Search Results (62)

Identifying crops at the plot level is essential for developing effective agricultural management policies across diverse scales. The agricultural landscape of the Comunitat Valenciana (CV) region in Spain is characterized by a high density of small plots and a wide variety of crops, ranging from rice fields to vine and tree orchards, the latter being the predominant type. This fragmentation poses challenges for current crop monitoring using satellite imagery provided by the Sentinel-2 (S2) mission, largely because its relatively low spatial resolution results in pixels overlapping field boundaries. However, this study proposes a methodological approach that exploits the high temporal resolution of S2 to help overcome these limitations and automatically classify the six most representative crop types in this fragmented landscape. The study analyzed temporal variations in the correlation structure of common spectral indices over the year, leading to the selection of the Normalized Difference Moisture Index (NDMI), Normalized difference Red Edge Index (NDRE), and Plant Senescence Reflectance Index (PSRI) for complementary information. Fourier coefficients of a year time series of these indices served as inputs for a random forest classifier. Relative importance of indices for the classification was also assessed. Additionally, a new metric for classification confidence at plot level is introduced. This metric enables strategies to balance between classification precision and the proportion of classified plots. The model achieved an overall accuracy of 86.85% and a kappa index of 0.82 without considering classification confidence levels. Applying a 70% confidence threshold increased overall accuracy to 93.44% and the kappa index to 0.91 at a cost of 16.19% of plots unclassified. Full article

Sustainable soil management is increasingly recognized as essential for crop health, productivity, and resilience, especially in vineyard ecosystems. Within the B-Wine project, biochar was evaluated as a soil amendment to improve physicochemical properties, water availability, plant eco-physiological functions, and yield. The trial was carried out in one growing season, one year after biochar application (16 t ha⁻¹ fresh weight ≈ 10.4 t ha⁻¹ dry weight) on three organically managed vineyards in the Chianti Classico region (Tuscany, Italy), integrating soil parameters (e.g., organic carbon content, soil moisture, saturated hydraulic conductivity, bulk density) and eco-physiological measurement (e.g., leaf water content, photosynthetic performance) with remote-sensing analysis of multispectral Sentinel-2 level-2A imagery from the Copernicus program and soil spectral measurements. Results indicated that biochar significantly improved key soil properties, although the magnitude of these improvements varied according to soil characteristics. Bulk density decreased by 5–16%, while soil organic carbon increase differed in the three sites, being nearly 50% in the medium-to-fine textured soils and exceeding 200% in the coarse-textured soil. The impact of biochar on saturated hydraulic conductivity varied depending on the soil, the type of biochar, and the moisture conditions. However, it improved the water balance of the vines and yield. Considering all three vineyard sites, the average yield increase was approximately 42%. However, this result was largely driven by pronounced responses at two sites, while the third showed no measurable increase, likely due to site-specific differences in soil properties and climatic conditions. Overall, biochar proved to be an effective, soil-dependent strategy for enhancing vineyard resilience, plant performance, and productivity under challenging conditions. Full article

This study examined how different fertilisation strategies (mineral, compost, vermicompost and non-fertilised control) influence vine nutrient status, must composition and wine chemical characteristics over two consecutive seasons (2024–2025) in two semi-arid Mediterranean vineyards, one deficit-irrigated and other rainfed. Compost and vermicompost were produced from winery residues, in line with a circular management approach. Organic fertilisation improved vine nitrogen and potassium levels, particularly at veraison, with cumulative effects observed over time. Musts from fertilised vines (mineral, compost and vermicompost) exhibited higher levels of yeast-assimilable nitrogen (YAN) and pH, as well as lower titratable acidity, compared to the control group (without fertilization). Wines obtained from these treatments exhibited higher ethanol content and modified acidity parameters, with compositional changes being more evident in the rainfed vineyard. Analysis of volatile compounds revealed that organic fertilisers, particularly vermicompost, promoted the formation of esters, higher alcohols, and terpenes linked to grape metabolism and fermentation. These results demonstrate that organic amendments derived from winery waste can serve as efficient nutrient sources, thereby enhancing the nutritional balance of vines and the composition of wines, while also promoting sustainable and circular practices in viticulture. Full article

