Artificial intelligence solves the problem of seed germination tests

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.