IVES 9 IVES Conference Series 9 International Terroir Conferences 9 Terroir 2020 9 Category: History and innovation of terroir

History and innovation of terroir

History and innovation of terroirIVES Conference SeriesTerroir 2020

Gamma-ray spectrometry In Burgundy vineyard for high resolution soil mapping

Aim: A soil mapping methodology based on gamma-ray spectrometry and soil sampling has been applied for the first time in Burgundy. The purpose of this innovative high-resolution mapping is to delimit soil areas, to define elementary units of soil for terroir characterization and vineyard management. The added value of this integrated approach is a continuous geophysical mapping of the soil with an investigation depth of 60cm.

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History and innovation of terroirIVES Conference SeriesTerroir 2020

Using image analysis for assessing downy mildew severity in grapevine

Aim: Downy mildew is a crucial disease in viticulture. In-field evaluation of downy mildew has been classically based on visual inspection of leaves and fruit. Nevertheless, non-invasive sensing technologies could be used for disease detection in grapevine. The aim of this study was to assess downy mildew severity in grapevine leaves using machine vision.

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History and innovation of terroirIVES Conference SeriesTerroir 2020

Prospects of thermal imaging as a non-invasive tool to assess water status for irrigation scheduling in commercial vineyards

Aim: Irrigated viticulture is expanding worldwide mainly as a short-term adaptation strategy to climate change. Plant-based methods are increasingly being used for irrigation scheduling in commercial vineyards. Canopy temperature (TC) has long been recognized as an indicator of plant water status. TC, but also the thermal stress indices, e.g. crop water stress index (CWSI) and stomatal

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History and innovation of terroirIVES Conference SeriesTerroir 2020

Detection of spider mite using artificial intelligence in digital viticulture

Aim: Pests have a high impact on yield and grape quality in viticulture. An objective and rapid detection of pests under field conditions is needed. New sensing technologies and artificial intelligence could be used for pests detection in digital viticulture. The aim of this work was to apply computer vision and deep learning techniques for automatic detection of spider mite symptoms in grapevine under field conditions. 

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