Embedded special

A system from Blue River Technology designed to maximise lettuce yield by removing unwanted plants

the camera to project a laser line onto the crop, enabling 2D cross sections of the target to be obtained by triangulating the geometric coordinates across its surface. A 3D profile of the whole target can then be constructed by combining the cross sections. To enable it to identify and classify crops

correctly, the vision system is equipped with Stemmer Imaging’s Polimago deep learning soſtware, which is first trained using a set of pre-defined data. ‘As part of the training process we have to feed the system images of broccoli heads,’ explained Ellen. ‘Tousands of images are gone through and identified manually. When the deep learning system is then faced with a new image, it can provide a score out of a hundred that reflects how sure it is that the image contains a broccoli head.’ According to Ellen, as 3D imaging is based on the

shape of the target rather than the intensity of the light being reflected off it, the technique is much less susceptible to variation in lighting, which makes it easier to train the deep learning soſtware. Certain deep learning challenges still exist,

however, due to the increasing variety of crops grown in agriculture. ‘Producers are using selective breeding to give certain qualities [to crops], such as dark green broccoli heads, or heads that protrude further than the leaves. Tey’re constantly evolving and constantly changing, so we have to make

sure that the vision system can cope with all these variants, which means that in the deep learning training set, we have to include broccoli that’s in the early, middle, and late stages of growth,’ Ellen said. ‘Tere’s also an issue in that the colour of soil

differs depending on the climate,’ added Pitt. ‘While we have dark soil over here in the UK, in Spain the soil is a lighter, sandy colour with stones oſten mixed in, which might be mistaken for small gem lettuces. Te vision system has to cope with everything.’ Using their current system,

If everything is successful, I would expect to see this out in the field in three years

the two companies are able to achieve a 96 per cent success rate in identifying crops, with the camera taking around 100ms to identify and grade them, and the robotic system taking approximately five seconds to

complete the picking cycle. Te vision-guided harvesting machines are

currently only operated by Capture Automation and those involved in their production. Te next stage of development, according to Ellen, is creating user interfaces for the vision system that can be used even by those who aren’t skilled in imaging: ‘You have complex robotic and vision systems on there, and you have to tie that all together in a simple start/ stop interface, so tailoring this to the end users is a challenge. If everything is successful I would expect to see this out in the field in three years.’ Ellen and Pitt both believe that faster, simpler embedded systems will drive vision-guided

June/July 2017 • Imaging and Machine Vision Europe 33

harvesting in the future. ‘Going forward it’s about embedding all this into a small box to have a nice, simple system running bespoke algorithms that can plug directly into a robot,’ Ellen concluded. ‘Tis will help the market I’m sure, as vision systems get smaller and more embedded.’

Spray to kill Weeds in the back garden might be a pain, but farmers are required to deal with weeds on a much larger scale. For this purpose, California-based Blue River Technology has combined a deep learning vision system with a set of herbicide-spraying nozzles on its See and Spray machine, a device that can be pulled by a tractor and identify any weeds it passes over, enabling it to apply a dose of herbicide to them without harming surrounding crops. Te company equips the machines with standard


Blue River Technology

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