On-The-Go VIS plus SW - NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard

Visible-Short Wave Near Infrared (VIS + SW - NIR) spectroscopy is a real alternative to break down the next barrier in precision viticulture allowing a reliable monitoring of grape composition within the vineyard to facilitate the decision-making process dealing with grape quality sorting and harvest scheduling, for example. On-the-go spectral measurements of grape clusters were acquired in the field using a VIS + SW - NIR spectrometer, operating in the 570-990 nm spectral range, from a motorized platform moving at 5 km/h. Spectral measurements were acquired along four dates during grape ripening in 2017 on the east side of the canopy, which had been partially defoliated at cluster closure. Over the whole measuring season, a total of 144 experimental blocks were monitored, sampled and their fruit analyzed for total soluble solids (TSS), anthocyanin and total polyphenols concentrations using standard, wet chemistry reference methods. Partial Least Squares (PLS) regression was used as the algorithm for training the grape composition parameters' prediction models. The best cross-validation and external validation (prediction) models yielded determination coefficients of cross-validation (R-cv(2)) and prediction (R-P(2)) of 0.92 and 0.95 for TSS, R-cv(2) = 0.75, and R-p(2) = 0.79 for anthocyanins, and R-cv(2) = 0.42 and R-p(2) = 0.43 for total polyphenols. The vineyard variability maps generated for the different dates using this technology illustrate the capability to monitor the spatiotemporal dynamics and distribution of total soluble solids, anthocyanins and total polyphenols along grape ripening in a commercial vineyard

Hyperspectral imaging to characterize table grapes

Table grape quality is of importance for consumers and thus for producers. Its objective quality is usually determined by destructive methods mainly based on sugar content. This study proposed to evaluate the possibility of hyperspectral imaging to characterize table grapes quality through its sugar (TSS), total flavonoid (TF), and total anthocyanin (TA) contents. Different data pretreatments (WD, SNV, and 1st and 2nd derivative) and different methods were tested to get the best prediction models: PLS with full spectra and then Multiple Linear Regression (MLR) were realized after selecting the optimal wavelengths thanks to the regression coefficients (coefficients) and the Variable Importance in Projection (VIP) scores. All models were good at showing that hyperspectral imaging is a relevant method to predict sugar, total flavonoid, and total anthocyanin contents. The best predictions were obtained from optimal wavelength selection based on coefficients for TSS and from VIPs optimal wavelength windows using SNV pre-treatment for total flavonoid and total anthocyanin content. Thus, good prediction models were proposed in order to characterize grapes while reducing the data sets and limit the data storage to enable an industrial use

Análisis de técnicas de aprendizaje automático en el sector de la viticultura

Este Trabajo Fin de Grado ofrece contribuciones relevantes al estado del arte de la investigación relacionada con la tecnología en el sector de la viticultura. En primer lugar, se presenta una exhaustiva visión de las técnicas de Inteligencia Artificial empleadas en los últimos años en el ámbito de la vinificación a partir del estudio de artículos que inciden en las técnicas empleadas y cómo estas ayudan a mejorar diversos aspectos, como puede ser la calidad del vino o incluso factores relacionados con la producción o cantidad del vino producido. A partir de estos datos, podemos ofrecer un recorrido documentado sobre las inclinaciones actuales de emplear este gran recurso, la Inteligencia Artificial. Este estudio se centra en las técnicas de Aprendizaje Automático que se pueden integrar en la gestión y procesos de vinificación de viñedos actuales para brindar resultados relevantes y útiles para la industria. Por otra parte, el segundo componente del trabajo destaca la importancia de las Bases de Datos empleadas, ofreciendo ejemplos y unas breves pinceladas sobre características importantes que influyen a la hora de afrontar un estudio con muestras de vino. Este documento concluye ofreciendo una interpretación de las nuevas tendencias que se adoptarán en el futuro cercano para mejorar un sector enormemente influyente en nuestro país y a nivel mundial.This Final Project offers relevant contributions to the state of the art’s research related to technology in the viticulture sector. On the one hand, an exhaustive vision of Artificial Intelligence techniques used in recent years in the field of winemaking is presented. In order to meet that goal, the study of articles that affect the techniques used and how they help to improve various aspects -such as wine quality or factors related to the production or quantity of the wine produced- are used. From these data, we can offer a documented tour of the current inclinations to use this great resource, Artificial Intelligence. This study focuses on Machine Learning techniques that can be integrated into current vineyard management and winemaking processes to deliver industry-relevant and useful results. On the other hand, the second component of the current work highlights the importance of the databases used, offering examples and a few brief notes on important characteristics that influence when facing a study with wine samples. This document concludes offering an interpretation of the new trends that will be adopted in the near future to improve a greatly influential sector in our country.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaGrado en Ingeniería de Tecnologías de Telecomunicació

