ArticlePlant phenomics (Washington, D.C.)2022
Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline.
Article in Plant phenomics (Washington, D.C.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery.ArXiv · 2026Article
- WaveUNet+: Preserving Root System Architecture Integrity in In Situ Root Segmentation via a Unified Spectral-Spatial Framework.Plants (Basel, Switzerland) · 2026Article
- Machine Learning-Driven Construction of High-Yielding Cucumber Plant Architectures in Greenhouse Environments.Plant biotechnology journal · 2026Article
- Dual-guided asymmetric MP-former for rice root instance segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- Root system architecture reorganization under decreasing soil phosphorus lowers root system conductance of Zea mays.Annals of botany · 2025Article
- Seeing the unseen: A novel approach to extract latent plant root traits from digital images.Plant phenomics (Washington, D.C.) · 2025Article
- Climate-Resilient Crops: Integrating AI, Multi-Omics, and Advanced Phenotyping to Address Global Agricultural and Societal Challenges.Plants (Basel, Switzerland) · 2025Review
- Stomata morphology measurement with interactive machine learning: accuracy, speed, and biological relevance?Plant methods · 2025Article
- VRoot: A VR-Based application for manual root system architecture reconstruction.Plant phenomics (Washington, D.C.) · 2025Article
- Identification of phenotypic and transcriptomic signatures underpinning maize crown root systems.Plant phenomics (Washington, D.C.) · 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
- Engineering plant holobionts for climate-resilient agriculture.The ISME journal · 2025Review
- Minirhizotron measurements can supplement deep soil coring to evaluate root growth of winter wheat when certain pitfalls are avoided.Plant methods · 2024Article
- Automated seminal root angle measurement with corrective annotation.AoB PLANTS · 2024Article
- Variation in forest root image annotation by experts, novices, and AI.Plant methods · 2024Article
- New interactive machine learning tool for marine image analysis.Royal Society open science · 2024Article
- Automatic Root Length Estimation from Images Acquired In Situ without Segmentation.Plant phenomics (Washington, D.C.) · 2024Article
- Fast and Efficient Root Phenotyping via Pose Estimation.Plant phenomics (Washington, D.C.) · 2024Article
- The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review.Plant phenomics (Washington, D.C.) · 2024Review
- Multi-year belowground data of minirhizotron facilities in Selhausen.Scientific data · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using "RootPainter." Then, an automated feature extraction from the segments is carried out by "RhizoVision Explorer." To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation (
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Registered trials
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