SEED ANALYSIS


Videometer’s spectral imaging solution for analysing seed


gwhich can equate to around 200kg of grain per hour at the speeds at which these


machines operate. Blanc explained that Bühler wanted to


lower the error rate to less than 3 per cent for grain moving at a speed of 1.5m/s. He said, on top of that, Bühler needed something that is compact, cheap and with low compute resources. Blanc and the team at CSEM developed


a vision system with embedded AI able to achieve an error rate of less than 3 per cent. Te images could also be used to add a layer of quality control to the process. Te CSEM system uses a 1.5-megapixel


RGB camera running at 60fps. Tis is paired with custom 150W illumination that can deliver 5µs pulses to avoid image blur. ‘We used an Nvidia Jetson Nano to orchestrate all the peripherals and run the algorithm in real time,’ Blanc explained during the trade fair. Everything is processed within the vision system. Te team was also able to include a small web server running onboard, to which mobile devices can connect wirelessly. Blanc said in his presentation: ‘We


developed a flow speed algorithm to measure the speed of the grain. We can get an accuracy of 40µm at a speed of 2m/s. Tis is thanks to the help of the powerful LEDs.’ Bühler is not interested in flow speed,


however, it is interested in the mass flow in kilograms per second. Tis can be calculated


from the flow speed multiplied by the cross section of the shoot, multiplied by the density of the grains, which depends on the water content of the grain. Te vision system doesn’t give the grain density, so the camera is combined with Bühler’s standard mass flow measurement system to give real- time analysis. CSEM also developed a neural network


to detect grain type, using the saturation and brightness information in the images to identify the grain. ‘We wanted to have an algorithm that is


able to assess the quality of the grain, and for that we needed a way to segment the grains,’ Blanc told the audience in Stuttgart. CSEM used weakly supervised training of its neural network to segment the grains. ‘A data-driven approach [to AI] requires a lot of annotated data, which is sometimes cumbersome and hard to get,’ he said. Te team therefore annotated a single image of the grain, and then went into a process of data augmentation, cropping the single image into a lot of sub-images. ‘We did a lot of stretching, rotating and flipping to generate a few thousand images that were used to train, test and validate a neural network,’ he said. ‘As an input into our neural network, we can provide 32 cropped images and as an output we get 32 masks of a single grain of segmentation,’ he said. ‘Tis is useful to fit an ellipse on these masks.’


22 IMAGING AND MACHINE VISION EUROPE OCTOBER/NOVEMBER 2021


‘Spectral imaging is a mature technology, but it’s something that’s specialised… You have a lot of opportunity to get noise instead of signal’


A single image of wheat, for instance, was


iterated three times, so 3 x 32 samples, which provides 86 valid grains on top of which ellipses were fitted. From the ellipses the group extracted the major and minor axes to make some statistical analyses and get an indication of grain quality. ‘If it’s not necessary, we don’t push to go


into the data-driven approach,’ Blanc said. ‘Te algorithm to detect the type of grain is based on classical image processing.’ Bühler uses lots of optical and imaging


methods in its grain sorting machines to squeeze the maximum amount of usable grain from a harvest. Te percentage gains from optical technologies can be quite small, but because these machines are sorting huge amounts of grain per hour, the benefits for its customers can be massive. It also helps reduce waste in a food system that’s getting pushed to the limit in terms of feeding the planet. O


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Videometer


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