Feasibility study using remote sensing technologies to improve zonal vineyard management

The primary purpose of this research was to examine the feasibility of using remote sensing data to improve efficiency of zonal vineyard management. To achieve this goal, correlation analysis between the significant vineyard management variables and different remote sensing data analysis tools were undertaken. The variables included leaf water potential, soil moisture, canopy size, vine health, vineyard yield, and fruit composition, which further impacts wine quality. The remote sensing data analysis tools included normalized difference vegetation index (NDVI), and other indices extracted from electromagnetic reflectance data of grapevine leaves and canopies. In each site, sentinel vines (i.e., 72-81) were identified in a grid form. GPS-based geolocation was carried out for six Cabernet Franc vineyards in Ontario's Niagara wine country. Even though remote sensing data analysis tools were not associated with several other important variables for quality grape production, this research still confirmed that remote sensing data analysis has significant potential to differentiate specific zones of canopy size, water stress, yield, some superior fruit compositions, and the resulting wine sensory attributes within a single vineyard site. This study also confirmed that the mechanism of plant defense systems against biotic stress could have impacts on the spectral behaviour of grapevine leaves and hyperspectral remote sensing technologies could be applied as a tool to identify the spectral behaviour changes due to stress. Overall, this study verified the feasibility of remote sensing technologies to enhance the efficiency of vineyard management in the correlation of data from various remote sensing data-analysis techniques and viticulturally important variables for plant health and growth, and fruit and wine quality. As a first step to develop a site-specific crop management (SSCM) model for vineyard management, it also proposes future research opportunities to test and develop an efficient vineyard management decision making model

Identifying individual nutrient deficiencies of grapevine leaves using hyperspectral imaging

The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images to identify healthy and individual nutrient deficiencies of grapevine leaves. Features such as mean reflectance, mean first derivative reflectance, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD) were employed at various stages in the ultraviolet (UV), visible (VIS) and near-infrared (N.I.R.) regions for our experiment. Leaves were examined visually in the laboratory and grouped as either healthy (i.e. control) or unhealthy. Then, the features of the leaves were extracted from these two groups. In a second experiment, features of individual nutrient-deficient leaves (e.g., N, K and Mg) were also analysed and compared with those of control leaves. Furthermore, a customised support vector machine (SVM) was used to demonstrate that these features can be utilised with a high degree of effectiveness to identify unhealthy samples and not only to distinguish from control and nutrient deficient but also to identify individual nutrient defects. Therefore, the proposed work corroborated that HS imaging has excellent potential to analyse features based on healthiness and individual nutrient deficiencies of grapevine leaves

Thermography to assess grapevine status and traits opportunities and limitations in crop monitoring and phenotyping – a review

