Management of grapevine trunk diseases: knowledge transfer, current strategies and innovative strategies adopted in Europe

Since the early 1990s, grapevine trunk diseases (GTDs) have posed threats for viticulture. Esca complex, Eutypa- and Botryosphaeria- diebacks, mostly detected in adult vineyards, are currently responsible for considerable economic losses in the main vine-growing areas of the world. Other GTDs, such as Petri- (Esca complex) and Black-foot diseases, are emerging problems in grapevine nurseries (resulting in grafting failures and/or loss of saleable plants) and in young vineyards. The impacts of GTDs in modern viticulture depend on several factors, some related to their complexity, and others linked to host plant characteristics, changes in vineyard management and to the scarcity of simple tools for their control. For these reasons control of GTDs remains difficult, also depending on knowledge transfer from research to field and vice versa. This paper outlines the main preventive and curative techniques currently applied, scientifically tested or not that have resulted from the outcomes of “Winetwork”, a European Union funded project with special emphasis on the promising and innovative approaches.

Caracterización aromática de variedades minoritarias del Piedemonte Pirenaico

Mediante los conservatorios de variedades y la colaboración ciudadana se han identificado más de un centenar de variedades minoritarias procedentes de las regiones que rodean los Pirineos. Con 78 de dichas variedades identificadas se han realizado más de 200 microvinificaciones que han permitido, por una parte, evaluar su potencial enológico y, por otra, caracterizar sensorial y químicamente dichas variedades

Monitoring grape berry maturity by hyperspectral imaging: Potential of robust methods for sugar content prediction

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Combination of multivariate curve resolution with factorial discriminant analysis for the detection of grapevine diseases using hyperspectral imaging. A case study: flavescence dorée

International audienceHyperspectral imaging is an emergent technique in viticulture that can potentially detect bacterial diseases in a non-destructive manner. However, the main problem is to handle the substantial amount of information obtained from this type of data, for which reliable data analysis tools are necessary. In this work, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) is proposed to detect the flavescence dorée grapevine disease from hyperspectral imaging. The main purpose of MCR-ALS in this work was to provide chemically meaningful basic spectral signatures and distribution maps of the constituents needed to describe both healthy and infected leaf images by flavescence dorée. MCR scores (distribution maps) were used as the starting information for FDA to distinguish between healthy and infected pixels/images. Such an approach is presumably more powerful than the direct use of FDA on the raw imaging data, since MCR scores are compressed and noise-filtered information on pixel properties, which makes them more suitable for discrimination analysis. High levels of correct pixel discrimination rates (CR = 85.1%) for the MCR-ALS/FDA discrimination model were obtained. The model presents a lesser ability to determine infected leaves than healthy leaves. Nevertheless, only two images were misclassified. Therefore, the proposed strategy constitutes a good approach for the detection of flavescence dorée that could be potentially used to detect other phytopathologies

Combinaison de la Méthode de Résolution Multivariée de Courbes avec la Méthode d’Analyse Discriminante Factorielle pour la Détection des Maladies de la Vigne à l'aide de l'Imagerie Hyperspectrale. Une Étude de Cas: La Flavescence Dorée

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Evaluation of a robust regression method (RoBoost-PLSR) to predict biochemical variables for agronomic applications: Case study of grape berry maturity monitoring

International audienceVisible and near infrared spectroscopy (VIS-NIR) is increasingly being transferred from laboratory to industry for in-line and portable applications in various domains. By intensively using VIS-NIR spectroscopy, some abnormal observations may certainly arise. It is then important to properly handle outliers to elaborate effective prediction models. The objective of this study is to investigate the potential of using a robust method called Roboost-PLSR to improve prediction model performances for a viticulture application. This work focuses on a case study to predict sugar content in grape berries of three different grape varieties of Vitis Vinifera in a maturity monitoring context. Hyperspectral images were acquired of grape berries of Syrah, Fer-Servadou and Mauzac varieties. Reference measurements of sugar levels were made in the laboratory by densimetric baths

Combination of Multivariate Curve Resolution with Factorial Discriminant Analysis for the Detection of Flavescence Dorée Grapevine Disease using Hyperspectral Imaging

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Hyperspectral images of grapevine leaves including healthy leaves and leaves with biotic and abiotic symptoms

Abstract A hyperspectral imaging database was collected on two hundred and five grape plant leaves. Leaves were measured with a hyperspectral camera in the visible/near infrared spectral range under controlled conditions. This dataset contains hyperspectral acquisition of grape leaves of seven different varieties. For each variety, acquisitions were performed on healthy leaves and leaves with foliar symptoms caused by different grapevine diseases showing clear symptoms of biotic or abiotic stress on other organs. For each leaf, chemical measurements such as chlorophyll and flavonol contents were also performed