# Development of imaging devices and algorithms to quantify Black Rot symptoms in grapevine under semi-controlled conditions: detached leaves in laboratory and growing cuttings in greenhouse

Marc Fermaud, Barna Keresztes, Antoine Guibert, Aymeric Deshayes, Ghislain Delestre, Xavier Burgun, Robin Raveau, Lionel Druelle, Jérôme Jolivet, Sébastien Gambier, et al.

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Marc Fermaud, Barna Keresztes, Antoine Guibert, Aymeric Deshayes, Ghislain Delestre, et al. Development of imaging devices and algorithms to quantify Black Rot symptoms in grapevine under semi-controlled conditions: detached leaves in laboratory and growing cuttings in greenhouse. IOBC-WPRS Working group IPM viticulture, Oct 2025, Mikulov, Czech Republic. hal-05385407

HAL Id: hal-05385407

https://hal.inrae.fr/hal-05385407v1

Submitted on 27 Nov 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Black rot (BR), caused by the fungal pathogen *Guignardia bidwellii*, is a historical and extremely harmful disease of grapevines (Fermaud et al, 2024, IOBC-WPRS Bulletin, 171, p.29). In France, where no anti-BR biocontrol product has been registered so far, the risk of BR epidemics may be rising following both i) the reduction in use of synthetic fungicides and ii) a near-future deployment of the grapevine cultivars resistant to powdery and downy mildews, but that are highly susceptible to the pathogen.

An acceleration of research is required for high-throughput BR phenotyping in order to promote research for developing biocontrol tools and further studying key epidemiological issues.

## Context & Objectives

## Image capture devices and database

### Two original image capture devices:

> **Lab device:** to observe symptoms on **detached symptomatic leaves** assessing BR severity (lesion size, density) and **counting pycnidia** conceptacles containing the asexual spores.

> **Greenhouse device:** to assess, more precisely and rapidly, BR severity in grapevine growing cuttings, notably in biocontrol bioassays according to Raveau et al (J. Fungi 2024, 10, 471. https://doi.org/10.3390/jof10070471).

### Three annotated databases:

> Detached leaves databases:

- DL_DB_1: approx 500 images at leaf scale

- DL_DB_2: approx 500 macro-images at infra-symptom scale (pycnidia counting)

> Young plant database: YP_DB

- YP_DB: 184 images from XXX young plant cuttings

Annotations conducted with CVAT software following a specific annotation protocol designed in this project AMBRE

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The methodology will be further developed and improved both from the epidemiological/biological as from the deep-learning/Al points of view.

> Possible confounding symptoms with foliar BR symptoms (e.g. biocontrol phytotoxicity) will be more tested/studied

> At the infra-symptom scale (pycnidia counts), there is still room for improvement: better segmentation, thresholds...

As **near-future research development**, we are going to address the vineyard issue with foliar BR detection and severity quantification based on field-images. A first database constitution is currently implemented in both Bordeaux and Cognac vineyard experimental plots (2025/2026).

As a more **long-term prospect**, such innovative methods would support BR biocontrol studies and decision-making for better managing the disease in commercial vineyards.

## Materials & Methods

Greenhouse trials were based on artificial BR inoculations (Raveau et al. *J. Fungi* 2024, 10, 471. https://doi.org/10.3390/jof10070471).

Studied growing cuttings issued from various biocontrol assays.

An image database based on 32 photographs per plant is in progress.

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### Greenhouse methodology for Black Rot assays

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## Deep learning algorithms

### At leaf scale, YOLO v11s (segmentation instance), with 3 steps:

- Detection, circle the symptoms into square

- Segmentation, detect the precise edges of each spot in a square

- Filtering, remove double detections

### At infra-symptom scale (pycnidia counting), U-Net adapted for small size object:

- Segmentation of the spots to avoid false detection outside spots

- Probability map of the pycnidia locations (+ thresholding)

- Counting the detected pycnidia

### At plant scale, the algorithms are still under development

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<figure><img src="image_8.png" /><figcaption>Infra-Symptom scale
Left = original
Right = probability map</figcaption></figure>

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## Results

### Leaf scale:

- R² = 0,97 spots surface measured by hand VS by algorithm

- Noted a confusion between different spots classes

### Infra-Symptom scale:

- R² = 0,59 number of pycnidia measured by hand VS by algorithm

- Under-estimation = then to try other thresholds on probability map

<figure><img src="image_10.png" /><figcaption>Leaf scale
X = spots surface by hand ; Y = spots surface by algorithm / Unit = number of pixel</figcaption></figure>

Infra-Symptom scale

X = pycnides counted by hand ;

Y = pycnides counted by algorithm

Unit = number of pycnidia

<figure><img src="image_11.png" /></figure>

## Conclusions and prospects