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imaging and machine vision europe june/july 2010

Image courtesy of MAF Roda Agrobotic.


food and agriculture A bumper harvest

Greg Blackman looks at where machine vision is finding uses in agriculture and for grading and sorting foodstuffs

Modern agriculture relies heavily on mechanisation to obtain the kind of crop yields needed to feed the world’s population. Mechanical systems for harvesting and sorting crop plants have developed, just as farming practices have with the use of fertiliser and pesticides along with the selective breeding of high-yield varieties. In the western world, cotton is now harvested with a cotton picker, a machine that removes the cotton from the boll – or, in places where picker varieties can’t grow, a cotton stripper that strips the entire boll from the plant. The combine harvester, a classic example of mechanisation in agriculture, is used for cereals and combines reaping, binding and threshing in a single operation. There are instances where machine vision

is used out in the field, but this is a very unforgiving environment for a vision system

to operate in – largely because of the changing lighting conditions. ‘One of the major problems with machine vision out in the field is that it doesn’t provide the flexibility and dynamics of the human eye – the lighting has to be carefully controlled, for instance,’ comments Ben Dawson, director of strategic development at Dalsa. Dalsa has been supplying vision hardware for various agricultural applications for a number of years – the company supplied hardware to researchers at the University of California 15 years ago for the development of an automated weeding system. More recently, researchers at Wageningen

University and Research Centre in The Netherlands have developed an automated weeding machine for controlling volunteer potato plants – potato plants that survive the winter and emerge in spring as weeds in a different crop rotation, such as sugar beet. The machine, engineered as part of a five-year PhD project culminating last year, consists of two Marlin cameras from Allied Vision Technologies in a covered and artificially lit enclosure that analyse images of the field as the system is dragged behind a tractor. Volunteer potato plants were used as a model

weed. They are a source of nematodes and diseases such as Phytophthora infestans (potato blight) which aren’t controlled by pesticides in the standard manner because the plants are not the crop. ‘The automated system is of interest to farmers as there are currently no selective herbicides available to kill these plants,’ explains Ard Nieuwenhuizen, a researcher at Wageningen University working on the project. Currently, removing the weeds has to be carried out plant- specifically and by hand, either by applying a non-discriminate herbicide like glyphosate or by uprooting them. The system was programmed to recognise

weeds based on colour and the crop row pattern. Crops are seeded in rows a known distance apart and therefore everything between those lines must be a weed. This information is used to train the system, after which the weed and the crops are distinguished based on colour. The system conducts a background subtraction to remove all the soil in the image and identify only the green vegetation. About five to 10 weed potato plants and sugar beet plants have to be detected for the system to be trained. During field trials 84 per cent of the weed potato plants were controlled or killed, while 1.4 per cent of

A sizer for grading oranges in Valencia, Spain. The machine processes 40 million kilograms of oranges per year

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