Vine field monitoring using high resolution remote sensing images: segmentation and characterization of rows of vines

Premium red wines are often aged in oak barrel. This widespread winemaking process is used, among others, to provide roundness and complexity to the wine. The study of wine evolution during barrel aging is crucial to better ensure control of wine quality. ¹H-NMR has already been proved to be an efficient tool to monitor winemaking process [1]. Indeed, it is a non-destructive technique, it requires a small amount of sample and a short time of analysis, yet it provides clues about several chemical families.

The Maillard reaction (MR) is a non-enzymatic reaction between reducing sugars and amino acids, resulting in the production of volatile and flavour-active compounds.

The increasing vulnerability of food systems is a pressing challenge amplified by global interconnectedness.

Nitrogen fertilization is an important practice to guarantee vineyards sustainability and performance over years, while ensuring berry quality. However, achieving a precise nitrogen fertilization to meet specific objectives of production is difficult. There is a lack of knowledge on the impact of nitrogen fertilizers (soil/foliar; organic/mineral) and different levels of fertilization on the interactions between carbon and nitrogen cycles within the vine. Crop models may be useful in that purpose because they can provide new insights of the effects of fertilization in carbon and nitrogen storage. The objective of this study is to build a model to simulate grapevine carbon and nitrogen content in vines to evaluate the impact of different fertilization strategies in vine growth and yield.

Optimizing the use of water and improving irrigation strategies has become increasingly important in most winegrowing countries due to the consequences of climate change, which are leading to more frequent droughts, heat waves, or alteration of precipitation patterns. Optimized irrigation scheduling can only be based on a reliable knowledge of the vineyard water status.

In this context, this work aims at the development of a novel methodology, using a contactless, miniaturized, low-cost NIR spectral tool to monitor (on-the-go) the vineyard water status variability. On-the-go spectral measurements were acquired in the vineyard using a NIR micro spectrometer, operating in the 900–1900 nm spectral range, from a ground vehicle moving at 3 km/h. Spectral measurements were collected on the northeast side of the canopy across four different dates (July 8th, 14th, 21st and August 12th) during 2021 season in a commercial vineyard (3 ha). Grapevines of Vitis vinifera L. Graciano planted on a VSP trellis were monitored at solar noon using stem water potential (Ψs) as reference indicators of plant water status. In total, 108 measurements of Ψs were taken (27 vines per date).

Calibration and prediction models were performed using Partial Least Squares (PLS) regression. The best prediction models for grapevine water status yielded a determination coefficient of cross-validation (r2cv) of 0.67 and a root mean square error of cross-validation (RMSEcv) of 0.131 MPa. This predictive model was employed to map the spatial variability of the vineyard water status and provided useful, practical information towards the implementation of appropriate irrigation strategies. The outcomes presented in this work show the great potential of this low-cost methodology to assess the vineyard stem water potential and its spatial variability in a commercial vineyard.