Search Results (228)

Smart technologies and artificial intelligence (AI) are increasingly reshaping viticulture by improving vineyard monitoring, supporting data interpretation, and enabling more targeted management decisions. This review examines how sensor networks, remote sensing, machine learning, deep learning, and decision-support systems contribute to more sustainable vineyard management and the production of high-quality grapes. Particular attention is paid to applications in grapevine stress monitoring, disease and pest detection, irrigation and nutrient management, yield estimation, grape quality prediction, and emerging automation. The review also highlights the main barriers that still limit broader adoption in commercial vineyards, including data quality issues, limited transferability across sites and seasons, interoperability gaps, vendor lock-in, and concerns related to governance, privacy, and cybersecurity. Although these constraints remain significant, the available evidence shows that smart viticulture can improve resource-use efficiency, support more precise interventions, and help growers respond more effectively to environmental variability. Future progress will depend on stronger validation under field conditions, better integration into practical vineyard workflows, interoperable digital infrastructures, and decision-support tools that are transparent, reliable, and useful for end users. Full article

Accurate vineyard yield estimation is essential for harvest planning, resource allocation, and economic decision-making, particularly under conditions of high spatial variability. Traditional sampling-based methods are labor-intensive, destructive, and prone to error, especially in high-productivity free-canopy systems. This study developed and evaluated predictive models for commercial irrigated vineyards of Carménère and Chardonnay in Chile’s Maule Region across two growing seasons (2023–2025). Structural yield components, physiological measurements, and UAV-derived multispectral indices (NDVI, GNDVI, NDRE) were collected from georeferenced sampling grids. Modeling approaches included linear regression, stepwise selection, and machine learning algorithms (Random Forest, Multilayer Perceptron). Validation results showed that cluster number was the primary driver of yield variability, explaining up to 40% of variation. Incorporating physiological and spectral variables improved accuracy, with the best models (least squares and MLP) achieving R² values up to 0.66 and reducing errors to 12–15%. Spatial yield maps reproduced intra-vineyard variability patterns, demonstrating that integrating plant-level and canopy-level data substantially enhances yield prediction. These findings provide a robust framework for precision viticulture applications. Full article

Efficient nutrient management through fertigation is essential for sustaining table grape production under water-limited Mediterranean environments. This study evaluated the effects of graded potassium (K) fertigation rates on yield and berry quality of grapevines under semi-arid conditions in northern Jordan. Field experiments were conducted over three consecutive seasons at three locations using four potassium application rates (0, 100, 200, and 300 kg K₂O ha⁻¹) applied through drip fertigation and synchronized with key vine phenological stages. Yield and fruit-quality parameters were analyzed using linear mixed-effects models accounting for treatment, year, location, and their interactions. Potassium fertigation significantly increased total yield, cluster weight, and berry physical attributes, including firmness, volume, weight, and diameter, whereas total soluble solids (TSS) and juice pH were largely unaffected. Relative to the control, potassium fertigation progressively increased total yield per vine by approximately 21%, 47%, and 72% under the 100, 200, and 300 kg K₂O ha⁻¹ treatments, respectively, although the magnitude of response differed among locations and growing seasons. Significant treatment × location interactions indicated that site-specific soil conditions influenced potassium response. These results demonstrate that synchronizing potassium supply with vine phenological demand through fertigation enhances productivity and berry physical quality without compromising fruit chemical composition. The observed improvements are consistent with the established physiological roles of potassium in osmotic regulation, assimilate transport, and berry development, supporting optimized potassium fertigation as a key component of precision nutrient management for sustainable viticulture in semi-arid Mediterranean regions. Full article

