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Assessing vines' vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features). The specific features were selected based on extensive literature research, including especially the fields of precision agri- and viticulture. To integrate only vine canopy-exclusive features into ML classifications, different feature types were extracted and spatially aggregated (zonal statistics), based on a combined pixel- and object-based image-segmentation-technique-created vine row mask around each single grapevine position. The extracted canopy features were progressively grouped into seven input feature groups for model training. Model overall performance metrics were optimized with grid search-based hyperparameter tuning and repeated-k-fold-cross-validation. Finally, ML-based growth class prediction results were extensively discussed and evaluated for overall (accuracy, f1-weighted) and growth class specific- classification metrics (accuracy, user- and producer accuracy). [ABSTRACT FROM AUTHOR]

Grapevines are subjected to many physiological and environmental stresses that influence their vegetative and reproductive growth. Water stress, cold damage, and pathogen attacks are highly relevant stresses in many grape-growing regions. Precision viticulture can be used to determine and manage the spatial variation in grapevine health within a single vineyard block. Newer technologies such as remotely piloted aircraft systems (RPASs) with remote sensing capabilities can enhance the application of precision viticulture. The use of remote sensing for vineyard variation detection has been extensively investigated; however, there is still a dearth of literature regarding its potential for detecting key stresses such as winter hardiness, water status, and virus infection. The main objective of this research is to examine the performance of modern remote sensing technologies to determine if their application can enhance vineyard management by providing evidence-based stress detection. To accomplish the objective, remotely sensed data such as the normalized difference vegetation index (NDVI) and thermal imaging from RPAS flights were measured from six commercial vineyards in Niagara, ON, along with the manual measurement of key viticultural data including vine water stress, cold stress, vine size, and virus titre. This study verified that the NDVI could be a useful metric to detect variation across vineyards for agriculturally important variables including vine size and soil moisture. The red-edge and near-infrared regions of the electromagnetic reflectance spectra could also have a potential application in detecting virus infection in vineyards. [ABSTRACT FROM AUTHOR]

The primary purpose of this study was to improve our understanding of remote sensing technologies and their potential application in vineyards to monitor yields and fruit composition, which could then be used for selective harvesting and winemaking. For yield and berry composition data collection, representative vines from the vineyard block were selected and geolocated, and the same vines were surveyed for remote sensing data collection by the multispectral and thermal sensors in the RPAS in 2015 and 2016. The spectral reflectance data were further analyzed for vegetation indices to evaluate the correlation between the variables. Moran's global index and map analysis were used to determine spatial clustering patterns and correlations between variables. The results of this study indicated that remote sensing data in the form of vegetation indices from the RPAS were positively correlated with yield and berry weight across sites and years. There was a positive correlation between the thermal emission and berry pH, berry phenols, and anthocyanins in certain sites and years. Overall, remote sensing technology has the potential to monitor and predict grape quality and yield, but further research on the efficacy of this data is needed for selective harvesting and winemaking. [ABSTRACT FROM AUTHOR]

Grapevines (Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dorée, esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges. [ABSTRACT FROM AUTHOR]

Purpose: Site-specific field management operations represent one of the fundamental principles of precision viticulture. The purpose of the research is to observe and analyse the evolution of a vineyard over three consecutive years to understand which factors most significantly influence the quality of the vineyard's production. Methods: The research involved technologically advanced tools for crop monitoring, such as remote and proximal sensors for vegetation surveys. In association, grape quality analyses were performed through laboratory analysis, constructing geostatistical interpolation maps and matrix correlation tables. Results: Both remote and proximal sensing instruments demonstrated their ability to effectively estimate the spatial distribution of vegetative and quality characteristics within the vineyard. Information obtained from GNDVI and CHM proved to be valuable and high-performance tools for assessing field variability. The differentiated plant management resulted in uniform production quality characteristics, a change evident through the monitoring techniques. Conclusion: The research highlights the effectiveness of using advanced technological instruments for crop monitoring and their importance in achieving uniformity in production quality characteristics through differentiated plant management. From the results obtained, it was possible to observe how differentiated plant management led to a uniformity of production quality characteristics and how the monitoring techniques can observe their evolution. This result represents a positive accomplishment in field management during the three monitoring years, responding to the principles and objectives of precision agriculture. [ABSTRACT FROM AUTHOR]

This study presents a method based on retrofitted low-cost and easy to implement tracking devices, used to monitor the whole harvesting process in viticulture, to map yield and harvest quality parameters in viticulture. The method consists of recording the geolocation of all the machines (harvest trailers and grape harvester) during the harvest to spatially re-allocate production parameters measured at the winery. The method was tested on a vineyard of 30 ha during the whole 2022 harvest season. It has identified harvest sectors (HS) associated with measured production parameters (grape mass and harvest quality parameters: sugar content, total acidity, pH, yeast assimilable nitrogen, organic nitrogen) and calculated production parameters (potential alcohol of grapes, yield, yield per plant) over the entire vineyard. The grape mass was measured at the vineyard cellar or at the wine-growing cooperative by calibrated scales. The harvest quality parameters were measured on grape must samples in a commercial laboratory specialized in oenological analysis and using standardized protocols. Results validate the possibility of making production parameters maps automatically solely from the time and location records of the vehicles. They also highlight the limitations in terms of spatial resolution (the mean area of the HS is 0.3 ha) of the resulting maps which depends on the actual yield and size of harvest trailers. Yield per plant and yeast assimilable nitrogen maps have been used, in collaboration with the vineyard manager, to analyze and reconsider the fertilization process at the vineyard scale, showing the relevance of the information. [ABSTRACT FROM AUTHOR]

The Eurasian grapevine (Vitis vinifera L.) is one of the most extensively cultivated horticultural crop worldwide, with significant economic relevance, particularly in wine production. Accurate grapevine variety identification is essential for ensuring product authenticity, quality control, and regulatory compliance. Traditional identification methods have inherent limitations limitations; ampelography is subjective and dependent on skilled experts, while molecular analysis is costly and time-consuming. To address these challenges, recent research has focused on applying deep learning (DL) and machine learning (ML) techniques for grapevine variety identification. This study systematically analyses 37 recent studies that employed DL and ML models for this purpose. The objective is to provide a detailed analysis of classification pipelines, highlighting the strengths and limitations of each approach. Most studies use DL models trained on leaf images captured in controlled environments at distances of up to 1.2 m. However, these studies often fail to address practical challenges, such as the inclusion of a broader range of grapevine varieties, using data directly acquired in the vineyards, and the evaluation of models under adverse conditions. This review also suggests potential directions for advancing research in this field. [ABSTRACT FROM AUTHOR]

