Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology.
Arbuscular mycorrhizal fungi
Computer vision
Mask R-CNN
Mixed linear models
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
13 05 2024
13 05 2024
Historique:
received:
29
06
2023
accepted:
02
05
2024
medline:
14
5
2024
pubmed:
14
5
2024
entrez:
13
5
2024
Statut:
epublish
Résumé
The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.
Identifiants
pubmed: 38740920
doi: 10.1038/s41598-024-61181-5
pii: 10.1038/s41598-024-61181-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
10866Subventions
Organisme : U.S. Department of Energy
ID : DE-SC0021386
Organisme : Division of Biological Infrastructure
ID : NSF DBI-1946937
Informations de copyright
© 2024. The Author(s).
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