Viticulture is a globally significant economic activity; however, the scientific literature lacks in-depth, long-term studies integrating historical trends with future market concentration projections. This study fills this gap by analyzing global grape production dynamics and market structure over a 63-year period (1961–2023). The detection of structural breaks and the forecasting of yield trajectories using AutoRegressive Integrated Moving Average with Exogenous Variables (ARIMAX) models are crucial for the strategic planning of agricultural resources and enhancing viticultural resilience. Results indicate that while the global population increased 2.58-fold (1961–2023), grape production rose only 1.69-fold, leading to a decline in per capita availability. Although traditional leaders remain dominant, the combined share of the top five producers fell from 60% to 51.8%. The market concentration analysis Herfindahl-Hirschman Index (HHI) = 0.092; the Concentration Ratio (CR₅) = 53.65%) for 2024–2030 suggests a monopolistic competition structure. The arithmetic mean of annual global production for the 2024–2030 period is projected to reach 79.42 million tons. China is expected to lead (23.11%), followed by Italy, the United States, France, and Spain. These findings highlight the necessity of precision viticulture and modern technology to stabilize yields and enhance competitiveness in high-value horticultural markets. Full article

Precision nano-fertilization offers transformative potential for sustainable improvement of grape quality, yet the underlying molecular mechanisms remain poorly understood. Here, we investigated the effects of foliar-applied nano zero-valent iron (nZVI) and potassium dihydrogen phosphate (KH₂PO₄), in combination, on berry quality and secondary metabolic reprogramming in Vitis vinifera cv. Marselan. The combined nZVI/KH₂PO₄ treatment improved photosynthetic capacity, Fe/P co-accumulation, and berry quality traits including soluble solid content, sugar–acid ratio, and phenolic and aroma metabolite profiles. Crucially, integrated transcriptomic and metabolomic profiling identified 631 differentially expressed genes and 838 differentially accumulated metabolites, converging on flavonoid biosynthesis and glutathione metabolism as the dominant regulatory axes. Correlation network analysis pinpointed five hub regulatory genes—VvHCT, VvFLS1, VvLAR1/2, VvUGT88F5, and VvODC—as central orchestrators of nanomaterial-driven metabolic reprogramming: VvHCT and VvFLS1 coordinately redirected carbon flux toward hydroxycinnamic acid conjugates and flavonol accumulation, while VvLAR1/2 governed proanthocyanidin polymerization, and VvUGT88F5 modulated glycosylation-dependent metabolite stabilization. Notably, VvODC linked polyamine metabolism to glutathione-mediated stress buffering, revealing a previously uncharacterized crosstalk between nano-iron signaling and antioxidant reprogramming. These findings establish a mechanistic framework in which nZVI and KH₂PO₄ synergistically remodel the secondary metabolome through discrete yet interconnected transcriptional nodes, providing molecular targets for nano-enabled precision viticulture and broader applications of engineered nanomaterials in high-value crop improvement. Full article

The precise and timely diagnosis of grapevine diseases is paramount for ensuring food security and mitigating economic losses within the viticulture sector. While existing deep learning models offer high accuracy, their computational intensity and hardware requirements often hinder their use in portable or low-power field systems. This study addresses this gap by proposing GrapeLeafNet, a lightweight convolutional neural network optimized for efficient feature extraction. GrapeLeafNet introduces a strategic hybrid approach that combines the low parameter efficiency of models like SqueezeNet with the rapid feature propagation advantages offered by shallow architectures such as AlexNet. By eliminating the sequential processing latency caused by SqueezeNet’s 18-layer deep structure and the excessive 61-million-parameter memory burden of AlexNet, this model establishes a critical balance between low latency and high accuracy through its optimized 7-layer architecture. Characterized by an original integration of standard convolutional layers, batch normalization, and max pooling, GrapeLeafNet achieves high computational efficiency with only 1.6 million parameters and a 6.26 MB memory footprint. This structural optimization enhances deep feature hierarchies, enabling the model to focus on distinctive pathological signs within complex leaf patterns and maximize classification sensitivity by filtering out irrelevant features. The evaluation was conducted using the Niphad Grape Leaf Disease (NGLD) dataset, incorporating data augmentation to mitigate inherent class imbalances. Additionally, data augmentation techniques were employed to mitigate inherent class imbalances within the dataset. Experimental results demonstrate that GrapeLeafNet achieved 97.06% accuracy and a 94.77% F1-score on the original dataset, outperforming recent benchmarks by 2.46%. Following augmentation, performance reached 98.29% accuracy and a 98.16% F1-score, representing a 6.16% higher F1-score than contemporary models. GrapeLeafNet exhibits high robustness against asymmetric class distributions and establishes a significant performance margin over existing architectures. Its lightweight nature, combined with superior accuracy and F1-score metrics, makes it an ideal candidate for integration into mobile devices and real-time agricultural monitoring systems. Full article

