Written by Charlotte Cunningham
Researchers have taught a clever new tool how to carry out one of crop breeding’s more challenging tasks, according to the latest news from the John Innes Centre. Charlotte Cunningham reports. In order for growers to maximise crop productivity, it’s important that seeds germinate effectively and uniformly within a given period. But the testing process, carried out by seed suppliers to ensure a certain germination rate is met, is difficult and time consuming. But it looks like there could be a novel new solution… SeedGerm – based on machine learning-driven image analysis – performs this tricky process in a low-cost, high-throughput and semi-automated way. The product is the result of a collaboration between the Earlham Institute (EI), the John Innes Centre (JIC), Syngenta and NIAB. Details of the research step-forward is published, open-access in New Phytologist, along with the open-source software and data. “Currently most seed germination is still recorded manually,” says Carmel O’Neill, research assistant in the Penfield Group at John Innes Centre and co-first author on the paper. “Against this, SeedGerm presents fast, accurate, high throughput screening and will be of major interest to crop seed production companies and research programs screening large germplasm collections.” Machine learning – a version…
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