ArticleScientific reports2021
Robust seed germination prediction using deep learning and RGB image data.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Phenotypic Differentiation Between Highland and Coastal Quinoa Under Cold Stress Conditions.Plant, cell & environment · 2026Article
- Higher plant seed container germination success predicted by smart farming optical RGB approach.Scientific reports · 2026Article
- GroupCeptionNet: a lightweight model for classifying chili seed germination with macro images.Scientific reports · 2026Article
- Deep learning-based seed germination prediction using morphological traits and RGB images.BMC plant biology · 2026Article
- KD-SSGD: knowledge distillation-enhanced semi-supervised germination detection.Frontiers in plant science · 2025Article
- Advancing plant biology through deep learning-powered natural language processing.Plant cell reports · 2024Review
- An exploration of the influence of ZnO NPs treatment on germination of radish seeds under salt stress based on the YOLOv8-R lightweight model.Plant methods · 2024Article
- Review
- QTL mapping for seed morphology using the instance segmentation neural network inFrontiers in plant science · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Achieving seed germination quality standards poses a real challenge to seed companies as they are compelled to abide by strict certification rules, while having only partial seed separation solutions at their disposal. This discrepancy results with wasteful disqualification of seed lots holding considerable amounts of good seeds and further translates to financial losses and supply chain insecurity. Here, we present the first-ever generic germination prediction technology that is based on deep learning and RGB image data and facilitates seed classification by seed germinability and usability, two facets of germination fate. We show technology competence to render dozens of disqualified seed lots of seven vegetable crops, representing different genetics and production pipelines, industrially appropriate, and to adequately classify lots by utilizing available crop-level image data, instead of lot-specific data. These achievements constitute a major milestone in the deployment of this technology for industrial seed sorting by germination fate for multiple crops.
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Registered trials
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