Classification of Selected Citrus Fruits Based on Color Using Machine Vision System. Issue 2 (1st February 2016)

• Record Type: Journal Article
• Authors: Iqbal, S. Md.; Gopal, A.; Sankaranarayanan, P.E.; Nair, Athira B.
• Is Part Of: International journal of food properties. Volume 19:Issue 2(2016)
• Page Start: 272
• Page End: 288
• Publication Date: 2016-02-01
• Subjects: Classification -- Single view -- Color distance -- PDF -- LDA -- Maturity -- Machine vision -- Image processing; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664.0705
• DOI: 10.1080/10942912.2015.1020439
• Languages: English
• ISSNs: 1094-2912

Abstract

Abstract : This article evaluated some of the machine vision techniques to classify selected citrus fruits like oranges, sweet-lime, and lemon based on color analysis using single view fruit images. The methods carried out analyze the fruit images to extract the hue and classify using methods like color distance, linear discriminant analysis, and probability distribution function. The performance was evaluated in terms of classification accuracy relative to human classification and computational complexity. Classification accuracy above 90% could be obtained based on color classification. The probability distribution function method was found to be computationally less intensive and providing better accuracy when compared to other two methods. Since surface color is one of the indicators of maturity, color determination was also extended to find the maturity of fruits based on color variations within the same variety of fruits using red/green value ratio. The analysis also showed that the color information found in terms of hue mean and hue median can be utilized to classify the fruits based on maturity. Appropriate algorithms were developed and implemented to classify the fruits based on color and maturity using the above methods.