ArticlePlant phenomics (Washington, D.C.)2021
Semiautomated 3D Root Segmentation and Evaluation Based on X-Ray CT Imagery.
Article in Plant phenomics (Washington, D.C.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- DiffPlantCT: a training-free, annotation-free approach to cross-species plant CT image segmentation.Plant methods · 2026Article
- RootXplorer: A computer vision-based 3D phenotyping platform for high-throughput quantification and spatio-temporal analysis of root system penetrability.Plant phenomics (Washington, D.C.) · 2025Article
- Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits.Plant methods · 2025Article
- Local mapping of root orientation traits by X-ray micro-CT and 3d image analysis: A study case on carrot seedlings grown in simulated vs real weightlessness.Plant methods · 2024Article
- TopoRoot+: computing whorl and soil line traits of field-excavated maize roots from CT imaging.Plant methods · 2024Article
- Cassava Breeding and Cultivation Challenges in Thailand: Past, Present, and Future Perspectives.Plants (Basel, Switzerland) · 2024Review
- Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X-ray computed tomography volumes with high noise levels.Plant methods · 2024Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
- Quantification of the three-dimensional root system architecture using an automated rotating imaging system.Plant methods · 2023Article
- 3D U-Net Segmentation Improves Root System Reconstruction from 3D MRI Images in Automated and Manual Virtual Reality Work Flows.Plant phenomics (Washington, D.C.) · 2023Article
- "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.Frontiers in plant science · 2023Article
- 3D segmentation of plant root systems using spatial pyramid pooling and locally adaptive field-of-view inference.Frontiers in plant science · 2023Article
- Editorial: Synthetic data for computer vision in agriculture.Frontiers in plant science · 2023Article
- "Chamber #8" - a holistic approach of high-throughput non-destructive assessment of plant roots.Frontiers in plant science · 2023Article
- X-ray driven peanut trait estimation: computer vision aided agri-system transformation.Plant methods · 2022Article
- Improving the efficiency of plant root system phenotyping through digitization and automation.Breeding science · 2022Article
- 4DRoot: Root phenotyping software for temporal 3D scans by X-ray computed tomography.Frontiers in plant science · 2022Article
- A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google's Colaboratory.Frontiers in plant science · 2022Article
- TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging.Plant methods · 2021Article
- 4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography.Plant methods · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComputed X-ray tomography (CTX) is a high-end nondestructive approach for the visual assessment of root architecture in soil. Nevertheless, in order to evaluate high-resolution CTX data of root architectures, manual segmentation of the depicted root systems from large-scale volume data is currently necessary, which is both time consuming and error prone. The duration of such a segmentation is of importance, especially for time-resolved growth analysis, where several instances of a plant need to be segmented and evaluated. Specifically, in our application, the contrast between soil and root data varies due to different growth stages and watering situations at the time of scanning. Additionally, the root system itself is expanding in length and in the diameter of individual roots.
objectiveFor semiautomated and robust root system segmentation from CTX data, we propose the
resultsUsing CTX volume data of full-grown bean plants as well as time-resolved (3D + time) growth studies of cassava plants,
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Registered trials
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