This study evaluated the effects of late pruning and late apical leaf removal on grapevine phenology, fruit composition, yield parameters, xylem sap carbohydrate content, and grape skin polyphenol levels over two consecutive vintages (2022 and 2023). As expected, delayed pruning shifted the phenological stages, with more pronounced delays observed in 2022 than in 2023. However, by August, all the treatments had reached the berry-softening stage, indicating a convergence in ripening. The grape juice composition showed no significant differences in sugar content in 2022; however, in 2023, the °Brix was notably reduced in control vines subjected to late apical defoliation. The titratable acidity and pH remained stable across treatments and years, while the malic acid concentrations were consistently higher in the late-pruned treatments, particularly LP2 (late pruning 2 was performed when the control vines had reached the eight-leaves-folded development stage). Late pruning significantly reduced the yield and bunch size, especially for the 2023 LP2 treatment. In contrast, late apical defoliation had minimal impact on the yield components. Vegetative growth, as assessed by cane diameter and weight, also declined under late pruning. Despite this, the xylem sap analysis revealed no significant changes in the glucose, fructose, or myo-inositol levels, suggesting that the carbohydrate reserves remained unaffected. Notably, LP2 consistently resulted in the highest total polyphenol content in the grape skins across both years, indicating enhanced phenolic maturity. Although the polyphenol concentrations were generally higher in 2023, the treatment effects varied more widely, likely due to the differing environmental conditions. These findings suggest that late pruning—particularly LP2—can be a valuable tool for improving grape phenolic quality, albeit at the cost of reduced yield and vine vigor. This study highlights the importance of site- and season-specific canopy management strategies in balancing fruit quality with productivity under variable climatic conditions. Full article

Climate change poses a major challenge for Mediterranean viticulture by accelerating ripening and reducing grape yield and quality. This study evaluated the synergistic effect of two adaptation strategies—non-positioned vegetation training (Sprawl) and foliar kaolin application—on the agronomic and oenological performance of Syrah cv. under semi-arid conditions over two consecutive seasons. Agronomic traits, bunch microclimate, and volatile composition of wines were determined. The combination of Sprawl and kaolin reduced bunch temperature by up to 2 °C, improved vine balance, and maintained optimal acidity and colour intensity. Wines from this treatment exhibited higher concentrations of esters and terpenes, generating more pronounced fruity, floral, and citrus aromas. Multivariate analysis of aroma series revealed clear differences between treatments and vintages, with 2025 showing stronger aromatic distinctions. Heatmap clustering confirmed that vintage was the main differentiating factor, followed by training system. These findings highlight the potential of integrating simple canopy management with reflective particle films to improve grape and wine quality under future Mediterranean conditions. Full article

The replacement of peat in horticultural substrates is a priority for sustainable plant production. This study evaluated compost and vermicompost, derived from vine pruning and sewage sludge, as partial peat substitutes in cucumber (Cucumis sativus L.) seedling production. Germination, early growth traits, growth efficiency indices, and leaf nutrient contents were assessed, and the relationships among variables were explored using correlation analysis and principal component analysis. Five substrates were tested: peat-perlite alone (control) and mixtures containing 10%, 20%, or 40% compost or vermicompost as peat replacements. Results showed that incorporating 10% vermicompost significantly improved germination, seedling vigor, and biomass accumulation, with performance comparable to, or exceeding, the control. In contrast, higher proportions of compost or vermicompost negatively affected germination and seedling quality. Nutrient analysis revealed that 10% vermicompost enhanced Ca and K accumulation, traits positively correlated with growth, whereas 20% compost and 20% vermicompost were associated with higher P and Mg contents but reduced seedling performance. Overall, these promising findings demonstrate that a low proportion of vermicompost (10%) is sufficient to successfully partially replace peat in cucumber seedling production, benefiting both performance and sustainability, whereas higher compost or vermicompost levels disrupt nutrient balance and limit this species’ growth. Full article

