ArticleScientific reports2017
Computer vision and machine learning for robust phenotyping in genome-wide studies.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Beyond the Classics: The Synergy of AI and Genomics Reveals an Expanded Repertoire of Pigmentation Genes.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2026Review
- Molecular signatures and machine learning driven stress biomarkers for rainbow trout aquaculture and climate adaptation.Scientific reports · 2025Article
- Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies.Journal of biomedical informatics · 2025Article
- Plant stress detection using multimodal imaging and machine learning: from leaf spectra to smartphone applications.Frontiers in plant science · 2025Article
- Evaluating classification tools for the prediction of in-vitro microbial pyruvate yield from organic carbon sources.PloS one · 2024Article
- Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding.Frontiers in plant science · 2024Review
- Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages.Plant methods · 2022Article
- Classification of rice leaf blast severity using hyperspectral imaging.Scientific reports · 2022Article
- Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane.Plants (Basel, Switzerland) · 2022Review
- Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean.Frontiers in plant science · 2022Article
- Comparing Early Transcriptomic Responses of 18 Soybean (International journal of molecular sciences · 2021Article
- Review
- Identifying novel transcript biomarkers for hepatocellular carcinoma (HCC) using RNA-Seq datasets and machine learning.BMC cancer · 2021Article
- Twelve years of GWAS discoveries for osteoporosis and related traits: advances, challenges and applications.Bone research · 2021Review
- The role of micropeptides in biology.Cellular and molecular life sciences : CMLS · 2021Review
- Next-Generation Breeding Strategies for Climate-Ready Crops.Frontiers in plant science · 2021Review
- Dissecting the Root Phenotypic and Genotypic Variability of the Iowa Mung Bean Diversity Panel.Frontiers in plant science · 2021Article
- UAS-Based Plant Phenotyping for Research and Breeding Applications.Plant phenomics (Washington, D.C.) · 2021Review
- Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline (SNAP).Plant phenomics (Washington, D.C.) · 2021Article
- Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance.Scientific reports · 2020Article
Corrections and comments
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8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic studies of abiotic stress iron deficiency chlorosis (IDC) of soybean is reported. IDC classification and severity for an association panel of 461 diverse plant-introduction accessions was evaluated using an end-to-end phenotyping workflow. The workflow consisted of a multi-stage procedure including: (1) optimized protocols for consistent image capture across plant canopies, (2) canopy identification and registration from cluttered backgrounds, (3) extraction of domain expert informed features from the processed images to accurately represent IDC expression, and (4) supervised ML-based classifiers that linked the automatically extracted features with expert-rating equivalent IDC scores. ML-generated phenotypic data were subsequently utilized for the genome-wide association study and genomic prediction. The results illustrate the reliability and advantage of ML-enabled image-phenotyping pipeline by identifying previously reported locus and a novel locus harboring a gene homolog involved in iron acquisition. This study demonstrates a promising path for integrating the phenotyping pipeline into genomic prediction, and provides a systematic framework enabling robust and quicker phenotyping through ground-based systems.
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