Biostimulants are organic agents employed for crop yield enhancement, quality improvement, and environmental stress mitigation, reducing, at the same time, reliance on inorganic inputs. With advancements in sustainable agriculture, data acquisition technologies have become crucial for monitoring the effects of such inputs. This study evaluates the impact of four increasing rates of Biopromoter biostimulant application on grapevines: 0, 100 g plant−1, 100 g plant−1 with additional foliar fertilizers, and 150 g plant−1 with additional foliar fertilizers. The biostimulant was applied via foliar or ground methods, and its effects were assessed using vegetation indices derived from unmanned aerial systems (UAS), as well as proximal and manual sensing tools, alongside qualitative and quantitative production metrics. The research was conducted over two seasons in a Malvasia Bianca vineyard in Sardinia, Italy. Results indicated that UAS-derived vegetation indices, consistent with traditional ground-based measurements, effectively monitored vegetative growth over time but revealed no significant differences between treatments, suggesting either an insufficient vegetative indices sensitivity or that the applied biostimulant rates were insufficient to elicit a measurable response in the cultivar. Among the tools employed, only the SPAD 502 m demonstrated the sensitivity required to detect treatment differences, primarily reflected in grape production outcomes, especially in the second year and in the two groups managed with the highest amounts of biostimulants distributed by foliar and soil application. The use of biostimulants promoted, although only in the second year, a greener canopy and higher productivity in treatments where it was delivered to the soil. Further agronomic experiments are required to improve knowledge about biostimulants' composition and mode of action, which are essential to increasing their effectiveness against specific abiotic stresses. Future research will focus on validating these technologies for precision viticulture, particularly concerning the long-term benefits. [ABSTRACT FROM AUTHOR]

Abstract Climate change pose significant challenges to modern agriculture management systems, threatening food production and security. Therefore, tackling its effects has never been so imperative to attain sustainable food access and nutrition worldwide. In the case of viticulture, besides jeopardizing grape production, climate change has severe impact in quality, which has becoming more challenging to manage, due to the increasingly frequent fungal contamination, with consequences for relevant quality parameters such as the aromatic profiles of grapes and wines and their phenolic compounds. This has been leading to a reconfiguration of the wine industry geostrategic landscape and economy dynamics, particularly in Southern Europe. To address these and other emerging challenges, in-field deployable proximity-based precision technologies have been enabling real-time monitoring of crops ecosystems, including climate, soil and plants, by performing relevant data gathering and storage, paving the way for advanced decision support under the Internet of Things (IoT) paradigm. This paper explores the integration of agronomic and technological knowledge, emphasizing the proper selection of IoT-capable sensors for viticulture, while considering more general ones from agriculture to fill gaps when specialized options are unavailable. Moreover, advisable practices for sensor installation are provided, according to respective types, data acquisition capabilities and applicability.

Climate change pose significant challenges to modern agriculture management systems, threatening food production and security. Therefore, tackling its effects has never been so imperative to attain sustainable food access and nutrition worldwide. In the case of viticulture, besides jeopardizing grape production, climate change has severe impact in quality, which has becoming more challenging to manage, due to the increasingly frequent fungal contamination, with consequences for relevant quality parameters such as the aromatic profiles of grapes and wines and their phenolic compounds. This has been leading to a reconfiguration of the wine industry geostrategic landscape and economy dynamics, particularly in Southern Europe. To address these and other emerging challenges, in-field deployable proximity-based precision technologies have been enabling real-time monitoring of crops ecosystems, including climate, soil and plants, by performing relevant data gathering and storage, paving the way for advanced decision support under the Internet of Things (IoT) paradigm. This paper explores the integration of agronomic and technological knowledge, emphasizing the proper selection of IoT-capable sensors for viticulture, while considering more general ones from agriculture to fill gaps when specialized options are unavailable. Moreover, advisable practices for sensor installation are provided, according to respective types, data acquisition capabilities and applicability. [ABSTRACT FROM AUTHOR]

Featured Application: This review elucidates the transformative impact of artificial intelligence (AI) on viticulture, showcasing its practical applications in disease prediction, pest management, automated grape harvesting, and the optimization of water and nutrient management. The implementation of AI-driven technologies enables vineyard managers to effectively mitigate challenges such as disease outbreaks and pest infestations, resulting in healthier vines and increased yields. Automated harvesting systems improve the efficiency and consistency of grape picking, which are essential for the production of high-quality wine. Furthermore, AI's data-centric approaches to resource management promote sustainable practices by optimizing water and nutrient use. These developments illustrate the potential of AI to revolutionize traditional viticultural practices, addressing the industry's increasing demands for quality and sustainability in winemaking. This review explores the transformative role of artificial intelligence (AI) in the entire winemaking process, from viticulture to bottling, with a particular focus on enhancing food safety and traceability. It discusses AI's applications in optimizing grape cultivation, fermentation, bottling, and quality control, while emphasizing its critical role in managing microbiological risks such as mycotoxins. The review aims to show how AI technologies not only refine operational efficiencies but also raise safety standards and ensure traceability from vineyard to consumer. Challenges in AI implementation and future directions for integrating more advanced AI solutions into the winemaking industry will also be discussed, providing a comprehensive overview of AI's potential to revolutionize traditional practices. [ABSTRACT FROM AUTHOR]

Using a novel joint inversion approach, this study tackles the challenge of accurately characterizing subsurface electrical resistivity in vineyards, a critical and strategic aspect of precision viticulture. For the first time, we integrate 3D Galvanic Contact Resistivity with multi-2D Capacitively Coupled Resistivity data. Conducted in a prestigious Sangiovese vineyard in Montalcino (Tuscany, Italy), the data are analyzed utilizing a single algorithm capable of inverting Capacitively Coupled Resistivity, Galvanic Contact Resistivity, and joint datasets. This approach combines data sensitive to different depths and spatial resolutions, resulting in a comprehensive analysis of soil resistivity variations and moisture distribution, thus providing a detailed and coherent subsurface model. The joint inversion produced a high spatial resolution 3D resistivity model with a density of 20.21 data/m3. This model significantly enhances subsurface characterization, delineating root systems and correlating water distribution with resistivity patterns, showing relative variations sometimes greater than 50%. This method reduced data misfit more effectively than individual inversions and identified a low-resistivity volume (<20 Ω·m), extending from northeast to south, indicating the presence of subsurface water. The systematic alternation of high and low resistivity across vineyard rows highlights the impact of soil management activities on resistivity and supports targeted interventions for vineyard health. [ABSTRACT FROM AUTHOR]

