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Poncelet, N.

Publications and source records attributed to Poncelet, N..

2 recordsLinked to original sources
bioRxiv2026

Towards operational epidemiology of Pyricularia oryzae in sports turf: contribution of a multi-matrix molecular monitoring strategy at stadium scale

Gray leaf spot can cause severe turfgrass damage, particularly in intensively managed sports fields, while disease management remains challenging due to the rapid development of symptoms under favorable environmental conditions. Early detection of Pyricularia oryzae could therefore provide valuable information for anticipating disease development and supporting management decisions. This study aimed to develop a digital PCR (dPCR) molecular tool to support the monitoring and management of gray leaf spot disease in stadium turfgrass. The assay showed high sensitivity, consistently detecting P. oryzae when infected tissue represented only 0.05% of a mixture with healthy turfgrass, and enabled pathogen detection before symptom appearance under both controlled and field conditions. Different matrices were also investigated across 15 stadiums to identify the most relevant material for disease monitoring. Roots showed the highest prevalence of P. oryzae detection, including during asymptomatic periods, highlighting their potential as a relevant matrix for pathogen surveillance. In a stadium monitored several months after a gray leaf spot outbreak, P. oryzae DNA was still detected in roots from 27 of the 32 sampled zones despite the absence of visible symptoms. Overall, these results demonstrate the potential of dPCR for the early detection and monitoring of P. oryzae in stadium turfgrass, particularly through root sampling during periods when environmental conditions are not favorable for gray leaf spot development, and a spatio-temporal surveillance at stadium scale.

plant biology↗
bioRxiv2026

MIRA: an open source and user-friendly software to automate counting and sizing of fungal spores

BackgroundThe quantification of fungal spores constitutes a fundamental metric in phytopathology, serving as the primary variable for inoculum standardization and being used as a proxy for disease severity. Historically, spore quantification has relied on manual hemocytometry, which remains the most precise counting process to date, where chambers such as the Malassez slide are used to count a subsample of the inoculum. However, this method applied manually is highly labor-intensive, time-consuming, and can be prone to operator-dependent variability. To overcome these limitations, we introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms. Featuring a user-friendly graphical interface, MIRA is adaptable to multiple camera systems and supports advanced object detection models, including YOLOv11 and YOLOv26. ResultsWe demonstrate that MIRA can be used to accurately detect and count spores from several phytopathogenic fungi, automatically measure spore surface area, and to differentiate spores across different genera. In an exhaustive comparative analysis using Pyricularia oryzae spores as an example, MIRA was benchmarked against manual gold-standard counting slides (Malassez and Kova) and indirect spectrophotometric methods (SPARK). The P. oryzae model loaded via MIRA achieved a strong correlation (R = 0.96) with manual gold standards while reducing processing time by over 90% for high-concentration samples (10 spores/mL). Beyond this benchmark, we also successfully tested specific YOLO models designed to recognize macro- and microconidia of Fusarium oxysporum f. sp. cubense, a model for Pseudocercospora fijiensis, and a single multiclass model capable of identifying six different rice pathogenic fungi. We provide comprehensive tutorials for operating the software and training custom detection models for free using Roboflow and Google Colab. MIRA is available both as open-source Python code and as standalone executables for Windows and Linux. ConclusionsMIRA provides a rapid, accurate, and highly reproducible alternative to manual spore counting, effectively removing a major bottleneck in phytopathology workflows. By combining advanced YOLO-based deep learning with an accessible interface and comprehensive training resources, MIRA makes accessible automated image analysis for researchers without programming expertise. Moreover, MIRA drastically improves the efficiency of high-throughput disease phenotyping and can be adapted for a wide range of microscopic quantification tasks across various biological disciplines.

plant biology↗