ArticlePlant phenomics (Washington, D.C.)2020
Nondestructive 3D Image Analysis Pipeline to Extract Rice Grain Traits Using X-Ray Computed Tomography.
Article in Plant phenomics (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- DiffPlantCT: a training-free, annotation-free approach to cross-species plant CT image segmentation.Plant methods · 2026Article
- MaizeField3D: A curated 3D point cloud and procedural model dataset of field-grown maize from a diversity panel.Plant phenomics (Washington, D.C.) · 2026Article
- Understanding seed germination responses to low-dose X-rays: the role of seed quality, variety, and density.Plant methods · 2025Article
- Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits.Plant methods · 2025Article
- A deep learning-based micro-CT image analysis pipeline for nondestructive quantification of the maize kernel internal structure.Plant phenomics (Washington, D.C.) · 2025Article
- Evaluation of 3D seed structure and cellular traits in-situ using X-ray microscopy.Scientific reports · 2025Article
- MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.Journal of experimental botany · 2025Article
- The seed morphospace, a new contribution towards the multidimensional study of angiosperm sexual reproductive biology.Annals of botany · 2024Article
- Automatic Measurement of Seed Geometric Parameters Using a Handheld Scanner.Sensors (Basel, Switzerland) · 2024Article
- Toward robust and high-throughput detection of seed defects in X-ray images via deep learning.Plant methods · 2024Article
- Plant microphenotype: from innovative imaging to computational analysis.Plant biotechnology journal · 2024Review
- A High-Throughput Method for Accurate Extraction of Intact Rice Panicle Traits.Plant phenomics (Washington, D.C.) · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- A Pathway to Assess Genetic Variation of Wheat Germplasm by Multidimensional Traits with Digital Images.Plant phenomics (Washington, D.C.) · 2023Article
- High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning.Frontiers in plant science · 2023Article
- Research on the evolutionary history of the morphological structure of cotton seeds: a new perspective based on high-resolution micro-CT technology.Frontiers in plant science · 2023Article
- Automated extraction of pod phenotype data from micro-computed tomography.Frontiers in plant science · 2023Article
- The influence of grain shape and size on the relationship between porosity and permeability in sandstone: a digital approach.Scientific reports · 2022Article
- 3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping.Breeding science · 2022Article
- An Intelligent Rice Yield Trait Evaluation System Based on Threshed Panicle Compensation.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The traits of rice panicles play important roles in yield assessment, variety classification, rice breeding, and cultivation management. Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive; moreover, these methods cannot obtain 3D grain traits. In this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits. After 104 samples were tested, the
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.