The use of passive optical remote sensing (RS) has a rich history in precision viticulture (PV), with the use of RS technologies being employed in a myriad of PV applications. The present work undertakes a scoping review to examine past and current trends in the use of RS in grapevine production. It aims to identify literature gaps and new research opportunities. The Scopus database facilitated the search for relevant articles published between 2014 and 2023 using a search string of keywords. A total of 640 articles were produced by the Scopus search. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting framework, the 640 articles were reviewed based on predefined inclusion and exclusion criteria, resulting in 388 articles being deemed eligible for further data extraction. Four research questions were defined to guide the data extraction process, and a coding scheme was implemented to address these questions. The scoping review found Italy and the United States to be leading contributors to the research field, with vineyard mapping, yield estimation, and grapevine water status being the most extensively studied RS–PV applications. However, the use of RS to map vineyard soil properties and grapevine cultivars remains underexplored, presenting promising avenues for future research. [ABSTRACT FROM AUTHOR]

Precision agriculture is evolving toward a contemporary approach that involves multiple sensing techniques to monitor and enhance crop quality while minimizing losses and waste of no longer considered inexhaustible resources, such as soil and water supplies. To understand crop status, it is necessary to integrate data from heterogeneous sensors and employ advanced sensing devices that can assess crop and water status. This study presents a smart monitoring approach in agriculture, involving sensors that can be both stationary (such as soil moisture sensors) and mobile (such as sensor-equipped unmanned aerial vehicles). These sensors collect information from visual maps of crop production and water conditions, to comprehensively understand the crop area and spot any potential vegetation problems. A modular fuzzy control scheme has been designed to interpret spectral indices and vegetative parameters and, by applying fuzzy rules, return status maps about vegetation status. The rules are applied incrementally per a hierarchical design to correlate lower-level data (e.g., temperature, vegetation indices) with higher-level data (e.g., vapor pressure deficit) to robustly determine the vegetation status and the main parameters that have led to it. A case study was conducted, involving the collection of satellite images from artichoke crops in Salerno, Italy, to demonstrate the potential of incremental design and information integration in crop health monitoring. Subsequently, tests were conducted on vineyard regions of interest in Teano, Italy, to assess the efficacy of the framework in the assessment of plant status and water stress. Indeed, comparing the outcomes of our maps with those of cutting-edge machine learning (ML) semantic segmentation has indeed revealed a promising level of accuracy. Specifically, classification performance was compared to the output of conventional ML methods, demonstrating that our approach is consistent and achieves an accuracy of over 90% throughout various seasons of the year. [ABSTRACT FROM AUTHOR]

Remote sensing is now a valued solution for more accurately budgeting water supply by identifying spectral and spatial information. A study was put in place in a Vitis vinifera L. cv. Cabernet-Sauvignon vineyard in the San Joaquin Valley, CA, USA, where a variable rate automated irrigation system was installed to irrigate vines with twelve different water regimes in four randomized replicates, totaling 48 experimental zones. The purpose of this experimental design was to create variability in grapevine water status, in order to produce a robust dataset for modeling purposes. Throughout the growing season, spectral data within these zones was gathered using a Near InfraRed (NIR) - Short Wavelength Infrared (SWIR) hyperspectral camera (900 to 1700 nm) mounted on an Unmanned Aircraft Vehicle (UAV). Given the high water-absorption in this spectral domain, this sensor was deployed to assess grapevine stem water potential, Ψstem, a standard reference for water status assessment in plants, from pure grapevine pixels in hyperspectral images. The Ψstem was acquired simultaneously in the field from bunch closure to harvest and modeled via machine-learning methods using the remotely sensed NIR-SWIR data as predictors in regression and classification modes (classes consisted of physiologically different water stress levels). Hyperspectral images were converted to bottom of atmosphere reflectance using standard panels on the ground and through the Quick Atmospheric Correction Method (QUAC) and the results were compared. The best models used data obtained with standard panels on the ground and allowed predicting Ψstem values with an R2 of 0.54 and an RMSE of 0.11 MPa as estimated in cross-validation, and the best classification reached an accuracy of 74%. This project aims to develop new methods for precisely monitoring and managing irrigation in vineyards while providing useful information about plant physiology response to deficit irrigation. [ABSTRACT FROM AUTHOR]

Yield forecasting is of immeasurable value in modern viticulture to optimize harvest scheduling and quality management. The number of inflorescences and flowers per vine is one of the main components and their assessment serves as an early predictor, which can explain up to 85-90% of yield variability. This study introduces a sophisticated framework that integrates the benchmark of different advanced deep learning and classic image processing to automate the segmentation of grapevine inflorescences and the detection of single flowers, to achieve precise, early, and non-invasive yield predictions in viticulture. The YOLOv8n model achieved superior performance in localizing inflorescences (F1-ScoreBox = 95.9%) and detecting individual flowers (F1-Score = 91.4%), while the YOLOv5n model excelled in the segmentation task (F1-ScoreMask = 98.6%). The models demonstrated a strong correlation (R2 > 90.0%) between detected and visible flowers in inflorescences. A statistical analysis confirmed the robustness of the framework, with the YOLOv8 model once again standing out, showing no significant differences in error rates across diverse grapevine morphologies and varieties, ensuring wide applicability. The results demonstrate that these models can significantly improve the accuracy of early yield predictions, offering a non-invasive, scalable solution for Precision Viticulture. The findings underscore the potential for Computer Vision technology to enhance vineyard management practices, leading to better resource allocation and improved crop quality.

