Related Experiment Video
Updated: Jul 3, 2026
08:28
Mycorrhizal Maps as a Tool to Explore Colonization Patterns and Fungal Strategies in the Roots of Festuca rubra and Zea mays
Published on: August 26, 2022
From manual scoring to machine learning: recent developments in image-based arbuscular mycorrhizal fungi root
Annabelle Bouvette1, Sulaimon Basiru2, Mohamed Hijri3,4
1Institut de Recherche en Biologie Végétale (IRBV), Département de Sciences Biologiques, Université de Montréal, Montréal, QC, H1X 2B2, Canada.
Mycorrhiza
|July 2, 2026
Summary
Quantifying arbuscular mycorrhizal fungi (AMF) in plant roots is complex. This review compares traditional and advanced image analysis methods to help researchers select the best tools for accurate AMF colonization assessment.
Area of Science:
- Plant Pathology
- Mycology
- Bioimaging
Background:
- Quantifying arbuscular mycorrhizal fungi (AMF) colonization in plant roots presents challenges due to diverse, often outdated, methodologies.
- A gap exists in comprehensive reviews of recent image analysis techniques for AMF root quantification.
Purpose of the Study:
- To synthesize the conceptual structure and thematic map of AMF colonization studies from 2001-2026.
- To critically compare traditional and emerging image analysis methods for AMF quantification.
- To guide researchers in selecting appropriate AMF quantification tools based on experimental needs.
Main Methods:
- Literature review and synthesis of studies on AMF root colonization (2001-2026).
- Examination of traditional methods (e.g., gridline intersect).
- Evaluation of emerging image analysis tools (e.g., VBA-AMF, MycoPatt, WinRHIZO, ImageJ, Zeiss Intellesis, AMFinder, TAIM, Mask R-CNN) and deep learning approaches.
Main Results:
- Identified a wide array of techniques for AMF root colonization assessment, ranging from classical to advanced.
- Detailed comparison of the principles, capabilities, and limitations of various image analysis tools.
- Highlighted the growing potential of image-based methods for accurate, reproducible, and high-throughput AMF quantification.
Conclusions:
- The selection of AMF quantification methods requires careful consideration of experimental objectives and technical constraints.
- Image analysis, particularly deep learning, offers significant advantages for improving AMF root colonization assessment.
- This review provides a crucial guide for researchers navigating the complex landscape of AMF quantification techniques.