This study addresses the challenge of detecting white grape bunches (Vitis vinifera L.) in high-density vineyard canopies, a critical task for precision viticulture and yield estimation. Traditional statistical and image-processing methods have struggled to cope with occlusion issues. In this work, more than 200 field RGB images were collected at La Bergonza (Toledo, Spain) and expanded through data augmentation. Several preprocessing strategies were evaluated to enhance bunch visibility. Different convolutional neural network (CNN) architectures were compared, with YOLOv8 outperforming Mask R-CNN in terms of both accuracy and efficiency. YOLOv8, trained for up to 100 epochs on equalized and augmented datasets, achieved outstanding performance, with 84.9% precision, 72.6% recall, and an mAP@0.5 of 83%, far surpassing Mask R-CNN (17% precision and 26% recall). The model successfully detected partially occluded grape bunches, including some that were not visible to human experts, and outperformed previous studies that relied on controlled backgrounds or artificial lighting. The results demonstrate that combining RGB equalization with data augmentation significantly improves detection performance. These findings highlight the potential of deep learning and low-cost RGB imaging systems to enable automated and scalable solutions for yield estimation and canopy analysis. In conclusion, YOLOv8 emerges as a promising tool for accurate grape bunch detection under real field conditions, effectively overcoming previous technological limitations. Full article

Accurate, timely, and resource-efficient decision-making is critical for sustainable precision agriculture. This paper proposes an agentic AI-based Internet of Things (IoT) framework that enables coordinated, closed-loop perception–decision–action processes across heterogeneous sensing and actuation components. The framework models agricultural systems as distributed collections of goal-driven agents responsible for multimodal sensing, uncertainty-aware reasoning, and adaptive decision-making. To provide a structured foundation, the proposed architecture is formalized within a Multi-Agent Partially Observable Markov Decision Process (MPOMDP) perspective, enabling systematic treatment of coordination, uncertainty, and decision policies. The framework integrates multimodal information sources, including vision-based perception and environmental sensing, and defines mechanisms for their fusion and use in system-level decision-making. A proof-of-concept instantiation is presented using publicly available datasets, combining visual perception models and tabular reasoning models within the proposed agentic workflow. The experiments are designed to demonstrate the feasibility, modularity, and coordination capabilities of the framework, rather than to benchmark predictive performance or provide field-validated evaluation. The results illustrate how multimodal information can be integrated to support adaptive and resource-aware decision processes. Finally, the paper discusses key challenges and outlines directions for future work, including real-world deployment, integration with physical actuation systems, and validation under operational conditions. Full article

Accurate and non-destructive evaluation of grape quality is crucial for intelligent viticulture, yet most existing approaches address cultivar classification and soluble solid content (SSC) prediction as independent tasks based on single-modality data, limiting robustness and practical applicability. This study proposes DualStream-RTNet, a unified multimodal deep learning framework that simultaneously performs grape cultivar classification and SSC prediction by integrating RGB-HSV fused images and PCA-compressed hyperspectral spectra. The dual-stream architecture enables the complementary learning of external chromatic–textural cues and internal physicochemical information, while a Transformer-enhanced fusion module strengthens global representation and cross-modal correlation. A dataset of 864 berries from five grape cultivars was used to validate the model. DualStream-RTNet achieved 93.64% classification accuracy, outperforming ResNet18 and other CNN baselines, and produced more compact and consistent confusion-matrix patterns. For SSC prediction, it consistently yielded the highest performance across cultivars, with R2p values up to 0.9693 and RMSE as low as 0.2567, surpassing the PLSR, SVR, LSTM, and Transformer regression models. These results demonstrate the superiority of the proposed framework in capturing both visual and spectral characteristics. DualStream-RTNet provides an efficient and scalable solution for comprehensive grape quality assessment, offering strong potential for real-time sorting, precision grading, and smart agricultural applications. Full article