Understanding geometric and biophysical characteristics is essential for determining grapevine vigor and improving input management and automation in viticulture. This study compares point cloud data obtained from a Terrestrial Laser Scanner (TLS) and various UAV sensors including multispectral, panchromatic, Thermal Infrared (TIR), RGB, and LiDAR data, to estimate geometric parameters of grapevines. Descriptive statistics, linear correlations, significance using the F-test of overall significance, and box plots were used for analysis. The results indicate that 3D point clouds from these sensors can accurately estimate maximum grapevine height, projected area, and volume, though with varying degrees of accuracy. The TLS data showed the highest correlation with grapevine height (r = 0.95, p < 0.001; R2 = 0.90; RMSE = 0.027 m), while point cloud data from panchromatic, RGB, and multispectral sensors also performed well, closely matching TLS and measured values (r > 0.83, p < 0.001; R2 > 0.70; RMSE < 0.084 m). In contrast, TIR point cloud data performed poorly in estimating grapevine height (r = 0.76, p < 0.001; R2 = 0.58; RMSE = 0.147 m) and projected area (r = 0.82, p < 0.001; R2 = 0.66; RMSE = 0.165 m). The greater variability observed in projected area and volume from UAV sensors is related to the low point density associated with spatial resolution. These findings are valuable for both researchers and winegrowers, as they support the optimization of TLS and UAV sensors for precision viticulture, providing a basis for further research and helping farmers select appropriate technologies for crop monitoring. [ABSTRACT FROM AUTHOR]

In the context of increasing global food demand and the urgent need for production processes optimization, plant protection products play a key role in safeguarding crops from insects, pests, and fungi, responsible of plant diseases proliferation and yield losses. Despite the inaccurate distribution of conventional aerial spraying performed by airplanes and helicopters, Unmanned Aerial Spraying Systems (UASSs) offer low health risks and operational cost solutions, preserving crops and soil from physical damage. This study explores the impact of UASS flight height (2 m and 2.5 m above ground level), speed (1 m s−1 and 1.5 m s−1), and position (over the canopy and the inter-row) on vineyard aerial spraying efficiency by analysing Water Sensitive Papers droplet coverage, density, and Number Median Diameter using a MATLAB script. Flight position factor, more than others, influenced the application results. The specific configuration of 2 m altitude, 1.5 m s−1 cruising speed, and inter-row positioning yielded the best results in terms of canopy coverage, minimizing off-target and ground dispersion, and represented the best setting to facilitate droplets penetration, reaching the lowest parts generally more affected from disease. Further research is needed to assess UASS aerial PPP distribution effectiveness and environmental impact in agriculture, crucial for technology implementation, especially in countries where aerial treatments are not yet permitted. [ABSTRACT FROM AUTHOR]

The priority problem in intensive viticulture is reducing pesticides, and fertilizers, and improving water-use efficiency. This is driven by global and EU regulatory efforts. This review, systematically examines 92 papers, focusing on progress in satellite solutions over time, and (pre)processing improvements of spatio-temporal and spectral resolution. The importance of the integration of satellites with ground truth data is highlighted. The results provide precise on-field adaptation strategies through the generation of prescription maps and variable rate application. This enhances sustainability and efficiency in vineyard management and reduces the environmental footprint of vineyard techniques. The effectiveness of different vegetation indices in capturing spatial and temporal variations in vine health, water content, chlorophyll levels, and overall vigor is discussed. The challenges in the use of satellite data in viticulture are addressed. Advanced satellite technologies provide detailed vineyard monitoring, offering insights into spatio-temporal variability, soil moisture, and vine health. These are crucial for optimizing water-use efficiency and targeted management practices. By integrating satellite data with ground-based measurements, viticulturists can enhance precision viticulture, reduce reliance on chemical interventions, and improve overall vineyard sustainability and productivity. [ABSTRACT FROM AUTHOR]

It is crucial for winegrowers to make informed decisions about the optimum time to harvest the grapes to ensure the production of premium wines. Global warming contributes to decreasing acidity and increasing sugar levels in grapes, resulting in bland wines with high contents of alcohol. Predicting quality in viticulture is thus pivotal. To assess the average ripeness, typically a sample of one hundred berries representative for the entire vineyard is collected. However, this process, along with the subsequent detailed must analysis, is time consuming and expensive. This study focusses on predicting essential quality parameters like sugar and acid content in Vitis vinifera (L.) varieties 'Chardonnay', 'Riesling', 'Dornfelder', and 'Pinot Noir'. A small near-infrared spectrometer was used measuring non-destructively in the wavelength range from 1 100 nm to 1 350 nm while the reference contents were measured using high-performance liquid chromatography. Chemometric models were developed employing partial least squares regression and using spectra of all four grapevine varieties, spectra gained from berries of the same colour, or from the individual varieties. The models exhibited high accuracy in predicting main quality-determining parameters in independent test sets. On average, the model regression coefficients exceeded 93% for the sugars fructose and glucose, 86% for malic acid, and 73% for tartaric acid. Using these models, prediction accuracies revealed the ability to forecast individual sugar contents within an range of ± 6.97 g/L to ± 10.08 g/L, and malic acid within ± 2.01 g/L to ± 3.69 g/L. This approach indicates the potential to develop robust models by incorporating spectra from diverse grape varieties and berries of different colours. Such insight is crucial for the potential widespread adoption of a handheld near-infrared sensor, possibly integrated into devices used in everyday life, like smartphones. A server-side and cloud-based solution for pre-processing and modelling could thus avoid pitfalls of using near-infrared sensors on unknown varieties and in diverse wine-producing regions. [ABSTRACT FROM AUTHOR]