Predicting road crash severity remains a major challenge in transportation safety research, requiring models that combine predictive accuracy, interpretability, and computational efficiency. This study introduces a Multi-Risk Index based on Copula Integration (MRI-Copula)—a hybrid framework that integrates Categorical Boosting (CatBoost) with SHapley Additive exPlanations (SHAP) and Vine Copula dependence modeling to assess and predict crash severity. The approach leverages CatBoost–SHAP to quantify the marginal contribution of each risk factor while maintaining model transparency and employs copula-based tail dependence to capture the joint escalation of risk under extreme crash conditions. Using a dataset of 877 police-reported crashes from Jeddah, Saudi Arabia, the framework constructs three interpretable sub-indices—Environmental Risk Index (ERI), Behavioural Risk Index (BRI), and Systemic Risk Index (SRI)—representing distinct domains of crash causation. These indices are combined through a convex weighting parameter (α), optimized via cross-validation (optimal α = 0.80), ensuring a balanced integration of predictive and dependence-based information. Comparative evaluation across multiple classifiers—CatBoost, Light Gradient Boosting Machine (LightGBM), Histogram-based Gradient Boosting (HistGB), and Logistic Regression—demonstrated the robustness of the framework. The CatBoost + MRI-Copula configuration achieved the highest predictive performance (AUC = 0.986; F1 = 0.904), while LightGBM and HistGB offered comparable accuracy (AUC ≈ 0.958; F1 ≈ 0.89) at a fraction of the computational time (≤1 s versus 32 s for CatBoost), highlighting a trade-off between analytical precision and scalability. Consequently, the MRI-Copula framework provides a transparent and theoretically grounded foundation for data-driven road safety management. It bridges predictive analytics and decision support offering a scalable, interpretable, and policy-relevant tool for proactive crash risk mitigation. Full article

This study examined the impact of five grapevine rootstocks (R110, 140Ru, 3309C, 41B, and FERCAL) on must composition, nitrogen status, and sensory attributes of Vitis vinifera L. cv. Assyrtiko wines. Vines were grown under uniform vineyard conditions, and microvinifications were conducted consistently across treatments. Rootstock genotype significantly influenced Baumé, density, titratable acidity, pH, and yeast-assimilable nitrogen (YAN). Musts from R110 contained the highest YAN (226.80 ± 0.99 mg/L) and intermediate Baumé (12.5°), whereas 140Ru exhibited the lowest YAN (132.60 ± 0.46 mg/L) and Baumé (11.7°). Wines from R110 contained the highest tannin concentration (0.375 g/L), while FERCAL produced the highest ethanol content (13.1% vol). Sensory evaluation revealed significant rootstock effects on color intensity, aroma intensity, aroma complexity, balance, and overall quality, with R110 and 3309C receiving the highest scores. The findings demonstrate that rootstock selection may affect the chemical and sensory profile of Assyrtiko wines, providing a practical tool for optimizing wine style and quality across diverse viticultural environments. Further research is needed to confirm these findings and explore extra parameters and novel rootstock–scion interactions. Full article

In the context of growing demand for renewable energy sources and greenhouse gas emission reductions, increasing attention is being paid to the use of agricultural waste as bioenergy feedstock. The energy potential of biomass in the form of vine stems and pomace from the Regent variety of grapes, grafted onto their own roots and various types of rootstocks (125AA, SO₄, 161-49), was assessed, where the control group consisted of ungrafted shrubs growing on their own roots, cultivated in south-eastern Poland. The analyses included the determination of technical and elementary parameters, pollutant emission indicators, and exhaust gas composition parameters. Compared to stems, pomace had a higher calorific value, higher C and H content, and lower dust emissions, while at the same time emitting more CO₂. Stems, on the other hand, showed higher ash content and higher dust emissions, which may limit their energy potential. Among the analysed substrates, pomace from 125AA achieved the highest calorific values at a low moisture content, while biomass from substrate 161-49 was distinguished by the lowest sulphur content and a favourable emission balance. Cluster analysis showed clear grouping of substrates in terms of fuel and emission parameters, indicating the possibility of optimal substrate selection for the production of bioenergy feedstock. The results confirm that the appropriate selection of rootstocks in viticulture can significantly increase the energy value of waste biomass and reduce emissions, supporting the development of local renewable energy systems. Full article