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoClimate change and the increasing water shortage pose increasing challenges to agriculture and viticulture, especially in typically dry and hot areas such as the Mediterranean and demand for solutions to use water resources more effectively. For this reason, new tools are needed to precisely monitor water stress in crops such as grapevine in order to save irrigation water, while guaranteeing yield. Imaging technologies and remote sensing tools are becoming more common in agriculture and plant/crop science research namely to perform phenotyping/selection or for crop stress monitoring purposes. Thermography emerged as important tool for the industry and agriculture. It allows detection of the emitted infrared thermal radiation and conversion of infrared radiation into temperature distribution maps. Considering that leaf temperature is a feasible indicator of stress and/or stomatal behavior, thermography showed to be capable to support characterization of novel genotypes and/or monitor crop’s stress. However, there are still limitations in the use of the technique that need to be minimized such as the accuracy of thermal data due to variable weather conditions, limitations due to the high costs of the equipment/platforms and limitations related to image analysis and processing to extract meaningful thermal data. This work revises the role of remote sensing and imaging in modern viticulture as well as the advantages and disadvantages of thermography and future developments, focusing on viticultureN/

Utilization of unmanned aerial vehicles and proximal sensing to detect Riesling vineyard variability

A single vineyard block can consist of significant spatial variability for several grape-growing attributes. The ability to detect and subsequently respond to this variation can lead to improved vineyard management, a growing practice termed precision viticulture. The overall goal of this research study was to determine if remote-sensing technologies could be used to detect Riesling vineyard variability, thus enhancing precision viticulture implementation. Approximately 80 grapevines in a grid pattern were geo-located within each of six commercial Riesling vineyards across the Niagara Peninsula in Ontario. From these grapevines the following variables were measured to determine their vineyard variation: soil and vine water status, vine size/vigor, winter hardiness, virus titer, yield components, and berry composition. Subsequently, remote-sensing technologies collected thermal [by unmanned aerial vehicle (UAV)] and multispectral (by UAV and ground-based proximal sensing technology GreenSeeker™) data from each block. Multispectral data were transformed into the Normalized Difference Vegetation Index (NDVI). Vineyard UAV NDVI maps were further used for selective harvesting of areas corresponding to low vs. high NDVI and wines made from these two zones were compared chemically and sensorially. The hypothesis was that remote and proximal sensing technologies could accurately detect vineyard variation for manually collected variables and further implicate differences in wine attributes upon zonal harvesting. Direct positive correlations were observed between remotely and proximally sensed NDVI vs. vine size, leaf stomatal conductance, leaf water potential, virus infection, yield, berry weight, and titratable acidity and inverse correlations with Brix and potentially-volatile terpene concentration. Maps created from remotely and proximally sensed data demonstrated similar spatial configurations to interpolated maps of these variables. In general, GreenSeeker NDVI demonstrated the most significant relationships with measured variables compared to UAV NDVI and UAV thermal data. Wines created from areas of low vs high NDVI differed inconsistently in their wine pH. Sensorially, in certain sites and vintages, panelists were able to distinguish between wines made from low vs high NDVI zones. Overall, remote sensing demonstrates the ability to detect vineyard areas differing in measures of vine health, size, yield, berry composition, and wine attributes, though more research is needed to understand the inconsistent results observed between vineyard sites and vintages

Aplicación de técnicas espectroscópicas vibracionales al estudio de la extractabilidad de compuestos fenólicos procedentes de subproductos enológicos.