Climate change is currently the greatest threat to the environment as we know it today. The present study aimed to highlight the changes in the main climatic elements during the last five decades (1971–2020) in northeastern Romania (Copou-Iaşi wine-growing center) and their impact on grape quality, as part of precision viticulture strategies and efficient management of grapevine plantations. Data analysis revealed a constant and significant increase in the average air temperature in the last 50 years (+1.70 °C), more pronounced in the last 10 years (+0.61 °C), with a number of days with extreme temperatures (>30 °C) of over 3.5-fold higher, in parallel with a fluctuating precipitation regime. The increase in average temperatures in the last 40 years was highly correlated with the advancement of the grape harvest date (up to 12 days), a significant increase in Vitis vinifera L. white grape sugar concentration (+15–25 g/L), and a drastic decrease in total acidity (−2.0–3.5 g/L tartaric acid). The significant increase in the values of the bioclimatic indices require the reclassification of the wine-growing area in higher classes of favorability, raising the opportunity to grow cultivars that are more suited to warmer climates, ensuring the efficiency of the plantation, and meeting current consumer expectations. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) technology has emerged as a pivotal tool in viticulture, offering unique capabilities for various applications. This study provides a comprehensive overview of the current state-of-the-art applications of SAR in viticulture, highlighting its significance in addressing key challenges and enhancing viticultural practices. The historical evolution and motivations behind SAR technology are also provided, along with a demonstration of its applications within viticulture, showcasing its effectiveness in various aspects of vineyard management, including delineating vineyard boundaries, assessing grapevine health, and optimizing irrigation strategies. Furthermore, future perspectives and trends in SAR applications in viticulture are discussed, including advancements in SAR technology, integration with other remote sensing techniques, and the potential for enhanced data analytics and decision support systems. Through this article, a comprehensive understanding of the role of SAR in viticulture is provided, along with inspiration for future research endeavors in this rapidly evolving field, contributing to the sustainable development and optimization of vineyard management practices. [ABSTRACT FROM AUTHOR]

The crop water stress index (CWSI) is widely used for assessing water status in vineyards, but its accuracy can be compromised by various factors. Despite its known limitations, the question remains whether it is inferior to the current practice of direct measurements of Ψstem of a few representative vines. This study aimed to address three key knowledge gaps: (1) determining whether Ψstem (measured in few vines) or CWSI (providing greater spatial representation) better represents vineyard water status; (2) identifying the optimal scale for using CWSI for precision irrigation; and (3) understanding the seasonal impact on the CWSI-Ψstem relationship and establishing a reliable Ψstem prediction model based on CWSI and meteorological parameters. The analysis, conducted at five spatial scales in a single vineyard from 2017 to 2020, demonstrated that the performance of the CWSI- Ψstem model improved with increasing scale and when meteorological variables were integrated. This integration helped mitigate apparent seasonal effects on the CWSI-Ψstem relationship. R2 were 0.36 and 0.57 at the vine and the vineyard scales, respectively. These values rose to 0.51 and 0.85, respectively, with the incorporation of meteorological variables. Additionally, a CWSI-based model, enhanced by meteorological variables, outperformed current water status monitoring at both vineyard (2.5 ha) and management cell (MC) scales (0.09 ha). Despite reduced accuracy at smaller scales, water status evaluation at the management cell scale produced significantly lower Ψstem errors compared to whole vineyard evaluation. This is anticipated to enable more effective irrigation decision-making for small-scale management zones in vineyards implementing precision irrigation. [ABSTRACT FROM AUTHOR]

Grapevines are subjected to many physiological and environmental stresses that influence their vegetative and reproductive growth. Water stress, cold damage, and pathogen attacks are highly relevant stresses in many grape-growing regions. Precision viticulture can be used to determine and manage the spatial variation in grapevine health within a single vineyard block. Newer technologies such as remotely piloted aircraft systems (RPASs) with remote sensing capabilities can enhance the application of precision viticulture. The use of remote sensing for vineyard variation detection has been extensively investigated; however, there is still a dearth of literature regarding its potential for detecting key stresses such as winter hardiness, water status, and virus infection. The main objective of this research is to examine the performance of modern remote sensing technologies to determine if their application can enhance vineyard management by providing evidence-based stress detection. To accomplish the objective, remotely sensed data such as the normalized difference vegetation index (NDVI) and thermal imaging from RPAS flights were measured from six commercial vineyards in Niagara, ON, along with the manual measurement of key viticultural data including vine water stress, cold stress, vine size, and virus titre. This study verified that the NDVI could be a useful metric to detect variation across vineyards for agriculturally important variables including vine size and soil moisture. The red-edge and near-infrared regions of the electromagnetic reflectance spectra could also have a potential application in detecting virus infection in vineyards.

Biostimulants are organic agents employed for crop yield enhancement, quality improvement, and environmental stress mitigation, reducing, at the same time, reliance on inorganic inputs. With advancements in sustainable agriculture, data acquisition technologies have become crucial for monitoring the effects of such inputs. This study evaluates the impact of four increasing rates of Biopromoter biostimulant application on grapevines: 0, 100 g plant−1, 100 g plant−1 with additional foliar fertilizers, and 150 g plant−1 with additional foliar fertilizers. The biostimulant was applied via foliar or ground methods, and its effects were assessed using vegetation indices derived from unmanned aerial systems (UAS), as well as proximal and manual sensing tools, alongside qualitative and quantitative production metrics. The research was conducted over two seasons in a Malvasia Bianca vineyard in Sardinia, Italy. Results indicated that UAS-derived vegetation indices, consistent with traditional ground-based measurements, effectively monitored vegetative growth over time but revealed no significant differences between treatments, suggesting either an insufficient vegetative indices sensitivity or that the applied biostimulant rates were insufficient to elicit a measurable response in the cultivar. Among the tools employed, only the SPAD 502 m demonstrated the sensitivity required to detect treatment differences, primarily reflected in grape production outcomes, especially in the second year and in the two groups managed with the highest amounts of biostimulants distributed by foliar and soil application. The use of biostimulants promoted, although only in the second year, a greener canopy and higher productivity in treatments where it was delivered to the soil. Further agronomic experiments are required to improve knowledge about biostimulants’ composition and mode of action, which are essential to increasing their effectiveness against specific abiotic stresses. Future research will focus on validating these technologies for precision viticulture, particularly concerning the long-term benefits.

The primary purpose of this study was to improve our understanding of remote sensing technologies and their potential application in vineyards to monitor yields and fruit composition, which could then be used for selective harvesting and winemaking. For yield and berry composition data collection, representative vines from the vineyard block were selected and geolocated, and the same vines were surveyed for remote sensing data collection by the multispectral and thermal sensors in the RPAS in 2015 and 2016. The spectral reflectance data were further analyzed for vegetation indices to evaluate the correlation between the variables. Moran’s global index and map analysis were used to determine spatial clustering patterns and correlations between variables. The results of this study indicated that remote sensing data in the form of vegetation indices from the RPAS were positively correlated with yield and berry weight across sites and years. There was a positive correlation between the thermal emission and berry pH, berry phenols, and anthocyanins in certain sites and years. Overall, remote sensing technology has the potential to monitor and predict grape quality and yield, but further research on the efficacy of this data is needed for selective harvesting and winemaking.