Interannual variability in climatic conditions represents a major source of uncertainty in cool-climate viticulture, highlighting the need for cultivar-specific assessments of climate–quality relationships. A multi-year on-farm experiment with six monitoring sites has been conducted in vineyards representative of the Tokaj wine region to monitor and assess vintage effect. This study, as the first part of a broader research project evaluating must components, quantifies relationships between climatic indices and key yield- and sugar-related traits (berry weight, total soluble solids, and total dry extract) in Vitis vinifera L. cv. Furmint grown in the Tokaj wine region over three contrasting vintages. Thermal, radiative, and water-availability variables were calculated for discrete phenological phases and statistically analyzed to identify climatic predictors of berry growth and must composition. Berry weight exhibited pronounced vintage sensitivity, showing consistent associations with precipitation-related variables during early developmental stages. In contrast, total soluble solids and total dry extract displayed weaker and less consistent responses to interannual climatic variability. Several widely used heat-accumulation indices showed limited explanatory power, indicating a moderate climatic sensitivity of sugar-related traits in this cultivar. Overall, the results suggest that early-season climatic conditions exert a stronger influence on berry growth than late-season thermal extremes, while compositional parameters related to sugar accumulation remain comparatively stable. These findings highlight the need to incorporate cultivar-specific response functions into statistical models that assess projected climate-change effects on grape quality. Full article

Hyperspectral imagery provides detailed insights for vineyard vegetation assessment, enabling improved pesticide management within precision agriculture. For this reason, the dataset presented here includes hyperspectral images acquired from grapevine leaves treated with two copper-based formulations: ZZ Cuprocol (containing 70% w/v copper oxychloride) and Cuprantol Duo (composed of 14% w/w copper oxychloride and 14% w/w copper hydroxide). In addition, a commonly used contact pesticide in both intensive and traditional viticulture, Folpet—free of copper but containing sulfur and chlorine—was also evaluated in its commercial formulation Vitipec Azul (Cimoxanil 6% w/w, Folpet 37.5% w/w, Ascenza, Portugal). For each product, six different dilution levels were prepared along with a distilled water control. Leaf samples were collected and analyzed during the 2023 growing season from three shoot locations (basal, middle, and apical) and from both orientations of the vine canopy: east and west. Following pesticide treatment, leaf hyperspectral images were captured using a 300-band Pika L camera (Resonon, Bozeman, MT, USA), mounted on a mechanical scanning platform synchronized with the imaging system. Full article

Downy mildew (Plasmopara viticola) poses a major and recurring threat to Greek viticulture, yet existing point-based forecasting models require in-vineyard stations, limiting scalability in fragmented landscapes. This study introduces a spatially explicit model (MeteoGrape) using one fully equipped reference meteorological station plus eight distributed sensors across an 85 km² area in Kavala, Greece. The model is structured in three phases. In Phase A, a single reference station was paired with eight low-cost distributed sensors to reconstruct hourly temperature and relative humidity data through regression correction and radial basis function interpolation, generating a 342-cell grid at 0.005° resolution. During Phases B and C, deterministic epidemiological rules were applied to simulate oospore development, with accumulated degree-hours and humidity exposure converted into spatial risk classifications. Cross-validation (leave-one-sensor-out) confirms meteorological reliability. The model captured an elevated risk period beginning on 16 May, preceding the regional advisory bulletin (23 May), and mapped the spatial distribution of accumulated risk through late May. Validation supports temporal consistency at the regional scale, while fine-scale spatial accuracy is identified as a subject for future field-based evaluation. The framework demonstrates the feasibility of extending established point-based disease models into spatially explicit risk maps under limited meteorological infrastructure. Full article

