Classification of Selected Citrus Fruits Based on Color Using Machine Vision System

Abstract

This paper evaluated some of the machine vision techniques to classify selected citrus fruits like oranges, sweet-lime and lemon based on colour analysis using single view fruit images. The methods carried out analyze the fruit images to extract the hue and classify using methods like Colour Distance, Linear Discriminant Analysis (LDA) and Probability Distribution Function (PDF). The performance was evaluated in terms of classification accuracy relative to human Downloaded by [New York University] at 01:31 18 June 2015A c c e p t e d M a n u s c r i p t 2 classification and computational complexity. Above 90% classification accuracy could be obtained based on colour classification. The PDF method was found to be computationally less intensive and providing better accuracy when compared to other two methods. Since surface colour is one of the indicators of maturity, colour determination was also extended to find the maturity of fruits based on colour variations within the same variety of fruits using R/G ratio.The analysis also showed that the colour information found in terms of hue mean & hue median can be utilized to classify the fruits based on maturity. Appropriate algorithms were developed and implemented to classify the fruits based on colour and maturity using the above methods.

Characterisation of liquid food colour from digital images

“…To measure colour from digital images, a computer vision system (CVS), consisting of a digital camera, an image acquisition ambient with controlled illumination and information processing software, is required. [11] In these systems, one key step is the conversion of the Red, Green, and Blue (RGB) colour values obtained from digital cameras to the CIELAB colour space. However, only few works have been reported using this methodology regarding the colour of translucent liquid foods.…”

Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

“…Discriminant analysis has been shown to be useful for classification problems in several academic disciplines. Typical examples can be found in classification of citrus fruits (Iqbal et al, 2016), screening for elderly drivers (Ferreira et al, 2012) and academic performance of first-year students (Young, 1989). (For additional details on the linear discriminant analysis model, Izenman (2008) may be consulted.…”

Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review

“…Segmentation methods based only on colour information have been largely investigated (Iqbal et al 2016) but most of them require a previous training step because the large variability in colours makes it difficult to find absolute systems capable of dealing with all this variability. A typical supervised method is to define particular classes like background, stem, sound peel, different types of defects, etc., and to assign them to particular colours.…”

“…Table 2 summarises the different works carried out for the application of computer vision in the citrus inspection in postharvest ordered by different topics chronologically. Table 2 Reference Achievement Estimation of properties of the fruit Blasco et al (2007a) Used an unsupervised segmentation method based on region growing to separate defects from sound skin Blasco et al (2007b) Tested different colour spaces to discriminate among eleven types of defects in the citrus peel and the stem Kim et al (2009) Introduced textural features in colour images to distinguish between some serious damages and other cosmetic defects (Blasco et al 2009) Introduced spectral and morphological information to distinguish between some serious damages and other cosmetic defects Omid et al (2010) Estimated the volume of the citrus using two cameras and computing the volume by dividing the fruit in a series of discs López-García et al (2010) Used multivariate image analysis introducing textural information and PCA to separate defects from sound skin López et al (2011) Used colour and texture features extracted in the RGB and HSI colour spaces to discriminate among seven common defects of citrus fruits Vijayarekha (2012a) Used several segmentation techniques to detect defects in citrus fruits Vijayarekha (2012b) Used several segmentation techniques to identify defects in citrus fruits Li et al (2013) Used RGB image ratios to discriminate the stem from different defects in oranges Cubero et al (2014b) Developed a robust method to detect stalks in different fruits, including oranges and mandarins Iqbal et al (2016) Investigated several supervised segmentation methods based on colour information Detection of decay lesions Gomez et al (2007) Used a Mahalanobis kernel to classify pixels as decay or sound skin in hyperspectral images Gómez-Sanchis et al (2008) Used correlation analysis, mutual information, stepwise, and genetic algorithms based on linear discriminant analysis (LDA) to select the most relevant bands of hyperspectral images, and classification and regression trees and LDA for pixels classification in decay or sound skin Kondo et al (2009) Studied the compounds involved in the fluorescence process to detect decay in oranges Kurita et al (2009) Innovative technique which alternatively switched on UV and white pulsed LED, thus allowing the inspection with both types of illumination, and hence allowed both a fluorescent and a colour image to be captured Slaughter et al (2008) Detect freeze-damages in the skin of trough fluorescence imaging Blanc et al (2010) Patented a commercial sorter for decay detection in citr...…”