ArticlePlant methods2023
Quantification of the three-dimensional root system architecture using an automated rotating imaging system.
Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Non-destructive monitoring of root biomass in hydroponically grown leafy vegetables: comparison between machine learning-based RGB and hyperspectral imaging.Plant methods · 2026Article
- Characterizing Root Morphology, Water and Nitrogen Uptake of Fibrous-Root and Taproot Crops Under Transparent Soil.Plants (Basel, Switzerland) · 2025Article
- Climate-Resilient Crops: Integrating AI, Multi-Omics, and Advanced Phenotyping to Address Global Agricultural and Societal Challenges.Plants (Basel, Switzerland) · 2025Review
- Phenotyping, genome-wide dissection, and prediction of maize root architecture for temperate adaptability.iMeta · 2025Article
- Overcoming Challenges in Plant Biomechanics: Methodological Innovations and Technological Integration.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- TopoRoot+: computing whorl and soil line traits of field-excavated maize roots from CT imaging.Plant methods · 2024Article
- Making watercress (Frontiers in plant science · 2023Article
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Authors and funding
13 authors.
Funding
Abstract
backgroundCrop breeding based on root system architecture (RSA) optimization is an essential factor for improving crop production in developing countries. Identification, evaluation, and selection of root traits of soil-grown crops require innovations that enable high-throughput and accurate quantification of three-dimensional (3D) RSA of crops over developmental time.
resultsWe proposed an automated imaging system and 3D imaging data processing pipeline to quantify the 3D RSA of soil-grown individual plants across seedlings to the mature stage. A multi-view automated imaging system composed of a rotary table and an imaging arm with 12 cameras mounted with a combination of fan-shaped and vertical distribution was developed to obtain 3D image data of roots grown on a customized root support mesh. A 3D imaging data processing pipeline was developed to quantify the 3D RSA based on the point cloud generated from multi-view images. The global architecture of root systems can be quantified automatically. Detailed analysis of the reconstructed 3D root model also allowed us to investigate the Spatio-temporal distribution of roots. A method combining horizontal slicing and iterative erosion and dilation was developed to automatically segment different root types, and identify local root traits (e.g., length, diameter of the main root, and length, diameter, initial angle, and the number of nodal roots or lateral roots). One maize (Zea mays L.) cultivar and two rapeseed (Brassica napus L.) cultivars at different growth stages were selected to test the performance of the automated imaging system and 3D imaging data processing pipeline.
conclusionsThe results demonstrated the capabilities of the proposed imaging and analytical system for high-throughput phenotyping of root traits for both monocotyledons and dicotyledons across growth stages. The proposed system offers a potential tool to further explore the 3D RSA for improving root traits and agronomic qualities of crops.
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