Future opportunities of proximal near infrared spectroscopy approaches to determine the variability of vineyard water status

Background and Aims: Non-destructive, reliable, fast and automated plant-based methods for the assessment of the water status of a grapevine are necessary to design irrigation strategies. The goal of this work was to test the capability of near infrared (NIR) spectroscopy using a vehicle-mounted and remote NIR sensor without plant contact (contactless) to assess the water status of grapevines in the vineyard. Methods and Results: An NIR spectrometer (11002100 nm) mounted on an all-terrain vehicle was used to acquire spectra (contactless, in stop-and-go mode) from leaves of water-stressed and non-stressed vines of Riesling at two timings during the season. Calibration and cross-validation models yielded R2 c = 0.95 and R2 cv = 0.88 for the estimation of the stomatal conductance measured in the same grapevines. Conclusions: The study demonstrates that NIR spectroscopy may become a potential tool for on-the-go assessment of proximal plant water status, although further research will be required for full confirmation. Significance of the Study: The NIR technology tested, capable of being installed on vineyard machinery, paves the way to collect data on plant water status at high spatial and temporal resolution to assist in irrigation scheduling

Calibration of non-invasive fluorescence-based sensors for the manual and on-the-go assessment of grapevine vegetative status in the field

Background and Aims: Optical sensors can accomplish frequent and spatially widespread non-destructive monitoring of plant nutrient status. The main goal was to calibrate a fluorescence sensor, used both manually (MXH) and on-the-go (MXM), for the assessment of the spatial variability in the vineyard of the concentration of chlorophyll, flavonol and nitrogen in grapevine leaves, against that of a leaf-clip type optical sensor (DX4). Methods and Results: Measurements were taken in a commercial vineyard on the adaxial and abaxial sides of leaves of nine Vitis vinifera L. cultivars, manually with the DX4 and MXH, and with the MXM mounted on an all-terrain vehicle. A significant correlation was obtained for the chlorophyll and nitrogen indices of MXH and DX4 (R2 > 0.90) and of MXM and DX4 (R2 > 0.74), and the calibration equations were defined. A similar spatial distribution was achieved for the chlorophyll, flavonol and nitrogen indices of the leaves. Conclusions: The capability of the fluorescence sensor, used manually and on-the-go, for characterising the nutritional status of grapevines was demonstrated. Significance of the Study: This work reports the first calibration of the hand-held and on-the-go fluorescence sensor to assess key nutritional parameters of grapevines. The applicability of this sensor on-the-go to characterise the spatial variability of the vegetative status of a vineyard for the delineation of homogeneous management zones was proved

A new method for assessment of bunch compactness using automated image analysis

Bunch compactness is a key feature determining grape and wine composition because tight bunches show a less homogeneous ripening, and are prone to greater fungal disease incidence. The Organisation Internationale de la Vigne et du Vin descriptor, the most recent method for the assessment of bunch compactness, requires visual inspection and trained evaluators, and provides subjective and qualitative values. The aim of this work was to develop a methodology based on image analysis to determine bunch compactness in a non-invasive, objective and quantitative way. Methods and Results: Ninety bunches of nine different red cultivars of Vitis viniferaL. were photographed with a colour camera, and their bunch compactness was determined by visual inspection. A predictive partial least squares (PLS) model was developed in order to estimate bunch compactness from the morphological features extracted by automated image analysis, after the supervised segmentation of the images. The PLS model showed a capability of 85.3% for predicting correctly the rating of bunch compactness. The most discriminant variables of the model were highly correlated with the tightness of the berries in the bunch (proportion of visibility of berries, rachis and holes) and with the shape of the bunch (roundness, compactness shape factor and aspect ratio). Conclusions: The non-invasive, image analysis methodology presented here enables the quantitative assessment of bunch compactness, thereby providing precise objective information for this key parameter. Significance of the Study: A quantitative, objective and accurate system based on image analysis was developed as an alternative to current visual methods for the estimation of bunch compactness. This novel method could be applied to the classification of table grapes and/or at the receival point of wineries for sorting and assessment of wine grapes before vinification