High-throughput characterization of vine varieties under natural conditions
Managers: Eric Duchêne (SVQV) & Lionel Ley (UEAV)

Establishing relationships between genetic information and plant characteristics to create the grape varieties of the future

The main objectives of the Génétique et Amélioration de la Vigne (GAV) team are to acquire knowledge and develop new methods and resources to create vine varieties that are durably resistant to disease and produce high-quality wines in the context of climate change.

To this end, it is conducting research aimed at identifying key regions of the genome that govern traits of agronomic interest (disease resistance, quality traits, productivity and adaptability), and developing modern breeding methods based on predictive approaches adapted to this species.

This work requires the culture and characterization of large numbers of different grapevine genotypes. The team currently has over 5,000 genotypes in the greenhouse or in the vineyard. Describing them in detail in a viticultural context, i.e. characterizing their phenotype, in order to establish genotype-phenotype relationships, is a challenge impossible to meet without the contribution of modern technologies.

Un équipement innovant

PhénoVigne 1



To meet this challenge, the UMR "Santé de la Vigne et Qualité du Vin" (Vine Health and Wine Quality) at the Université de Strasbourg - INRAE Grand Est center in Colmar has received funding from the Grand Est region and INRAE. In 2020, it acquired a data acquisition system mounted on a vineyard tractor, comprising a LiDAR (Light imaging Detection And Ranging) system, generating 3D point clouds of plants, and RGB cameras. This equipment is used in the field by the Unité Expérimentale Agronomique et Viticole of the INRAE Grand Est - Colmar center.

This first set, built on the expertise of the PHENOME-EMPHASIS infrastructure, was completed in spring 2023 with an inertial unit, flashes and a multispectral camera.

Operational functionalities

The position of the sensors is obtained at a frequency of 50hz, on the one hand from GPS data corrected by information from an RTK terminal, and on the other from attitude information supplied by the inertial unit (Letekoma, 2022). More than 2,400 plots of 3 to 5 stocks are currently georeferenced, and the system provides a separate file for each of them during PhénoVigne passages.

PhénoVigne Nuage points

Point clouds

LiDAR equipment can be used to generate point clouds. The presence of two LiDARs enables us to cover the entire vegetation curtain. These data have already been used to identify relationships between genetic variations on chromosome 1 and vine growth parameters (Chedid et al, 2023).



RGB images (example of a corrected RGB image, taken during the day) or mutispectral images should enable us to quantify the leaf surface and its activity using indices such as NDVI, and ultimately enable us to detect and quantify disease attacks.

Les images RGB (exemple d'image RGB corrigée, prise de jour) ou mutispectrales doivent nous permettre de quantifier la surface foliaire et son activité à l'aide d'indices tels que le NDVI, et à terme, nous permettre de détecter et de quantifier les attaques de maladies.

Current projects

Since 2020, PhénoVigne data have been obtained on several experimental sites in Colmar, Bergheim and Wintzenheim.

They will be used as part of Jacem Ben Hamden's thesis, which began in early 2024 thanks to funding from INRAE and the Grand Est region.

Controlling sugar levels and maintaining correct acidity in grapes is one of the major challenges in adapting vines to climate change. Part of the genetic variability lies in ripening start dates (veraison dates), but the leaf/fruit ratio undoubtedly plays an important role. The aim of this thesis project is to implement methods for characterizing photosynthetic capacity that enable an objective breakdown of the "sugar accumulation" character in berries.

Secondly, we will look at the detection and quantification of foliar disease attacks.

The medium-term use of an autonomous vector carrying the entire acquisition system is under study.


Chedid E, Avia K, Dumas V, Ley L, Reibel N, Butterlin G, Soma M, Lopez-Lozano R, Baret F, Merdinoglu D, Duchêne E (2022) Electronic phenotyping is effective in describing vine growth and detecting associated genetic loci. Plant Phenomics 5. doi :10.34133/plantphenomics.0116

Modification date: 18 April 2024 | Publication date: 07 March 2024 | By: INRAE Grand Est-Colmar