Water scarcity increasingly threatens viticulture in the Macaronesian region due to climatic variability and recurrent droughts. This study evaluated the physiological and productive responses of grapevines (Vitis vinifera L.) to foliar applications of two amino acid-based biostimulants, pyroglutamic acid and pipecolic acid, under contrasting water availability conditions on Madeira Island, Portugal. Three non-irrigated treatments were arranged in a randomized complete block design: T1 (no irrigation and no amino acids), T2 (pyroglutamic acid, without irrigation), and T3 (pipecolic acid, without irrigation), while conventional irrigation (T4) was included as a non-randomized reference. Agronomic parameters and UAV-derived multispectral and thermal data were analyzed during the 2023 (moderate drought) and 2024 (severe drought) growing seasons. Vegetation indices (NDVI, GNDVI, NDRE, NGRDI, and GLI) and the Simplified Crop Water Stress Index (CWSI_si) were used to assess canopy vigor and plant water status. In 2023, T4 showed significantly higher bunch number and total yield, whereas differences among non-irrigated treatments were not statistically significant. Nevertheless, T2 showed consistent numerical trends toward higher yield components and a comparatively more stable canopy thermal response than the untreated control. In 2024, severe drought reduced productivity across all treatments, with no significant difference detected. Yield components were generally strongly correlated, while CWSI_si was negatively associated with vegetation indices, particularly under moderate drought. The NGRDI demonstrated potential as a low-cost RGB-based indicator but requires cautious interpretation. Overall, pyroglutamic acid may represent a complementary strategy to irrigation and UAV-based precision monitoring in drought-prone viticulture, although confirmation through longer-term and higher-powered field studies is required. Full article

This study focuses on identifying wine-growing subzones within the PDO Amyndeon and PGI Drama wine-growing zones in Northern Greece, with the aim of assessing their suitability for producing high-quality red wines from the Xinomavro (Vitis vinifera L.) and Cabernet Sauvignon (Vitis vinifera L.) grape varieties, respectively. The initial delineation of suitability zones was carried out using readily available satellite data on soil, topography, and climate, in four different suitability categories. To validate how effectively these categories distinguished actual wine-growing regions, we compared them against two years of field data collected from experimental vineyards. The results showed that this methodology was able to discern the most suitable areas for both varieties and regions with an acceptable relation to real grape and wine attributes as confirmed by the collection of data from the pilot vineyards. The overall performance of this method will ultimately depend on the validity of the expert knowledge used to define the most critical parameters and their range. According to the results of this study, and given the relevance of the proposed suitability criteria, this method has the potential to provide an alternative solution for subzone delineation in cases where wine analytical and sensory data are not available. Full article

Estimating grape yields in viticulture is hindered by persistent challenges, including strong occlusion between grapes, irregular cluster morphologies, and fluctuating illumination throughout the growing season. This study introduces YOLOv11-IMP, an improved multiscale anchor-free detection framework extending YOLOv11, tailored to vineyard environments. Its architecture comprises five specialized components: (i) a viticulture-oriented backbone employing cross-stage partial fusion with depthwise convolutions for enriched feature extraction, (ii) a bifurcated neck enhanced by large-kernel attention to expand the receptive field coverage, (iii) a scale-adaptive anchor-free detection head for robust multiscale localization, (iv) a cross-modal processing module integrating visual features with auxiliary textual descriptors to enable fine-grained cluster-level yield estimation, and (v) aross multiple scales. This work evaluated YOLOv11-IMP on five grape varieties collecten augmented spatial pyramid pooling module that aggregates contextual information acd under diverse environmental conditions. The framework achieved 94.3% precision and 93.5% recall for cluster detection, with a mean absolute error (MAE) of 0.46 kg per vine. The robustness tests found less than 3.4% variation in accuracy across lighting and weather conditions. These results demonstrate that YOLOv11-IMP can deliver high-fidelity, real-time yield data, supporting decision-making for precision viticulture and sustainable agricultural management. Full article