Interrow management in vineyards significantly contributes to sustainable viticulture, particularly in water-scarce European regions. Grassy and herbaceous cover crops have been proven to enhance multiple regulating ecosystem services, including soil conservation, carbon sequestration, and improved water infiltration. However, the potential for water competition with vines necessitates region-specific approaches. This review aims to analyze the effects of different cover crop types and interrow tillage methods on water management and regulating ecosystem services, focusing on main European vineyard areas. The research involved a two-stage literature review by Google Scholar and Scopus, resulting in the identification of 67 relevant scientific publications, with 11 offering experimental data from European contexts. Selected studies were evaluated based on climate conditions, soil properties, slope characteristics, and interrow treatments. Findings highlight that the appropriate selection of cover crop species, sowing and mowing timing, and mulching practices can optimize vineyard resilience under climate stress. Practical recommendations are offered to help winegrowers adopt cost-effective and environmentally adaptive strategies, especially on sloped or shallow soils, where partial cover cropping is often the most beneficial for both yield and ecological balance. Cover crops and mulching reduce erosion, enhance vineyard soil moisture, relieve water stress consequences, and, as a result, these cover cropping techniques can improve yield and nutritional values of grapes (e.g., Brix, pH, K concentration), but effects vary; careful, site-specific, long-term management is essential for best results. Full article

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. Full article

This study aimed to detect weeds in vineyards throughout the crop cycle using pixel-based classification of RGB imagery captured by unmanned aerial vehicles (UAVs). Five vegetation indices (NGRDI, NDVI, GLI, NDRE, and GNDVI) and three supervised classifiers (SVM, RT, and KNN) were evaluated during four flight campaigns. Classification performance was assessed using precision, recall, and F1-Score, supported by descriptive statistics (mean, standard deviation, and 95% confidence interval), inferential tests (Shapiro–Wilk, ANOVA, and Kruskal–Wallis), and visual map inspection. Statistical analyses, both descriptive and inferential, did not indicate significant differences between classification methods. NGRDI consistently showed strong performance, especially for vine and soil classes, and effectively detected weeds, with F1-Scores above 0.78 in some campaigns, occasionally outperforming the supervised classifiers. GLI displayed variable results and a higher sensitivity to noise, whereas NDVI showed limitations when applied to RGB data, particularly in sparsely vegetated areas. Among the classifiers, the SVM achieved the highest F1-Score for vine (0.9330) and soil (0.9231), whereas KNN produced balanced results and visually coherent maps. RT showed lower accuracy and greater variability, particularly in the weed class. Despite the lack of statistically significant differences, visual analysis favored NGRDI and SVM for generating cleaner classification outputs. Study limitations include lighting variability, reduced spatial coverage owing to low flight altitude, and a lack of spatial context in pixel-based methods. Future research should explore object-based approaches and advanced classifiers (e.g., Random Forest and Convolutional Neural Networks) to enhance robustness and generalization. Overall, RGB-based indices, particularly NGRDI, are cost-effective and reliable tools for weed detection, thereby supporting scalable precision in viticulture. Full article

