ArticleJournal of experimental botany2025
MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.
Article in Journal of experimental botany, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Lipid MRI in plant science: principles and potential areas of application.Journal of experimental botany · 2026Review
- Dual effects of slightly acidic electrolyzed water on rice seed germination: insights from water dynamics via low-field NMR.Frontiers in plant science · 2026Article
- Multispectral imaging and automated analysis for quantifying grain quality to reveal known and potential novel alleles affecting grain traits in wheat.Frontiers in plant science · 2025Article
- Leveraging sensor technologies for seed phenotyping by genebanks.Frontiers in plant science · 2025Article
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Authors and funding
9 authors.
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Abstract
Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programmes. There is a need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we introduce a novel tool, MRI-Seed-Wizard, which integrates deep learning algorithms with non-invasive magnetic resonance imaging (MRI) for use in a new domain-plant MRI. The tool enabled in vivo quantification of 23 grain traits, including volumetric parameters of inner seed structure. Several of these features cannot be assessed using conventional techniques, including X-ray computed tomography. MRI-Seed-Wizard was designed to automate the manual processes of identifying, labeling, and analysing digital MRI data. We further provide advanced MRI protocols that allow the evaluation of multiple seeds simultaneously to increase throughput. The versatility of MRI-Seed-Wizard in seed phenotyping is demonstrated for wheat (Triticum aestivum) and barley (Hordeum vulgare) grains, and it is applicable to a wide range of crop seeds. Thus, artificial intelligence, combined with the most versatile imaging modality, MRI, opens up new perspectives in seed phenotyping and crop improvement.
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Registered trials
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