food and agriculture 13
disease, or even natural blemishes could result in the fruit being rejected. ‘The severity of the disease will differ, some of which will result in a rejection of the fruit while others will be accepted depending on the market and the requirements of the packing house,’ he says. The vision system can define up to eight
quality categories, eight colour categories, and 16 categories for size. Different combinations of characteristics are then programmed into Orphea in order to sort the fruit. Infrared imaging is a useful tool to separate
the fruit from the background, explains Rodière: ‘All fruits viewed in infrared are depicted at the same level of grey – shiny yellow apples or dark red apples will appear at the same grey level in infrared.’ In addition, he notes defects will also be more visible in infrared and will not be influenced by the colour of the fruit. Certain diseases can also be defined by colour and IR images can be used to determine a region of interest and then processed in combination with colour information. The sorting machines have to be quick and
reliable, but also must handle the fruit gently to avoid any damage. According to Rodière, the main advantage with GlobalScan 5 is its performance: ‘80 to 90 per cent of what was
previously a manual process for sorting apples can be automated with this system.’ A manual final check is often carried out prior to packing the fruit in boxes to send to the supermarket he says, but the majority of human labour is saved. The system also accumulates data about
the fruit during the processing in order to determine a price to pay the grower. Records are kept to trace the produce as it passes through the packing house to the supermarket.
Food packing Once the produce has been sorted and graded, the final steps of food production are packaging and transport to the consumer, which, according to Dawson of Dalsa, have made extensive use of machine vision for many years – examples include optical recognition of barcodes for sorting packaged goods, or the automated visual inspection of package labels for correctness and print quality. Dawson says that sorting organic produce is
a different set of challenges from traditional part gauging and metrology work done by machine vision systems. ‘Machine vision can measure a machine part to a 10,000th of an inch, which is something a human cannot do,’ he says.
‘However, a human would be able to look at an apple and instantly tell if it’s good or bad.’ A large number of food inspection
applications use 3D imaging. An automated system to disinfect cow teats prior to milking uses a robot arm that sprays disinfectant guided by a 3D laser-based vision system which locates the cow’s teats. ‘This is a good example of a labour-intensive process that has been almost completely automated,’ says Dawson. In contrast, many traditional machine vision tasks are effectively 2D or can be arranged to be so. Dawson comments: ‘A combination of advances in robotics and advances in machine vision, as well as a reduction in the cost of technology, are making many agricultural applications practical, rather than simply being academic demonstrations of what could be done with no constraints on time and money.’ Most automated vision applications are
and will be post-harvest, for sorting, grading and packaging where the environment can be controlled more easily. Some, however, are finding their way further upstream into the farmer’s field – the automated milking system is a good practical example – and as other systems are developed, vision looks likely to infiltrate further into this area.
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