Pruning wood mass is crucial for grapevine management, as it reflects the vine’s vigor and balance. However, traditional manual measurement methods are time-consuming and labor-intensive. Recent advances in digital imaging offer non-invasive techniques, but limited research has explored pruning wood weight estimation, especially regarding the use of artificial backgrounds and lighting. This study assesses the use of image analysis for estimating wood weight, focusing on image acquisition conditions. This research aimed to (i) evaluate the necessity of artificial backgrounds and (ii) identify optimal daylight conditions for accurate image capture. Results demonstrated that estimation accuracy strongly depends on the sun’s position relative to the camera. The highest accuracy was achieved when the camera faced direct sunlight (morning on the northwest canopy side and afternoon on the southeast side), with R² values reaching 0.90 and 0.93, and RMSE as low as 44.24 g. Artificial backgrounds did not significantly enhance performance, suggesting that the method is applicable under field conditions. Leave-One-Group-Out Cross-Validation (LOGOCV) confirmed the model’s robustness when applied to Catarratto cv. (LOGOCV R² = 0.86 in NB and 0.84 in WB), though performance varied across other cultivars. These findings highlight the potential of automated image-based assessment for efficient vineyard management, using minimal effort adjustments to image collection that can be incorporated into low-cost setups for pruning wood weight estimation. Full article

Semi-arid viticultural regions globally are experiencing severe and frequent growing-season heat waves that negatively impact grapevine (Vitis vinifera L.) physiological performance and productivity. At the leaf level, heat stress can photodamage both Photosystem I (PSI) and Photosystem II (PSII). In order to study the self-protection mechanism of grapevine leaves, in this study, 3-year-old potted ‘Merlot’ and ‘Muscat Hamburg’ grapevines were exposed to a 5-day simulated heatwave (45/25 °C day/night) and compared to vines maintained at 25/18 °C. After heat exposure, ‘Merlot’ demonstrated superior thermotolerance and superior physiological performance as measured by gas exchange, oxidative parameters, chlorophyll loss, and photoinhibition of PSI and PSII. Additionally, non-photochemical quenching (NPQ) dissipated the excess light energy in the form of heat. Y(NPQ) progressively rose from 0 to 0.6, signaling the start of the grapevine leaves’ self-defense against temperature stress. Furthermore, the stimulation of cyclic electron flow (CEF) under high temperatures contributed to the energy balance of PSI. The CEF of ‘Muscat Hamburg’ under high light intensities increased dramatically from 1 to 4. NAD(P)H dehydrogenase-dependent CEF around PSI increased markedly, suggesting its role in self-protection. These results demonstrate that both NPQ and CEF play key photoprotective roles by generating a proton gradient under heat stress. Full article

Precision Farming (PF) in vineyards represents an innovative approach to vine cultivation that leverages the advantages of the latest technologies to optimize resource use and improve overall field management. This study investigates the application of PF techniques in a vineyard, focusing on sensor-based decision-making for autonomous driving. The goal of this research is to define a repeatable methodology for virtual testing of autonomous driving operations in a vineyard, considering realistic scenarios, efficient control architectures, and reliable sensors. The simulation scenario was created to replicate the conditions of a real vineyard, including elevation, banking profiles, and vine positioning. This provides a safe environment for training operators and testing tools such as sensors, algorithms, or controllers. This study also proposes an efficient control scheme, implemented as a state machine, to autonomously drive the tractor during two distinct phases of the navigation process: between rows and out of the field. The implementation demonstrates improvements in trajectory-following precision while reducing the intervention required by the farmer. The proposed system was extensively tested in a virtual environment, with a particular focus on evaluating the effects of micro and macro terrain irregularities on the results. A key feature of the control framework is its ability to achieve adequate accuracy while minimizing the number of sensors used, relying on a configuration of a Global Navigation Satellite System (GNSS) and an Inertial Measurement Unit (IMU) as a cost-effective solution. This minimal-sensor approach, which includes a state machine designed to seamlessly transition between in-field and out-of-field operations, balances performance and cost efficiency. The system was validated through a wide range of simulations, highlighting its robustness and adaptability to various terrain conditions. The main contributions of this work include the high fidelity of the simulation scenario, the efficient integration of the control algorithm and sensors for the two navigation phases, and the detailed analysis of terrain conditions. Together, these elements form a robust framework for testing autonomous tractor operations in vineyards. Full article