ArticleScientific reports2021
Fully-automated root image analysis (faRIA).
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- Advancing crop improvement through GWAS and beyond in mung bean.Frontiers in plant science · 2024Pooled it
- RootHairAreaFinder: an image processing method for quantifying barley root growth and root hairs simultaneously in a flat rhizotron system.Plant methods · 2026Article
- COMPOSITUM 2/ WHEAT FRIZZY PANICLE dosage dependently regulates inflorescence and root architecture in Triticeae cereals.The Plant journal : for cell and molecular biology · 2026Article
- Machine Learning-Driven Construction of High-Yielding Cucumber Plant Architectures in Greenhouse Environments.Plant biotechnology journal · 2026Article
- Diversity of Root System Architecture in Mediterranean Maize Inbred Lines Provides New Breeding Opportunities to Improve Stress Resilience and Resource Efficiency.Plants (Basel, Switzerland) · 2026Article
- Dual-guided asymmetric MP-former for rice root instance segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- Root-TransUNet enables high-throughput phenotyping ofFrontiers in plant science · 2026Article
- Clade specific divergence, cumulative haplo-pheno analysis, and genomic prediction of cytokinin oxidase (CKX) gene family under drought stress in rice.Plant molecular biology · 2025Article
- ClearDepth Method for Evaluations of Root Depth in Soil-Filled Pots.Bio-protocol · 2025Article
- Python algorithm package for automated Estimation of major legume root traits using two dimensional images.Scientific reports · 2025Article
- Research on Fine-Grained Phenotypic Analysis of Temporal Root Systems - Improved YoloV8seg Applied for Fine-Grained Analysis of In Situ Root Temporal Phenotypes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Rice homolog of Arabidopsis Xylem NAC domain 1 (OsXND1), a NAC transcription factor regulates drought stress responsive root system architecture in indica rice.Molecular genetics and genomics : MGG · 2024Article
- Variation in forest root image annotation by experts, novices, and AI.Plant methods · 2024Article
- RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs.Scientific reports · 2024Article
- The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review.Plant phenomics (Washington, D.C.) · 2024Review
- As good as human experts in detecting plant roots in minirhizotron images but efficient and reproducible: the convolutional neural network "RootDetector".Scientific reports · 2023Article
- A method of cotton root segmentation based on edge devices.Frontiers in plant science · 2023Article
- Awn Image Analysis and Phenotyping Using BarbNet.Plant phenomics (Washington, D.C.) · 2023Article
- RootPainter: deep learning segmentation of biological images with corrective annotation.The New phytologist · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.
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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.