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sugar beet plants were unintentionally killed. ‘It is difficult to design a vision system for use in agriculture, because of the environment,’ states Nieuwenhuizen. ‘Every field and every crop is different and, what’s more, the conditions from day to day and even within a day will differ. Light levels can vary enormously, which is why the cameras were covered and the scene artificially lit. This meant that the cameras only had to cope with the variable conditions caused by the crop itself and not changes in light.’ The researchers used algorithms that were adaptive to changes in colour, in that the

‘Light levels can vary enormously, which is why the cameras were covered’

algorithm only takes into account weeds from the past 10m in the field. Changing soil conditions within a field, such as water and nitrogen content, can cause colour changes in the plants. Adaptive algorithms meant that only the local difference between crops and weeds were used in classification. There is around a four-week time slot between

when weed plants start to emerge to when they start to spread disease, and during that timeframe the plants have to be removed. This, according to Nieuwenhuizen, is the main reason for developing the automated system, in that there is a time constraint for controlling the weeds. In addition, removing weed plants requires a lot of manual labour, which is expensive. The team at Wageningen University has showed that the machine is more economical compared to manual weeding. Allied Vision Technologies’ colour and near-

infrared Marlin cameras are used to distinguish between crops and weeds. Combining images captured in the NIR and red spectrums gives a good indication of how healthy a plant is and how fast it’s growing, a evaluation termed the Normalised Difference Vegetation Index (NDVI), which indicates whether or not a plant is growing. Using NIR in combination with colour images gives extra features to better distinguish between the crops, which would have a higher NDVI associated with healthier growth, and the weeds, which would typically exhibit a lower NDVI. Camera resolution was important (1mm

resolution per pixel was required) to locate the plants precisely. The Marlin’s FireWire interface made it easy to connect the cameras with National Instruments’ LabView real-time operating system. ‘The machine is running

behind a tractor and so it’s important to have a real-time system with an actuator continuously carrying out tasks,’ Nieuwenhuizen says. The system is currently a proof-of-principle machine and has been demonstrated to researchers and farmers. Nieuwenhuizen is now extending the technology to identify other weeds to make it more commercially attractive for farmers. ‘Most other weeds are smaller than potato plants,’ he says. ‘There are also further colour differences from weed to weed and from crop to crop, and tests need to be carried out to see if the adaptive algorithm will work well in these situations. The cameras give good quality images, but the intelligence in the system needs to be extended to be able to detect other weeds as well.’

Sorting fruit Fruit picking is one task that is still largely done by hand. ‘There has been a lot of work carried out developing automated fruit picking, because of the increasing cost of manual labour and the value of the crop,’ comments Dawson of Dalsa. The Automation Centre for Research and Education (ACRO), based in Belgium, has developed a mechanised fruit picker that uses a large blue tent to surround the fruit trees as the tractor moves through the orchard. The tent provides a diffuse blue background, which creates high contrast between the red apples and green leaves making the vision job a lot easier. ‘Designing a robotic system that will probe in and around the branches harvesting fruit without the tent is a much harder to achieve because the background cannot be controlled,’ says Dawson. In some circumstances, high contrast can also be achieved with infrared or UV imaging – a system has even been developed for harvesting tomatoes using low-intensity X-rays. There are some crops that generally wouldn’t lend themselves to using vision for harvesting

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    


  

A machine vision system can be used to distinguish between cherries and the background by the fruit’s colour and shape


 

Image courtesy of Dalsa.

 

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