases using color texture feature analysis. Int J Agric & Biol Eng, 2009; 2(3): 41