How geographical origin and vineyard management influence cv. Cabernet-Sauvignon in Chile – Machine learning based quality prediction

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality under climate change conditions. This last is expected to drastically modify plant growth, with possible negative effects, especially in arid and semi-arid regions of Europe on the viticultural sector. In this context, the monitoring of spatial behavior of grapevine during the growing season represents an opportunity to improve the plant management, winegrowers’ incomes, and to preserve the environmental health, but it has additional costs for the farmer. Nowadays, UAS equipped with a VIS-NIR multispectral camera (blue, green, red, red-edge, and NIR) represents a good and relatively cheap solution to assess plant status spatial information (by means of a limited set of spectral vegetation indices), representing important support in precision agriculture management during the growing season. While differences between UAS-based multispectral imagery and point-based spectroscopy are well discussed in the literature, their impact on plant status estimation by vegetation indices is not completely investigated in depth. The aim of this study was to assess the performance level of UAS-based multispectral (5 bands across 450-800nm spectral region with a spatial resolution of 5cm) imagery, reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500 nm with spatial resolution of <2 m) through Convolutional Neural Network (CNN) approach, and point-based field spectroscopy (collecting 600 wavelengths across 400-1000 nm spectral region with a surface footprint of 1-2 cm) in a plant status estimation application, and then, using Bayesian regularization artificial neural network for leaf chlorophyll content (LCC) and plant water status (LWP) prediction. The test site is a Greco vineyard of southern Italy, where detailed and precise records on soil and atmosphere systems, in-vivo plant monitoring of eco-physiological parameters have been conducted.

Monitoring vine water status is a major challenge for vineyard management because it influences both yield and harvest quality. It is also a challenge at the territorial scale for identifying periods of high water restriction or zones regularly impacted by water stress. This information is of major importance for defining collective strategies, anticipating harvest logistic or applying for irrigation authorisation. At this spatial scale, existing tools and methods for monitoring vine water status are few and often require strong assumptions (e.g. water balance model). This paper proposes to consider a collaborative collection of observations by winegrowers and wine industry stakeholders (crowdsourcing) as an interesting alternative. Indeed, it allows the collection of a large number of field observations while pooling the collection effort. However, the feasibility of such a project and its interest in monitoring vine water status at regional scale has never been tested.

The objective of this article is to explore the possibility of making a regional map of vine water status based on crowdsourcing observations. It is based on the study of the free mobile application ApeX-Vigne, which allows the collection of observations about vine shoot growth. This information is easy to collect and can be considered, under certain conditions, as a proxy for vine water status. This article presents the first results obtained from the nearly 18,000 observations collected by winegrowers and wine industry stakeholders during 2019, 2020 and 2021 seasons. It presents the vine shoot growth maps obtained at regional scale and their evolution over the three vintages studied. It also proposes an analysis of the factors that favoured the number of observations collected and those that favoured their quality. These results open up new perspectives for monitoring vine water status at a regional scale but above they provide references for other crowdsourcing projects in viticulture.

The wine sector is of great relevance and tradition in Mediterranean countries, however, it may be most susceptible to climate change. In recent years, wine production is facing changes worldwide, both at environmental as well as commercial levels, due to global warming and the shift in consumers’ preferences. Wine growers and wine makers are in search of solutions that allow to face these new challenges. One of the most promising initiatives in the long term is the introduction of new plant materials, specifically intraspecific hybridizations between premium varieties that may improve traditional germplasm in its adaptation to climate change. These inter-varietal crosses have the potential to generate quality wines, whilst maintaining the regional typicity, and constitute an attractive alternative for the consumer due to their sensory attributes. In this study, we have evaluated wines from 29 intraspecific Garnacha x Tempranillo hybrids in two different locations, with the aim to assess their oenological potential and sensory attributes. Thirteen of the selections were white and 16 were red. Microvinifications were conducted with two or three replications depending on grape availability. Conventional oenological parameters were determined for all wines. The sensory evaluation and hedonic scores were given by five experts. Red selections obtained higher quality scores than white ones. Among the white selections with higher quality scores, GT-41 Varea and GT-159 Varea outstand, due to their high total acidity and high malic acid content. Regarding red selections, GT-57 Varea and GT-57 UR were perceived as higher in quality, highlighted for their moderate alcoholic and high anthocyanin content. Our results indicate that intraspecific hybridization may be a powerful tool for adapting traditional cultivars to climate change in Rioja.

A large varietal collection including over 1700 varieties was maintained in Conegliano, ITA, since the 1950s. Phenological data on a subset of 400 grape varieties including wine grapes, table grapes, and raisins were acquired at bud break, flowering, veraison, and ripening since 1964. Despite the efforts in maintaining and acquiring data over such an extensive collection, the data set has varying degrees of missing cases depending on the variety and the year. This is ubiquitous in phenology datasets with significant size and length. In this work, we evaluated four state-of-the-art methods to estimate missing values in this phenological series: k-Nearest Neighbour (kNN), Multivariate Imputation by Chained Equations (mice), MissForest, and Bidirectional Recurrent Imputation for Time Series (BRITS). For each phenological stage, we evaluated the performance of the methods in two ways. 1) On the full dataset, we randomly hold-out 10% of the true values for use as a test set and repeated the process 1000 times (Monte Carlo cross-validation). 2) On a reduced and almost complete subset of varieties, we varied the percentage of missing values from 10% to 70% by random deletion. In all cases, we evaluated the performance on the original values using normalized root mean squared error. For the full dataset we also obtained performance statistics by variety and by year. MissForest provided average errors of 17% (3 days) at budbreak, 14% (4 days) at flowering, 14.5% (7 days) at veraison, and 17% (3 days) at maturity. We completed the imputations of the Conegliano dataset, one of the world’s most extensive and varied phenological time series and a steppingstone for future climate change studies in grapes. The dataset is now ready for further analysis, and a rigorous evaluation of imputation errors is included.

homogeneous zones by intersecting several partial zonings of major factors that influence vineyard growth. Each of them follows specific process from their corresponding disciplines. Soil zoning specifically refers to a Soil Resource Inventory map that has traditionally been generated by conventional soil mapping methods. These methods have shortcomings in reaching fine cartographic and categorical details and involve significant expenses, which undermines their applicability. A new framework named Digital Soil Mapping has introduced quantitative models by statistical techniques to establish soil-landscape relationships and is able to provide intensive scale cartography.

In the present study, a microzoning at 1:10.000 scale is generated from an initial zoning, where the conventional soil map with polytaxic map units is replaced by a new one from digital techniques that disaggregates them. The comparison between the zonings considers a quantitative evaluation of capability for each Homogeneous Terroir Unit by means of the Viticultural Quality Index and its categorization based on its distribution by map. The spatial intersection of both maps gives rise to a confusion matrix in which the flows of class variations after the substitution are assessed.

The results show a five-fold increase in the number of Homogeneous Terroir Units identified and a larger differentiation among them, evidenced by a wider range in the capability index distribution. Both elements are accompanied by an increase in the detection of areas of higher potential within previously undervalued uniform zones.These features are a direct effect of the improvements brought by Digital Soil Mapping techniques and would verify the advantages of their implementation in the Integrated Terroir zoning. Eventually, such new highly detailed terroir units would benefit precision viticulture and sustainable management practices.