terclim by ICS banner
IVES 9 IVES Conference Series 9 GiESCO 9 Use of artificial intelligence for the prediction of microbial diseases of grapevine and optimisation of fungicide application

Use of artificial intelligence for the prediction of microbial diseases of grapevine and optimisation of fungicide application

Abstract

Context and purpose of the study – Plasmopara viticola, the causal agent of downy mildew (DM), and Uncinula necator, the causal agent of powdery mildew (PM), are two of the main phytopathogenic microorganisms causing major economic losses in the primary sector, especially in the wine sector, by wilting bunches and leaves with a consequent decrease in the photosynthetic rate of the plant and in the annual yield. Currently, the most widespread methods for planning spraying are based on the 3-10 rule, which states that the first application should take place when: (i) the air temperature is greater than 10°C; (ii) shoots are equal or greater than 10 cm; and (iii) a minimum of 10 mm rainfall within 24–48 hours has occurred, or at the beginning of the bud break with periodic applications according to the manufacturer’s instructions. These rules are applied to prevent possible infectious events that may occur while new tissues are forming on the vine, which are more susceptible to infection. In addition, establishing a starting point for spraying is crucial, as the pathogen can complete the infection cycle in one to two weeks depending on environmental conditions. However, this approach is not completely effective, as the chemical compound can be washed off the leaves, photo-oxidized, applied at higher doses than necessary, negatively affecting the biodiversity of the agroecosystem, or in discordance with the life cycle of the pathogen. Therefore, the aim of the VitiGEOSS disease early warning service focuses on the application of Artificial Intelligence models to predict the appearance of diseases in the vineyard and consequently apply fungicide products at the right time and dose, minimizing crop losses and the use of pesticides and water.

Material and methods – A total of six study plots located in three countries of the European Union were used: Quinta do Bomfim (Portugal), L’Aranyó (Spain) and Mirabella Eclano (Italy). Disease monitoring was carried out from March to October 2021 and 2022, with field visits every 7 days to measure the percentage of incidence and severity of infection on leaves. To analyze these data, eight different Machine and Deep Learning models were evaluated to classify the degree of infection and provide treatment recommendations using climatic features and phenological change events in the plant.

Results – The three study regions showed significant climatic differences. On one hand, the best prediction algorithm was the one based on conditional probability obtaining a precision metric of 90% for DM and 79% for PM, respectively. On the other hand, a comparative analysis showed that the incorporation of plant phenological stages in the model increased the accuracy rate up to 9%, so it would be interesting to consider the effect of other physiological aspects of the plant for future analyses. Finally, it should be noted that model recommendations reduce water consumption by 21% on average. In any case, it is advisable to continue collecting data, as two production seasons can lead to overfitting issues, and to incorporate climatological and phenological predictions to be able to develop short- and medium-term warnings.

DOI:

Publication date: July 5, 2023

Issue: GiESCO 2023

Type: Poster

Authors

Marta OTERO1, Boris BASILE2*, Jordi ONRUBIA1, Josep PIJUAN1

1Eurecat, Centre Tecnològic de Catalunya, Unit of Applied Artificial Intelligence, 25003 Lleida, Spain
2Department of Agricultural Sciences, University of Naples Federico II, 80055 Naples, Italy

Contact the author*

Keywords

Mildew diseases, risk anticipation, effective vineyard management, Artificial Intelligence

Tags

GiESCO | GIESCO 2023 | IVES Conference Series

Citation

Related articles…

New insights about sensory contribution of grape stems during winemaking: role of astilbin, a sweet polyphenol

In this video recording of the IVES science meeting 2025, Marie Le Scanff (University of Bordeaux, UMR 1366 Oenologie, Institut des Sciences de la Vigne et du Vin, Villenave d’Ornon, France) speaks about the sensory contribution of grape stems during winemaking and about the role of astilbin, a sweet polyphenol. This presentation is based on an original article accessible for free on OENO One and on a technical article published on IVES Technical Reviews.

Application of non-Saccharomyces yeasts in peculiar winemaking, sparkling and sweet wines: biological acidification, prise de mousse, aroma profile. Two cases of study

In this video recording of the IVES science meeting 2025, Raffaele Guzzon (Fondazione Edmund Mach, Centro di Trasferimento Tecnologico, San Michele all’Adige (TN), Italy) speaks about the application of non-Saccharomyces yeasts in peculiar winemaking, sparkling and sweet wines (biological acidification, prise de mousse, aroma profile). This presentation is based on an original article accessible for free on OENO One.

What does the concept of natural wine evoke in the minds and senses of tasters? Effect of the level of expertise.

In this video recording of the IVES science meeting 2025, Jordi Ballester (Centre des sciences du goût et de l’alimentation, CNRS, INRAE, Institut Agro, Université Bourgogne-Franche-Comté, Dijon, France) and María-Pilar Sáenz-Navajas (Instituto de Ciencias de la Vid y el Vino (ICVV) (CSIC-UR-GR), La Rioja, Spain) speak about the concept of natural wine. This presentation is based on an original article accessible for free on OENO One.

The role of rootstock and its genetic background in plant mineral status

In this video recording of the IVES science meeting 2025, Marine Morel (EGFV, Univ. Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, Villenave-d’Ornon, France) speaks about the role of rootstock and its genetic background in plant mineral status. This presentation is based on an original article accessible for free on OENO One.

Rootstocks and climate change: adding up means learning faster

In this video recording of the IVES science meeting 2025, Gonzaga Santesteban (Public University of Navarra, Pamplona, Spain) speaks about rootstocks, climate change and meta-analysis. This presentation is based on an original article accessible for free on OENO One.