Aims: The aims of this study were to (1) detect alterations in the reflectance spectra of vines with fungal diseases, (2) map these alterations, and (3) determine the best wavelengths which may be used as early indicators of fungal diseases in vines.
Aim: The aim of this work was to evaluate the use of Variable Rate Application technologies based on prescription maps in commercial vineyards with large intra-parcel variability to achieve a more sustainable distribution of Plant Protection Products (PPP)
Aims: This work aimed i) to calibrate the accuracy of estimating vineyard water status by crop water stress index (CWSI) compared to stem water potential; ii) to determine the time interval during the day that best correlates to stem water potential and iii) to understand the its usefulness.
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.
Aim: Through machine-learning modelling of plant water status from environmental characteristics, this work aims to develop a model able to predict grape phenolic composition in space and time to guide selective harvest decisions at the estate scale.