In the framework of precision viticulture, satellite data have been demonstrated to significantly support many tasks. Specifically, they enable the rapid, large-scale estimation of some viticultural parameters like vine stem water potential (Ψstem) and intercepted solar radiation (ISR) that traditionally require time-consuming ground surveys. The practice of covering table grape vineyards with plastic films introduces an additional challenge for estimation, potentially affecting vine spectral responses and, consequently, the accuracy of estimations from satellites. This study aimed to address these challenges with a special focus on the exploitation of Sentinel-2 Level 2A and meteorological data to monitor a plastic-covered vineyard in Southern Italy. Estimates of Ψstem and ISR were obtained using different algorithms, namely, Ordinary Least Square (OLS), Multivariate Linear Regression (MLR), and machine learning (ML) techniques, which rely on Random Forest Regression, Support Vector Regression, and Partial Least Squares. The results proved that, despite the potential spectral interference from the plastic coverings, ISR and Ψstem can be locally estimated with a satisfying accuracy. In particular, (i) the OLS regression-based approach showed a good performance in providing accurate ISR estimates using the near-infrared spectral bands (RMSE < 8%), and (ii) the MLR and ML algorithms could estimate both the ISR and vine water status with a higher accuracy (RMSE < 7 for ISR and RMSE < 0.14 MPa for Ψstem). These results encourage the adoption of medium–high resolution multispectral satellite imagery for deriving satisfying estimates of key crop parameters even in anomalous situations like the ones where plastic films cover the monitored vineyard, thus marking a significant advancement in precision viticulture. [ABSTRACT FROM AUTHOR]

Data acquisition through unmanned aerial vehicles (UAVs) has become integral to the study of agricultural crops, especially for multitemporal analyses spanning the entire growing season. Ensuring accurate data alignment is essential not only to maintain data quality but also to leverage the continuous monitoring of the same area over time. Ground control points (GCPs) play a critical role in geolocating UAV data. Their absence can lead to planimetric and altimetric discrepancies, which are particularly impactful in 3D plant-level studies. This study is centered on the examination of misalignment effects in a challenging steep slope vineyard environment and their impacts on 3D alignment accuracy. For this purpose, a UAV equipped with an RGB camera to capture imagery at two distinct flight heights. Various scenarios, each involving a different number of GCPs, were assessed to evaluate their impact on alignment precision. The methodology employed holds potential for assessing geolocation accuracy in complex 3D environments, providing value insights for vineyard monitoring. [ABSTRACT FROM AUTHOR]

This study delves into the analysis of a vineyard in Carinthia, Austria, focusing on the automated derivation of ecosystem structures of individual vine parameters, including vine heights, leaf area index (LAI), leaf surface area (LSA), and the geographic positioning of single plants. For the derivation of these parameters, intricate segmentation processes and nuanced UAS-based data acquisition techniques are necessary. The detection of single vines was based on 3D point cloud data, generated at a phenological stage in which the plants were in the absence of foliage. The mean distance from derived vine locations to reference measurements taken with a GNSS device was 10.7 cm, with a root mean square error (RMSE) of 1.07. Vine height derivation from a normalized digital surface model (nDSM) using photogrammetric data showcased a strong correlation (R2 = 0.83) with real-world measurements. Vines underwent automated classification through an object-based image analysis (OBIA) framework. This process enabled the computation of ecosystem structures at the individual plant level post-segmentation. Consequently, it delivered comprehensive canopy characteristics rapidly, surpassing the speed of manual measurements. With the use of uncrewed aerial systems (UAS) equipped with optical sensors, dense 3D point clouds were computed for the derivation of canopy-related ecosystem structures of vines. While LAI and LSA computations await validation, they underscore the technical feasibility of obtaining precise geometric and morphological datasets from UAS-collected data paired with 3D point cloud analysis and object-based image analysis. [ABSTRACT FROM AUTHOR]

Addressing the urgent sustainability challenges in the wine industry, this study explores the efficacy of sustainability-oriented innovations (SOIs) and smart farming technologies (SFTs) across wine value chains in Cyprus and Italy. Utilising a mixed-methods approach that includes quantitative analysis through Key Performance Indicators (KPIs) and qualitative assessments to understand stakeholders' perspectives, this research delves into the environmental, economic, and social impacts of these technologies. In Cyprus, the integration of digital labelling and smart farming solutions led to a substantial reduction in pesticide usage by up to 75% and enhanced the perceived quality of wine by an average of 8%. A pilot study in Italy witnessed a 33.4% decrease in greenhouse gas emissions, with the additional benefit of a 5.3% improvement in intrinsic product quality. The pilot introduced a carbon credit system, potentially generating an average annual revenue of EUR 4140 per farm. These findings highlight the transformative potential of SOIs and SFTs in promoting sustainable practices within the wine industry, demonstrating significant advancements in reducing environmental impact, improving product quality, and enhancing economic viability. This study underscores the critical role of innovative technologies in achieving sustainability goals and provides a compelling case for their wider adoption within the agricultural sector. [ABSTRACT FROM AUTHOR]

In viticulture, downy mildew is one of the most common diseases that, if not adequately treated, can diminish production yield. However, the uncontrolled use of pesticides to alleviate its occurrence can pose significant risks for farmers, consumers, and the environment. This paper presents a new framework for the early detection and estimation of the mildew's appearance in viticulture fields. The framework utilizes a protocol for the real-time acquisition of drones' high-resolution RGB images and a cloud-docker-based video or image inference process using object detection CNN models. The authors implemented their framework proposition using open-source tools and experimented with their proposed implementation on the debina grape variety in Zitsa, Greece, during downy mildew outbursts. The authors present evaluation results of deep learning Faster R-CNN object detection models trained on their downy mildew annotated dataset, using the different object classifiers of VGG16, ViTDet, MobileNetV3, EfficientNet, SqueezeNet, and ResNet. The authors compare Faster R-CNN and YOLO object detectors in terms of accuracy and speed. From their experimentation, the embedded device model ViTDet showed the worst accuracy results compared to the fast inferences of YOLOv8, while MobileNetV3 significantly outperformed YOLOv8 in terms of both accuracy and speed. Regarding cloud inferences, large ResNet models performed well in terms of accuracy, while YOLOv5 faster inferences presented significant object classification losses. [ABSTRACT FROM AUTHOR]