Las características de la uva en el momento de la vendimia condicionan en gran medida la calidad del vino elaborado a partir de ellas. En regiones de clima cálido, las altas temperaturas ambientales, acentuadas en las últimas décadas por los efectos del cambio climático, originan un desfase entre la madurez tecnológica de la uva, cada vez más temprana y la madurez fenólica, cada vez más tardía, que desencadena, entre otros, problemas de estabilidad cromática en el envejecimiento. Ante esta situación se pone de manifiesto la necesidad de implementar medidas de adaptación que permitan preservar la calidad de estos vinos. En este sentido, el aprovechamiento de subproductos de la industria vitivinícola ricos en compuestos fenólicos,como pueden ser las virutas de madera cruda de las tonelerías,añadidas en etapas iniciales de la vinificación, se presenta como una estrategia enológica, nueva y sostenible, para paliar los efectos del cambio climático en la calidad de los vinos tintos.Los compuestos fenólicos de la madera, que pasarán al vino durante el proceso de maceración, junto con los compuestos fenólicos procedentes de la uva, determinarán la composición fenólica del vino y condicionarán sus características cromáticas y organolépticas. Tradicionalmente,la composición fenólica, y la facilidad de cesión de estos compuestos al vino desde diferentes matrices, se ha controlado por métodos de análisis fisicoquímicos que,en muchos casos, resultan complejos y tediosos. En las últimas décadas se han desarrollado técnicas espectroscópicas que permiten estimar de forma rápida, no destructiva y respetuosa con el medio ambiente,distintos parámetros de interés enológico. En este trabajo, inicialmente se ha evaluado el efecto que tiene la adición de chips de madera en la extracción de compuestos antociánicos del hollejo de uva tinta clasificada hiperespectralmente como uva con baja capacidadde cesión de dichos compuestos.Se ha comprobadoque la adición de copigmentos provenientes de la madera, que contribuyen a mejorar el color del vino,noalterael equilibrio de extracción de antocianos del hollejo. Se ha utilizado el análisis de imagen hiperespectral en la región del infrarrojo cercano y la regresión por mínimos cuadrados parciales (MPLS)para analizar de forma rápida el contenido total yel contenido extraíble de fenoles totales y flavanoles en semillas de uva tinta.De forma similar, se ha desarrollado un modelo MPLS para predecir el contenido fenólico extraíble en viruta cruda de roble. Se ha evaluado la capacidad dela espectroscopía NIR portátilpara estimar in situ de forma rápida el contenido fenólico extraíble delhollejo de uva y de la viruta cruda de roble. La influencia de las condiciones ambientalesen el viñedo en el momento de la adquisición espectral pueden condicionar los resultados obtenidos para los hollejos, de forma que los modelos presentan errores que comprometen su aplicación con fines predictivos. Sin embargo, en virutas de roble, los modelos propuestos para lapredicción del contenido extraíble de fenoles totales y de elagitaninosofrecen resultados satisfactorios para todos los parámetros evaluados. Así mismo, utilizando espectroscopía en el infrarrojo medio por transformada de Fourier y espectroscopíaRaman se han caracterizado muestrasde viruta cruda de madera y se han relacionado las características espectrales con la facilidad de extracción de compuestos fenólicos. De esta forma, se ha podido confirmar la relación existente entre la extractabilidad de compuestos fenólicos en viruta de madera y la composición y distribución de los componentes de su pared celular.La microscopía electrónica de barrido (SEM) aporta información topográfica complementaria que respalda la relación entre la estructura de la madera y los niveles de extractabilidad fenólica. Por último, se ha evaluado objetivamente el potencial enológico de la viruta cruda de roble, subproducto de tonelería. Para ello se ha determinado,en una situación real de vinificación en tinto, la influencia de la adición post-fermentativade virutas en la composición fenólica y la calidad cromática de vinos tintos de variedad Syrah.Los resultados obtenidos demuestran que la adición pos-fermentativa de viruta de madera cruda representa un procedimiento útil para la elaboración de vinos tintos en clima cálido, ya que produce una mejora tanto de la composición fenólica como de la calidad y estabilidad cromática.Premio Extraordinario de Doctorado U

Grapes and Wine

Grape and Wine is a collective book composed of 18 chapters that address different issues related to the technological and biotechnological management of vineyards and winemaking. It focuses on recent advances, hot topics and recurrent problems in the wine industry and aims to be helpful for the wine sector. Topics covered include pest control, pesticide management, the use of innovative technologies and biotechnologies such as non-thermal processes, gene editing and use of non-Saccharomyces, the management of instabilities such as protein haze and off-flavors such as light struck or TCAs, the use of big data technologies, and many other key concepts that make this book a powerful reference in grape and wine production. The chapters have been written by experts from universities and research centers of 9 countries, thus representing knowledge, research and know-how of many regions worldwide