ArticlePlant methods2020
Computer vision and machine learning enabled soybean root phenotyping pipeline.
Article in Plant methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- 2D-to-3D Image Reconstruction in Agriculture: A Review of Methods, Challenges, and AI-Driven Opportunities.Sensors (Basel, Switzerland) · 2026Review
- YOLO-based high-throughput phenotyping pipeline for soybean nodulation traits in genomic research.Frontiers in plant science · 2026Article
- Integrating Drought Stress Signaling and Smart Breeding for Climate-Resilient Crops: Regulatory Mechanisms and Genetic Strategies.Plants (Basel, Switzerland) · 2025Review
- Non-Invasive Phenotyping of Sugar Beet and Maize Roots Using Field-Scale Spectral Electrical Impedance Tomography.Plant, cell & environment · 2025Article
- Climate-Resilient Crops: Integrating AI, Multi-Omics, and Advanced Phenotyping to Address Global Agricultural and Societal Challenges.Plants (Basel, Switzerland) · 2025Review
- Validating the contributions of stem and root traits to barley lodging in western Canada.Frontiers in plant science · 2025Article
- Soybean genomics research community strategic plan: A vision for 2024-2028.The plant genome · 2024Article
- Genome-Wide Association study for root system architecture traits in field soybean [Glycine max (L.) Merr.].Scientific reports · 2024Article
- High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.Plants (Basel, Switzerland) · 2024Article
- Root phenotypes for improved nitrogen capture.Plant and soil · 2024Review
- PREPs: An Open-Source Software for High-Throughput Field Plant Phenotyping.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
- Fast and Efficient Root Phenotyping via Pose Estimation.Plant phenomics (Washington, D.C.) · 2024Article
- Automatic Root Length Estimation from Images Acquired In Situ without Segmentation.Plant phenomics (Washington, D.C.) · 2024Article
- Review
- Application of an Improved 2-Dimensional High-Throughput Soybean Root Phenotyping Platform to Identify Novel Genetic Variants Regulating Root Architecture Traits.Plant phenomics (Washington, D.C.) · 2023Article
- "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.Frontiers in plant science · 2023Article
- Explainable deep learning in plant phenotyping.Frontiers in artificial intelligence · 2023Review
- Soybean Seedling Root Segmentation Using Improved U-Net Network.Sensors (Basel, Switzerland) · 2022Article
- Cassava root crown phenotyping using three-dimension (3D) multi-view stereo reconstruction.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRoot system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis.
resultsThis high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle.
conclusionsThis system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.
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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.