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The application of sensors in viticulture is a fast and efficient method to monitor grapevine vegetative, yield, and quality parameters and determine spatial intra-vineyard variability. Molecular analysis at the gene expression level can further contribute to the understanding of the observed variability by elucidating how pathways contributing to different grape quality traits behave in zones diverging on any of these parameters. The intra-vineyard variability of a Cabernet Sauvignon vineyard was evaluated through a Normalized Difference Vegetation Index (NDVI) map calculated from a multispectral image and detailed ground-truthing (e.g., vegetative, yield, and berry ripening compositional parameters). The relationships between NDVI and ground measurements were explored by correlation analysis. Moreover, berries were investigated by microarray gene expression analysis performed at five time points from fruit set to full ripening. Comparison between the transcriptomes of samples taken from locations with the highest and lowest NDVI values identified 968 differentially expressed genes. Spatial variability maps of the expression level of key berry ripening genes showed consistent patterns aligned with the vineyard vigor map. These insights indicate that berries from different vigor zones present distinct molecular maturation programs and suggest that transcriptome analysis may be a valuable tool for the management of vineyard variability. [ABSTRACT FROM AUTHOR]

Hyperspectral images provide rich spectral/spatial data and have shown remarkable performance in precision viticulture. Standardized data-processing methods are necessary to reduce the dimensionality and to identify powerful wavelengths. Toward this goal, we evaluated and compared the performance and novel-wavelength-identification ability of five renowned machine learning models: the linear models Ridge and LASSO, a 1D + 2D convolutional neural network (1D +2D CNN) non-linear model, a gradient boosting decision tree (GBDT) using XGBoost as an ensemble model, and an explainable boosting machine (EBM) followed by support vector regression (SVR) as a hybrid model. The model evaluations were conducted using leave-one-out cross-validation (LOOCV) as we sought to clarify the best-fitted machine learning model. Our results demonstrated that Ridge, LASSO, showed better performance with relatively high accuracy but were weak as a wavelength identifier. GDBT-XGBoost showed considerable prediction power and wavelength identification. EBM-SVM emerged as the most powerful model, demonstrating exceptional stability and clear wavelength classification even for destructive measurements under varying environmental stresses across Rondo's growth stages. The combined approach of 1D + 2D CNN algorithms was advantageous to handle the dynamic shapes of spectral curve and horizontal shift of the wavelengths obtained from outdoor data acquisition, and notably, it showed the highest accuracy to predict the brix and pH of wine grapes for both indoor and outdoor sensings. But the combined effects of 1D and 2D CNN algorithms were difficult to clarify the importance of spectral features for the brix and pH prediction. The integrated machine learning models with dimensionality reduction, and proper image acquisition can increase the model's accuracy. The common absorption peaks were observed in the near-infrared region of 700 nm and 900 nm. Those wavelengths should be considered for the development of low-cost sensing platforms with fewer bands. Wavelengths over 900 nm are also important to develop outdoor sensing platforms.

It is crucial for winegrowers to make informed decisions about the optimum time to harvest the grapes to ensure the production of premium wines. Global warming contributes to decreasing acidity and increasing sugar levels in grapes, resulting in bland wines with high contents of alcohol. Predicting quality in viticulture is thus pivotal. To assess the average ripeness, typically a sample of one hundred berries representative for the entire vineyard is collected. However, this process, along with the subsequent detailed must analysis, is time consuming and expensive. This study focusses on predicting essential quality parameters like sugar and acid content in Vitis vinifera (L.) varieties ‘Chardonnay’, ‘Riesling’, ‘Dornfelder’, and ‘Pinot Noir’. A small near-infrared spectrometer was used measuring non-destructively in the wavelength range from 1 100 nm to 1 350 nm while the reference contents were measured using high-performance liquid chromatography. Chemometric models were developed employing partial least squares regression and using spectra of all four grapevine varieties, spectra gained from berries of the same colour, or from the individual varieties. The models exhibited high accuracy in predicting main quality-determining parameters in independent test sets. On average, the model regression coefficients exceeded 93% for the sugars fructose and glucose, 86% for malic acid, and 73% for tartaric acid. Using these models, prediction accuracies revealed the ability to forecast individual sugar contents within an range of ± 6.97 g/L to ± 10.08 g/L, and malic acid within ± 2.01 g/L to ± 3.69 g/L. This approach indicates the potential to develop robust models by incorporating spectra from diverse grape varieties and berries of different colours. Such insight is crucial for the potential widespread adoption of a handheld near-infrared sensor, possibly integrated into devices used in everyday life, like smartphones. A server-side and cloud-based solution for pre-processing and modelling could thus avoid pitfalls of using near-infrared sensors on unknown varieties and in diverse wine-producing regions.

Object Detection and Tracking have provided a valuable tool for many tasks, mostly time-consuming and prone-to-error jobs, including fruit counting while in the field, among others. Fruit counting can be a challenging assignment for humans due to the large quantity of fruit available, which turns it into a mentally-taxing operation. Hence, it is relevant to use technology to ease the task of farmers by implementing Object Detection and Tracking algorithms to facilitate fruit counting. However, those algorithms suffer undercounting due to occlusion, which means that the fruit is hidden behind a leaf or a branch, complicating the detection task. Consequently, gathering the datasets from multiple viewing angles is essential to boost the likelihood of recording the images and videos from the most visible point of view. Furthermore, the most critical open-source datasets do not include labels for certain fruits, such as grape bunches. This study aims to unravel the scarcity of public datasets, including labels, to train algorithms for grape bunch Detection and Tracking by considering multiple angles acquired with a UAV to overcome fruit occlusion challenges.

