ArticleThe New phytologist2022
RootPainter: deep learning segmentation of biological images with corrective annotation.
Article in The New phytologist, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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Who cites it
74 citing papers in PubMed.
- RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery.ArXiv · 2026Article
- Advances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.Journal of experimental botany · 2026Review
- High-resolution 3D micro-CT reveals an additional muscle layer in esophageal atresia.Esophagus : official journal of the Japan Esophageal Society · 2026Article
- Novel imaging approaches for visualizing root-mycorrhizal fungal interactions.Journal of experimental botany · 2026Review
- Observing the invisible: X-ray CT for plant-microbe interactions.The New phytologist · 2026Review
- Organ-specific terpenoid responses in Tanacetum vulgare are chemotype-dependent.Plant biology (Stuttgart, Germany) · 2026Article
- Soil Warming Shifts Seasonal and Soil Depth-Related Root Growth Dynamics in Subarctic Grasslands.Global change biology · 2026Article
- Deep soil layers show the most pronounced genetic variation in wheat root length.Plant methods · 2026Article
- Deep roots through time and crops: insight from five seasons at DeepRootLab.The New phytologist · 2026Article
- A systematic comparison of transformers and ConvNets for root segmentation across nine datasets.Plant methods · 2026Article
- Leaf Analyzer: A fully automated and open-source tool for high-throughput leaf trait measurement.Plant phenomics (Washington, D.C.) · 2026Article
- Dual-guided asymmetric MP-former for rice root instance segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- Clinically validated dataset of 435 human colons segmented from CT colonography.Scientific data · 2026Article
- Root system architecture and drought adaptation: emerging tools and genetic insights.Frontiers in plant science · 2026Review
- Diversity of root system architecture and root-shoot biomass allocation in industrial hemp (Cannabis sativa L.).PloS one · 2026Article
- Winter wheat phenotyping for deep root growth and function, reduced water stress and increased uptake of deep N and water.Annals of botany · 2025Article
- Modern wheat has deeper roots than ancient wheats: is this an adaptation to higher productivity?Annals of botany · 2025Article
- Root system architecture reorganization under decreasing soil phosphorus lowers root system conductance of Zea mays.Annals of botany · 2025Article
- Deep learning in plant phenotyping: the first ten years.Plant phenomics (Washington, D.C.) · 2025Review
- Plant traits and associated ecological data from Afromontane grasslands of Maloti-Drakensberg, South Africa.Scientific data · 2025Article
14 more citing papers are in PubMed but not listed here.
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interface based software tool for the rapid training of deep neural networks for use in biological image analysis. We evaluate RootPainter by training models for root length extraction from chicory (Cichorium intybus L.) roots in soil, biopore counting, and root nodule counting. We also compare dense annotations with corrective ones that are added during the training process based on the weaknesses of the current model. Five out of six times the models trained using RootPainter with corrective annotations created within 2 h produced measurements strongly correlating with manual measurements. Model accuracy had a significant correlation with annotation duration, indicating further improvements could be obtained with extended annotation. Our results show that a deep-learning model can be trained to a high accuracy for the three respective datasets of varying target objects, background, and image quality with < 2 h of annotation time. They indicate that, when using RootPainter, for many datasets it is possible to annotate, train, and complete data processing within 1 d.
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