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Artificial intelligence solves the problem of seed germination tests

A new tool, SeedGerm - based on machine learning-driven image analysis – is able to test seed samples to ensure a certain germination rate is met in a low-cost, high-throughput and semi-automated way.

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The product is the result of a collaboration between the Earlham Institute (EI), the John Innes Centre (JIC), Syngenta and NIAB. Details have been published in the New Phytologist, along with the open-source software and data.

 

Carmel O’Neill, research assistant in the Penfield Group at John Innes Centre says: “Currently most seed germination is still recorded manually. Against this, SeedGerm presents fast, accurate, high throughput screening and will be of major interest to crop seed production companies and research programmes screening large germplasm collections.”

 

SeedGerm uses a cabinet equipped with cameras that take photographs throughout the germination process, documenting each stage from imbibition (seeds taking up water) through to the emergence of the root and further changes in the newly growing plant.

 

Supervised machine learning is used to automatically determine how germination is progressing through comparing images. Algorithms can be trained to predict how likely it is that a seed has germinated based on measurements extracted from an image that relate to the seed’s size, shape, and colour, according to the research.


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Germination rate

 

Seed germination experts from Syngenta confirmed the effectiveness of SeedGerm for measuring germination rate and seedling health across five major crop species, including tomato and rapeseed. It opens the way for SeedGerm to replace manual seed scoring, while also contributing to seed certification, seed insurance and sowing guidance.

 

In addition to being a quality assurance tool for seed companies and farmers, the power of SeedGerm to measure phenotypic changes over time has further novel applications in crop improvement research. Many of the characteristics that can be measured help to estimate performance in the field in terms of canopy closure, weed suppression and predicted yield, according to the researchers.

 

Rene Benjamins, senior scientist at Syngenta Seeds said: “The developments and learnings from SeedGerm are truly a big step forward in automation and generating high quality and reliable data in scoring seed germination. This will help seed companies like Syngenta in providing the best quality to their customers.”

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