The ability to detect and respond to vineyard spatial variation can lead to improved management—a practice known as precision viticulture. The goal of this study was to determine if remote sensors can enhance precision viticulture applications by detecting vineyard spatial variation. The hypothesis was that differences in vine spectral reflectance, as detected by remote sensors, would be associated with variations in viticultural variables due to known relationships with vine size, structure, and pigmentation. Riesling grapevines were geolocated within six commercial vineyards across Niagara, Ontario. Water status, vine size, winter hardiness, virus titer, yield components, and berry composition were measured on these vines. Remote sensing technologies subsequently collected multispectral data by unmanned aerial vehicles and by proximal sensing technology (GreenSeeker™), which were transformed into the Normalized Difference Vegetation Index (NDVI). Direct relationships between NDVI and vine size, water status, yield, berry weight, and titratable acidity were observed, as well as inverse relationships between NDVI and Brix and potentially volatile terpenes. Remote sensing demonstrated the ability to detect vineyard areas differing in measures of vine health, yield, and berry composition in certain sites and years; however, more research is needed to determine when these technologies should be used for precision viticulture applications.

Accurately predicting grape yield in vineyards is essential for strategic decision-making in the wine industry. Current methods are labour-intensive, costly, and lack spatial coverage, reducing accuracy and cost-efficiency. Efforts to automate and enhance yield estimation focus on scaling fruit weight assessments. Machine learning, particularly deep learning, shows promise in improving accuracy through automatic feature extraction and hierarchical representation. However, most of these methods have been developed for analyses at a particular time point and solutions able to consider temporal information captured across sequential frames are currently poorly developed. This paper addresses this gap by introducing a system for yield estimation, utilising publicly available data repositories, such as Embrapa WGISD, alongside an in-house dataset collected by a Blackmagic camera at the pre-harvest stage. We introduce a system that utilises bunch weight regression to estimate grape yield. Bunch weight estimates are obtained by summing samples randomly drawn from the grape bunch weight distribution through empirical calibration. Grapevine bunches are identified and segmented using Mask R-CNN with Swin Transformer, and a SiamFC-based tracking mechanism is employed to estimate the number of unique bunches per panel or row. The number of berries for each tracked bunch is determined using a density approach known as multitask point supervision. In our experiments, we demonstrate the effectiveness of the proposed system for yield estimation, achieving harvested weight errors of less than 5% in two of the three vineyard panels. Larger harvest weight errors (around 15%) were observed due to inaccuracies in tracking certain bunches caused by dense concentration of bunches in one panel. However, these errors should be contrasted with the current practice error of up to 30%, highlighting the potential of machine vision for hands-off yield estimation at scale.

This review explores the transformative role of artificial intelligence (AI) in the entire winemaking process, from viticulture to bottling, with a particular focus on enhancing food safety and traceability. It discusses AI’s applications in optimizing grape cultivation, fermentation, bottling, and quality control, while emphasizing its critical role in managing microbiological risks such as mycotoxins. The review aims to show how AI technologies not only refine operational efficiencies but also raise safety standards and ensure traceability from vineyard to consumer. Challenges in AI implementation and future directions for integrating more advanced AI solutions into the winemaking industry will also be discussed, providing a comprehensive overview of AI’s potential to revolutionize traditional practices.

How accurately do Sentinel-2 (S2) images describe vine row spatial variability? Can they produce effective management zones (MZs) for precision viticulture? S2 and UAV datasets acquired over two years for different drip-irrigated vineyards in the Colli Morenici region (northern Italy) were used to assess the actual need to use UAV-NDVI maps instead of S2 images to obtain effective MZ maps. First, the correlation between S2 and UAV-NDVI values was investigated. Secondly, contingency matrices and dichotomous tables (considering UAV-MZ maps as a reference) were developed to compare MZ maps produced using S2 and UAV imagery. Moreover, data on grape production and quality were analyzed through linear discrimination analyses (LDA) to evaluate the effectiveness of S2-MZs and UAV-MZs to explain spatial variability in yield and quality data. The outcomes highlight that S2 images can be quite good tools to manage fertilization based on the within-field vigor variability, of which they capture the main features. Nevertheless, as S2-MZs with low and high vigor were over-estimated, S2-MZ maps cannot be used for high-accuracy input management. From the LDA results, the UAV-MZs appeared slightly more performant than the S2-MZs in explaining the variability in grape quality and yield, especially in the case of low-vigor MZs. [ABSTRACT FROM AUTHOR]

Precision viticulture systems are essential for enhancing traditional intensive viticulture, achieving high-quality results, and minimizing costs. This study explores the integration of Unmanned Aerial Vehicles (UAVs) and artificial intelligence in precision viticulture, focusing on vine detection and vineyard zoning. Vine detection employs the YOLO (You Only Look Once) deep learning algorithm, achieving a remarkable 90% accuracy by analysing UAV imagery with various spectral ranges from various phenological stages. Vineyard zoning, achieved through the application of the K-means algorithm, incorporates geospatial data such as the Normalized Difference Vegetation Index (NDVI) and the assessment of nitrogen, phosphorus, and potassium content in leaf blades and petioles. This approach enables efficient resource management tailored to each zone's specific needs. The research aims to develop a decision-support model for precision viticulture. The proposed model demonstrates a high vine detection accuracy and defines management zones with variable weighting factors assigned to each variable while preserving location information, revealing significant differences in variables. The model's advantages lie in its rapid results and minimal data requirements, offering profound insights into the benefits of UAV application for precise vineyard management. This approach has the potential to expedite decision making, allowing for adaptive strategies based on the unique conditions of each zone. [ABSTRACT FROM AUTHOR]

The spatial variability in vineyard soil might negatively affect wine composition, leading to inhomogeneous flavonoid composition and aromatic profiles. In this study, we investigated the spatial variability in wine chemical composition in a Cabernet Sauvignon (Vitis vinifera L.) vineyard in 2019 and 2020. Because of the tight relationships with soil profiles, mid-day stem water potential integrals (Ψstem Int) were used to delineate the vineyard into two zones, including Zone 1 with relatively higher water stress and Zone 2 with relatively lower water stress. Wine from Zone 2 generally had more anthocyanins in 2019. In 2020, Zone 1 had more anthocyanins and flavonols. Zone 2 had more proanthocyanidin extension and terminal subunits as well as total proanthocyanidins in 2020. According to the Principal Component Analyses (PCA) for berry and wine chemical composition, the two zones were significantly different in the studied wine aromatic compounds. In conclusion, this study provides evidence of the possibility of managing the spatial variability of both wine flavonoid composition and aromatic profiles through connecting vineyard soil variability to grapevine season-long water status. [ABSTRACT FROM AUTHOR]