SEED ANALYSIS
Spectral vision foresees seed germination story
Greg Blackman finds out how seed producers are reaping the rewards of imaging
I
n the spring earlier this year, I helped sow a meadow with a mix of wild flower and grass seed in the hope that, eventually,
the flowers and grasses would outcompete the thistles. Success so far has been limited. One of the better areas was a stand of linseed, but instead of the expected, more traditional blue flowers, these plants turned out to have white blossom. Mistakenly growing a variety of linseed
with white flowers rather than blue is not the end of the world but, for seed producers, the plant variety that is packaged and sold to growers matters, sometimes down to the level of the single seed. According to Jens Michael Carstensen, CEO of imaging firm Videometer, which works heavily in the seed industry, it can be required by law that batches of seed are sufficiently pure to be traded. Videometer provides hyperspectral
imaging systems and generates almost half its revenue from systems sold for seed and grain analysis. (Inspecting other foodstuffs accounts for a quarter of its revenue, with the remaining quarter for solutions for pharmaceutical inspection, medical devices, cosmetics and other areas.) For the kind of highly accurate analysis
where each and every seed is sorted, Videometer would use a robot with validated
picking, Carstensen said, ‘so we are sure not only that we detect a seed that’s the wrong variety, but that we also remove it.’ Te aim is to have validation throughout the process chain. ‘A lot of effort is put into breeding the right
kind of seed,’ Carstensen said. ‘By the same token, a lot of effort goes into making sure the supply chain works so that the seed sown is as good as it’s supposed to be, it’s the correct variety, and it germinates and grows into a strong plant.’ Spectral imaging can help in both
production quality control and in plant breeding – two important parts of the seed industry. In plant breeding, the aim is to screen seeds to pick out candidates with the right traits. In either breeding or production, spectral
imaging can determine the variety and whether there are off-types contaminating a batch of seed, such as seeds from different crops or weed seeds. ‘Purity is extremely important in seed production – that it’s the right crop and the right plant variety,’ Carstensen said. Spectral imaging can also show whether
the seed is damaged in any way, from insects or fungi, for instance, and whether the seed is likely to germinate. ‘Saying whether a seed will germinate or not is tricky because normally you need to sow the seed and wait for it to germinate and grow,’ Carstensen explained. He said that the Holy Grail would be to detect whether a seed is viable or not with imaging, something that’s not possible at the moment, but spectral imaging can give indicators as to viability. ‘We don’t expect that, for all seeds, we can
20 IMAGING AND MACHINE VISION EUROPE OCTOBER/NOVEMBER 2021
tell the entire germination story based on the dry seed, but for many seed types we can see how viable they are and how likely they are to germinate,’ Carstensen continued. ‘Seed suppliers can make decisions based on a partial knowledge of germination, and that will help them in their logistics.’ Along with pest damage, spectral
imaging can assess whether a seed has been harvested immaturely – a chlorophyll fluorescence test can show how much chlorophyll there is in the seed and therefore its maturity – and also whether a seed has been stored for too long by identifying oxidative damage. Spectral imaging can show a lot, but
nevertheless, the organisations controlling seed testing – the International Seed Testing Association and the Association of Official Seed Analysts – require a germination test. Videometer would typically use its
standard system, VideometerLab, for seed analysis. Tis scans across a spectrum from 350nm to 1,000nm to create a data cube. ‘We can go a little further into the deep UV and the near infrared, but there are challenges getting super apochromatic lenses to cover the complete spectrum,’ Carstensen said. Te company has a machine learning
engine that it trains with spectral imaging data from different crops and plants. Te algorithms look for different markers in the seed, such as the variety or whether there’s fungal damage. ‘We work with all crops,’ Carstensen said.
‘We look for similar markers with spectral imaging, but each crop will be different and it’s difficult to extrapolate from one crop to another. We work a lot with vegetable seeds
@imveurope |
www.imveurope.com
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