ArticleScientific reports2023
Quantifying root colonization by a symbiotic fungus using automated image segmentation and machine learning approaches.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Recent advances in the defense mechanisms of arbuscular mycorrhizal fungi in antibiotic-contaminated soil ecosystems.Archives of microbiology · 2026Review
- Novel imaging approaches for visualizing root-mycorrhizal fungal interactions.Journal of experimental botany · 2026Review
- From manual scoring to machine learning: recent developments in image-based arbuscular mycorrhizal fungi root quantification.Mycorrhiza · 2026Review
- Fungal ecology in the age of 'omics.The New phytologist · 2026Review
- Fungal Frontiers in (Bio)sensing.Biosensors · 2026Review
- Histochemical Staining of Arbuscular Mycorrhizal Roots for Quantification of Fungal Colonization, High-Resolution Imaging, and Localization of Symbiotic Gene Expression.Methods in molecular biology (Clifton, N.J.) · 2026Article
- RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Arbuscular mycorrhizas (AM) are one of the most widespread symbiosis on earth. This plant-fungus interaction involves around 72% of plant species, including most crops. AM symbiosis improves plant nutrition and tolerance to biotic and abiotic stresses. The fungus, in turn, receives carbon compounds derived from the plant photosynthetic process, such as sugars and lipids. Most studies investigating AM and their applications in agriculture requires a precise quantification of the intensity of plant colonization. At present, the majority of researchers in the field base AM quantification analyses on manual visual methods, prone to operator errors and limited reproducibility. Here we propose a novel semi-automated approach to quantify AM fungal root colonization based on digital image analysis comparing three methods: (i) manual quantification (ii) image thresholding, (iii) machine learning. We recognize machine learning as a very promising tool for accelerating, simplifying and standardizing critical steps in analysing AM quantification, answering to an urgent need by the scientific community studying this symbiosis.
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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 Socratic graph.