ArticlePlant phenomics (Washington, D.C.)2022
Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.).
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 20 papers.
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Who cites it
20 citing papers in PubMed.
- Innovative 3D photosynthetic trait assessment of slash pine using drone-LiDAR fusion and machine learning algorithms.Plant phenomics (Washington, D.C.) · 2026Article
- Effect of phosphorus deficiency on biomass and root system architecture in diverseFrontiers in plant science · 2026Article
- Root system architecture and drought adaptation: emerging tools and genetic insights.Frontiers in plant science · 2026Review
- DU-Net-L: an effective and lightweight segmentation model for alfalfa images that integrates the strengths of DeepLabV3+ and U-Net.aBIOTECH · 2025Article
- Climate-Resilient Crops: Integrating AI, Multi-Omics, and Advanced Phenotyping to Address Global Agricultural and Societal Challenges.Plants (Basel, Switzerland) · 2025Review
- Detection techniques for tomato diseases under non-stationary climatic conditions.Frontiers in plant science · 2025Article
- Effect of intra- and inter-specific plant interactions on the rhizosphere microbiome of a single target plant at different densities.PloS one · 2025Article
- The whole-genome dissection of root system architecture provides new insights for the genetic improvement of alfalfa (Horticulture research · 2025Article
- A genome-wide association study reveals novel loci and candidate genes associated with plant height variation in Medicago sativa.BMC plant biology · 2024Article
- Multi-locus genome-wide association study reveal genomic regions underlying root system architecture traits in Ethiopian sorghum germplasm.The plant genome · 2024Article
- GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa.G3 (Bethesda, Md.) · 2024Article
- Phenotyping Alfalfa (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
- Exploring root system architecture and anatomical variability in alfalfa (Medicago sativa L.) seedlings.BMC plant biology · 2023Article
- GWAS identifies candidate genes controlling adventitious rooting inHorticulture research · 2023Article
- Phenotyping ofPlant phenomics (Washington, D.C.) · 2023Article
- Integrated web portal for non-destructive salt sensitivity detection ofFrontiers in plant science · 2023Article
- Explainable deep learning in plant phenotyping.Frontiers in artificial intelligence · 2023Review
- Transcriptomics and metabolomics revealed that phosphate improves the cold tolerance of alfalfa.Frontiers in plant science · 2023Article
- Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.).Plant phenomics (Washington, D.C.) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Active breeding programs specifically for root system architecture (RSA) phenotypes remain rare; however, breeding for branch and taproot types in the perennial crop alfalfa is ongoing. Phenotyping in this and other crops for active RSA breeding has mostly used visual scoring of specific traits or subjective classification into different root types. While image-based methods have been developed, translation to applied breeding is limited. This research is aimed at developing and comparing image-based RSA phenotyping methods using machine and deep learning algorithms for objective classification of 617 root images from mature alfalfa plants collected from the field to support the ongoing breeding efforts. Our results show that unsupervised machine learning tends to incorrectly classify roots into a normal distribution with most lines predicted as the intermediate root type. Encouragingly, random forest and TensorFlow-based neural networks can classify the root types into branch-type, taproot-type, and an intermediate taproot-branch type with 86% accuracy. With image augmentation, the prediction accuracy was improved to 97%. Coupling the predicted root type with its prediction probability will give breeders a confidence level for better decisions to advance the best and exclude the worst lines from their breeding program. This machine and deep learning approach enables accurate classification of the RSA phenotypes for genomic breeding of climate-resilient alfalfa.
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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.