In-Line Estimation of the Standard Colour Index of Citrus Fruits Using a Computer Vision System Developed For a Mobile Platform

Abstract

A key aspect for the consumer when it comes to deciding on a particular product is the colour. In order to make fruit available to consumers as early as possible, the collection of oranges and mandarins begins before they ripen fully and reach their typical orange colour. As a result they are therefore subjected to certain degreening treatments, depending on their standard colour citrus index at harvest. Recently, a mobile platform that incorporates a computer vision system capable of pre-sorting the fruit while it is being harvested has been developed as an aid in the harvesting task. However, due to the restrictions of working in the field, the computer vision system developed for this machine is limited in its technology and processing capacity compared to conventional systems. This work shows the optimised algorithms for estimating the colour of citrus in-line that were developed for this mobile platform and its performance is evaluated against that of a spectrophotometer used as a reference in the analysis of colour in food. The results obtained prove that our analysis system predicts the colour index of citrus with a good reliability 2 (R 2 = 0.925) working in real time. Findings also show that it is effective for classifying harvested fruits in the field according to their colour.

Swingle citrumelo seed vigor and storability associated with fruit maturity classes based on RGB parameters

“…The primary colors, red, green and blue (RGB), provide the most common color model used in computer vision systems (Vidal et al, 2013), where sensors capture the intensity of light in the red, green and blue spectrum (Léon et al, 2006). Vidal et al (2013) developed a rapid, reliable computer vision system based on average RGB values to examine citrus fruit color according to the amount of degreening.…”

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

“…An illumination system based on pulsed LEDs ensured low energy consumption. The system was capable of inspecting and sorting the fruit into three categories using the standard citrus colour index (Vidal et al 2013) and size properties at a rate of 8 fruits/s.…”

“…Kurtulmus et al (2011) Developed an algorithm to distinguish immature green citrus fruits in natural outdoor on images using colour, circular Gabor texture, and a novel 'eigenfruit' method Bansal et al (2013) Used Fast Fourier Transform (FFT) was to distinguish fruit from other objects in natural outdoor colour images Sengupta and Lee (2014) Identified immature green citrus from digital colour images using a Hough circle detection and texture classification by a support vector machine Mobile platforms Kohno et al (2011) Developed a computer vision system in combination with an NIR spectrometer mounted on a mobile platform to measure both the external (colour and diameter) and the internal quality (sugar content and acidity) of citrus fruits with low speed. Shin et al (2012aShin et al ( & 2012b Developed a computer vision system that inspected the number and size distribution of harvested fruits on a conveyor belt while transferred from a catch harvester Vidal et al (2013) Developed fast algorithms to inspect colour of citrus fruits at harvest time mounted on an agricultural vehicle Cubero et al (2014a) Developed a computer vision system energetically efficient to inspect at a high speed the colour and size of citrus fruits at harvest time mounted on an agricultural vehicle…”

“…The first type of system processes the images separately; each image is processed in the moment it is captured and, finally, when the fruit is completely inspected, a decision is made based on the results of the partial images. This was the strategy followed by Vidal et al (2013) and Cubero et al (2014a). The second type extracts the central strip from every image captured.…”

Analysis of the detachment of citrus fruits by vibration using artificial vision

“…A potential alternative is to develop systems based on automatic image analysis technology, which are already being used in other areas of agriculture (Cubero et al, 2011;Lorente et al, 2012), including applications in the field. For instance, Mizushima and Lu (2010) and Vidal et al (2012) developed systems for the pre-sorting of apples and oranges respectively in the field. Jimenez et al (2000) used machine vision to locate fruit on the trees.…”