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Using Artificial Vision to Compare Oranges to Oranges
Oct 2011
MONCADA, Spain, Oct. 14, 2011 — Scientists have created machines that detect and separate rotten oranges, classify mandarin segments according to their quality, and help citrus fruit pickers out in the field. All prototypes use computer vision to automatically inspect the fruit.

The work was carried out by a team at the Valencian Institute of Agrarian Research (IVIA).

As well as developing statistical and computing techniques, the prototypes use the highest image resolution that modern equipment can achieve. They can analyze objects in regions of the electromagnetic spectrum that the human eye cannot see, such as ultraviolet and infrared, according to José Blasco, a researcher at IVIA.

Until now, rotten oranges have been detected manually in dark rooms with the help of ultraviolet light that illuminates the essential oils in a damaged rind through fluorescence. The job is carried out in strictly timed shifts because of the risk that this type of light poses. However, the team has created a machine that can carry out the task automatically.

A machine based on artificial vision classifies citrus fruits on the production line. (Image: IVIA)

“Through collaboration with a company within this sector, we have developed software and hardware that can locate rotten citrus fruits and discard those that are not fit for sale,” said Blasco, a member of the team that patented the machine.

Another machine — in the presence of visible light — classifies citrus fruits on the production line according to their quality, coloring and skin damage. First-class fruits that are destined for more demanding markets can be separated from second-class fruits that are perfectly edible despite having some small defect like bruises or scratchs. The analysis is carried out at a speed of 15 to 20 pieces of fruit per second.

Researchers have also developed a device that automates the inspection of ready-to-eat mandarin segments. After being individually separated on a vibrating platform, they are transported on a conveyor belt to the inspection area where they are examined at a rate of 28 segments per second. At this stage, the broken segments are separated from the whole ones, and the same goes for those with or without pips. Skin and foreign bodies are also identified and eliminated from the production line.

“We have even started to inspect the internal quality of fruit using magnetic resonance imaging, computerized axial tomography or x-rays, like those that are used in medicine,” Blasco added.

“At the moment these are costly techniques and we must continue in our investigations so as to facilitate their installation and make them more efficient in the fruit selection process.”

One of the latest investigations focuses on the use of hyperspectral imaging which can be applied as a way of identifying chemical compounds whose concentrations can change as the fruit ripens or rots, making it possible to predict the perfect time to eat the fruit or to track the evolution of a disease in it.

The largest of IVIA’s projects is a machine the size of a large tractor that helps during the picking of citrus fruits. Blasco said that it is a self-propelled prototype that can classify oranges while they are still in the field. As a result, he states that “it is very important that the inspection system be highly efficient from an energy consumption point of view.”

Operators pick the fruits and place them on the machine’s mobile belt. Sensors and a vision system are used to determine quality, color and the presence of external damage. The information gathered by the sensors is then transmitted to an automated logic controller for the fruit to be classified according to those three categories.

Upon arrival at the warehouse, the fruit has already been preclassified and comes with complete statistics on its quality, allowing its immediate destination to be determined.

The team’s results have been published in Food and Bioprocess Technology in a paper titled “Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables.”

For more information, visit:

hyperspectral imaging
Methods for identifying and mapping materials through spectroscopic remote sensing. Also called imaging spectroscopy; ultraspectral imaging.
machine vision
Interpretation of an image of an object or scene through the use of optical noncontact sensing mechanisms for the purpose of obtaining information and/or controlling machines or processes.
artificial vision and fruit inspectioncomputer vision and fruit inspectionEuropeFood and Bioprocess Technologyfruit inspection CATfruit inspection computerized axial tomographyfruit inspection hyperspectral imagingfruit inspection magnetic resonance imagingfruit inspection MRIfruit inspection x-rayshyperspectral imagingimagingIVIAJosé Blascomachine visionmandarin fruit inspectionorange fruit inspectionResearch & TechnologySensors & DetectorsSpainTest & MeasurementValencian Institute of Agrarian Research

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