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the fruit while rotating it in order to image the entire surface area. The vision system is just one aspect and there are numerous other sensors involved, including spectrometers to measure the sugar content, a sensor for the firmness, and strain gauge sensors to determine the weight. The various measurements are made and


Readout from the automated weeding machine developed at Wageningen University and Research Centre, which is designed to spray weed potato plants selectively in a farmer’s field


Best Players play at VISION


VISION has established itself as the world’s most important trade fair for machine vision. It is therefore almost a matter of course that the interna- tional who’s who of the machine vision industry meets year after year in Stuttgart.


And why is that? Try for yourself. www.vision-messe.de


– nuts, for instance, are harvested by vigorous mechanical shaking, causing them to fall onto a plastic sheet. ‘You can’t really beat that in terms of economy,’ says Dawson. ‘Replacing human vision (and muscle) in


the field is perhaps the most difficult vision (and robotics) task in the food production continuum,’ comments Dawson. The next step is using vision to sort and grade foodstuffs. As in any other manufacturing process,


depending on the value of the parts, it’s cheaper to find a defect early in the production chain rather than after time and money have been spent to transport it and wash it, and so forth. ‘Here, the machine vision aspect is still challenging, but easier because there is more control of the environment – the lighting can be controlled and the stream of material to sort or inspect is constrained,’ Dawson says. Food sorting also used to be carried out


mechanically – almonds can be sorted by using air shoots to blow any debris away from the nuts; potatoes can be sorted by placing them in a saline solution, whereby the potatoes float and any stones sink. By moving to machine vision, sorting can be faster and grading can be automated. Using optical sorting, the potatoes can be graded in terms of size and whether the produce is damaged in any way, as well as sorting out any debris. Dawson notes: ‘It’s a matter of value-adding, because now the higher-value components can be sorted out and preferentially treated, which justifies the cost of the machine vision system.’ MAF Roda Agrobotic, based in Montauban,


Messe Stuttgart 9 – 11 November 2010


France, manufactures grading and packing systems for fresh fruit and vegetable packing houses. Its GlobalScan 5 vision system is designed to sort fruit as it moves along a sizer (a conveyor made up of lanes that presents the fruit to the vision system). The sizer transports


then the fruit is diverted down one of 10 to 60 outlets, depending on the sizer. ‘The system is set up so that each outlet collects fruit with the same characteristics – the same weight, the same colour, the same quality, the same firmness, etc,’ explains Michel Rodière, electronic department manager at MAF Roda Agrobotic. The decision to send a piece of fruit to one outlet is made through Orphea, MAF Roda’s software. ‘The user might decide to divert small fruit at the first outlet, red fruit at outlet 15, and so on until the end of the sizer,’ Rodière says. The conveyor operates at speeds up to 15ft/s per lane. Cameras capture 20 colour and 20 infrared


images per piece of fruit as it rotates. Near infrared is used to detect disease or pest damage in the fruit, such as bitter pit, russet and bruises in apples. These diseases are characterised by dark spots on the skin and which are identified easily using IR images, because there is no interference from the colour of the fruit. Vision is used, firstly, for measuring the


dimensions of the fruit to sort it according to the diameter, area or volume. ‘Fruit is rejected if it is the wrong shape – round lemons, for example, are removed, because lemons need to be elongated,’ Rodière explains. Secondly, the cameras will sort the fruit according to colour. ‘This isn’t a simple task and customers will want data on numerous colour criteria, such as the hue of the overall background, the hue of the coloured part of the fruit, the percentage of the surface area that is a certain colour, how well- distributed the colour is across the surface of the fruit, etc.’ The Matrox Imaging Library (MIL) is used to


process images from the cameras. Binarisation and blob analysis are used to separate the fruit from the background and obtain dimensions. Colour calculations are typically histograms that are carried out in MIL. ‘Processing separates the hue of the background (the green part of an apple, for instance) from the hue of the coloured area (any red patches), and then statistical tests are performed to determine the various criteria – the percentage area of a specific hue, for instance,’ explains Rodière. The third, and according to Rodière the most


important, use of vision in the fruit sorting system is to identify defects, such as quality flaws on the skin – dark spots, any kind of


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