A Double Image Acquisition System with Visible and UV LEDs for Citrus Fruit

Abstract

There are many types of citrus fruit grading machine with machine vision capability. While most of them sort fruit by size, shape and color, detection of fruit rot remains challenging because their colors are similar with normal parts. Objectives of this research were to investigate if fluorescence would be a good indicator of the fruit rot, and to develop an economical solution to add the rot inspection capability to an existing machine vision fruit inspection station. A machine vision system consisting of a pair of white and UV LED lighting devices and a color CCD camera was proposed for the orange fruit grading task. Since the time lag between the color and fluorescence image captures was short (14 ms), it was possible to inspect color, shape, size, and rot of a fruit on the move before it leaves an existing industrial inspection chamber.

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

“…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...…”

Detection of early decayed oranges by structured-illumination reflectance imaging coupling with texture feature classification models

“…Fluorescence imaging provides another means for decay detection of citrus, based on the observation that citrus epidermal tissues can emit yellow fluorescence under excitation of ultraviolet (UV)-A light ( Obenland et al, 2010 ). Kurita et al (2009) designed a double image acquisition system with visible and UV LEDs for decay detection of citrus fruit. However, not all the decayed areas of citrus produce detectable fluorescence, ensuring the effectiveness of this imaging technique ( Momin et al, 2012 ).…”

Identification of UV-Fluorescence Components for Detecting Peel Defects of Lemon and Yuzu using Machine Vision

“…Our lab has evaluated the ability of a machine vision system, based on UV-induced fluorescence imaging, to detect peel defects in citrus (Kurita et al, 2009, Momin et al, 2010, Ogawa et. al., 2011.…”