ArticleFrontiers in plant science2024
Deep learning for automated segmentation and counting of hypocotyl and cotyledon regions in mature
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- Research progress on rapid detection technology of soybean phenotypic indicators under saline-alkali stress.Planta · 2026Review
- Artificial Intelligence Methods in Forest Biotechnology: Current Status and Future Prospects.International journal of molecular sciences · 2026Review
- Review
- Estimating Radicle Length of Germinating Elm Seeds via Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Residual swin transformer for classifying the types of cotton pests in complex background.Frontiers in plant science · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
In commercial forestry and large-scale plant propagation, the utilization of artificial intelligence techniques for automated somatic embryo analysis has emerged as a highly valuable tool. Notably, image segmentation plays a key role in the automated assessment of mature somatic embryos. However, to date, the application of Convolutional Neural Networks (CNNs) for segmentation of mature somatic embryos remains unexplored. In this study, we present a novel application of CNNs for delineating mature somatic conifer embryos from background and residual proliferating embryogenic tissue and differentiating various morphological regions within the embryos. A semantic segmentation CNN was trained to assign pixels to cotyledon, hypocotyl, and background regions, while an instance segmentation network was trained to detect individual cotyledons for automated counting. The main dataset comprised 275 high-resolution microscopic images of mature
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Registered trials
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