Search Results (24)

Agriculture faces growing pressure to optimize water use, particularly in woody perennial crops where irrigation systems are installed once and seldom redesigned despite changes in canopy structure, soil conditions, or plant mortality. Such static layouts may accumulate inefficiencies over time. This study introduces a decision support system (DSS) that evaluates the hydraulic adequacy of existing irrigation systems using two new concepts: the Resource Overutilization Ratio (ROR) and the Irrigation Ecolabel. The ROR quantifies the deviation between the actual discharge of an installed irrigation network and the theoretical discharge required from crop water needs and user-defined scheduling assumptions, while the ecolabel translates this value into an intuitive A+++–D scale inspired by EU energy labels. Crop water demand was estimated using the FAO-56 Penman–Monteith method and adjusted using canopy cover derived from UAV-based canopy height models. A vineyard case study in Galicia (Spain) serves an example to illustrate the potential of the DSS. Firstly, using a fixed canopy cover, the FAO-based workflow indicated moderate oversizing, whereas secondly, UAV-derived canopy measurements revealed substantially higher oversizing, highlighting the limitations of non-spatial or user-estimated canopy inputs. This contrast (A+ vs. D rating) illustrates the diagnostic value of integrating high-resolution geospatial information when canopy variability is present. The DSS, released as open-source software, provides a transparent and reproducible framework to help farmers, irrigation managers, and policymakers assess whether existing drip systems are hydraulically oversized and to benchmark system performance across fields or management scenarios. Rather than serving as an irrigation scheduler, the DSS functions as a standardized diagnostic tool for identifying oversizing and supporting more efficient use of water, energy, and materials in perennial cropping systems. Full article

The rapid and reliable acquisition of canopy-related metrics is essential for improving decision support in viticultural management, particularly when monitoring individual vines for targeted interventions. This study presents a spatially explicit workflow that integrates Uncrewed Aerial System (UAS) imagery, 3D point-cloud analysis, and Object-Based Image Analysis (OBIA) to detect and monitor individual grapevines throughout the growing season. Vines are identified directly from 3D point clouds without the need for prior training data or predefined row structures, achieving a mean Euclidean distance of 10.7 cm to the reference points. The OBIA framework segments vine vegetation based on spectral and geometric features without requiring pre-clipping or manual masking. All non-vine elements—including soil, grass, and infrastructure—are automatically excluded, and detailed canopy masks are created for each plant. Vegetation indices are computed exclusively from vine canopy objects, ensuring that soil signals and internal canopy gaps do not bias the results. This enables accurate per-vine assessment of vigour. NDRE values were calculated at three phenological stages—flowering, veraison, and harvest—and analyzed using Local Indicators of Spatial Association (LISA) to detect spatial clusters and outliers. In contrast to value-based clustering methods, LISA accounts for spatial continuity and neighborhood effects, allowing the detection of stable low-vigour zones, expanding high-vigour clusters, and early identification of isolated stressed vines. A strong correlation (R² = 0.73) between per-vine NDRE values and actual yield demonstrates that NDRE-derived vigour reliably reflects vine productivity. The method provides a transferable, data-driven framework for site-specific vineyard management, enabling timely interventions at the individual plant level before stress propagates spatially. Full article

Precise monitoring of vegetative growth is essential for assessing crop responses to environmental changes. Conventional methods of geometric characterization of plants such as RGB imaging, multispectral sensing, and manual measurements often lack precision or scalability for growth monitoring of rice. LiDAR offers high-resolution, non-destructive 3D canopy characterization, yet applications in rice cultivation across different growth stages remain underexplored, while LiDAR has shown success in other crops such as vineyards. This study addresses that gap by using LiDAR for geometric characterization of rice plants at early, middle, and late growth stages. The objective of this study was to characterize rice plant geometry such as plant height, canopy volume, row distance, and plant spacing using the proximal LiDAR sensing technique at three different growth stages. A commercial LiDAR sensor (model: VPL−16, Velodyne Lidar, San Jose, CA, USA) mounted on a wheeled aluminum frame for data collection, preprocessing, visualization, and geometric feature characterization using a commercial software solution, Python (version 3.11.5), and a custom algorithm. Manual measurements compared with the LiDAR 3D point cloud data measurements, demonstrating high precision in estimating plant geometric characteristics. LiDAR-estimated plant height, canopy volume, row distance, and spacing were 0.5 ± 0.1 m, 0.7 ± 0.05 m³, 0.3 ± 0.00 m, and 0.2 ± 0.001 m at the early stage; 0.93 ± 0.13 m, 1.30 ± 0.12 m³, 0.32 ± 0.01 m, and 0.19 ± 0.01 m at the middle stage; and 0.99 ± 0.06 m, 1.25 ± 0.13 m³, 0.38 ± 0.03 m, and 0.10 ± 0.01 m at the late growth stage. These measurements closely matched manual observations across three stages. RMSE values ranged from 0.01 to 0.06 m and r² values ranged from 0.86 to 0.98 across parameters, confirming the high accuracy and reliability of proximal LiDAR sensing under field conditions. Although precision was achieved across growth stages, complex canopy structures under field conditions posed segmentation challenges. Further advances in point cloud filtering and classification are required to reliably capture such variability. Full article

Among grapevine trunk diseases, Eutypa dieback, caused by the fungus Eutypa lata, is one of the most critical ones, due to its widespread infection in vineyards and the lack of effective treatments. This fungus is a vascular pathogen that enters grapevines through pruning wounds. The infection process is associated with phytotoxic metabolites produced by the fungus, and as such, the identification of new metabolites from different culture conditions and broths could provide valuable insights into the fungus’s enzymatic system and help its control. For the purposes of this study, the OSMAC (one strain, many compounds) approach was applied to investigate the secondary metabolism of E. lata strain 311 isolated from Vitis vinifera plants in Spain. A total of twenty metabolites were isolated, including five reported for the first time from E. lata and four that are newly identified compounds in the literature: eulatagalactoside A, (R)-2-(4′-hydroxy-3′-methylbut-1′-yn-1′-yl)-4-(hydroxymethyl)phenol, (S)-7-hydroxymethyl-3-methyl-2,3-dihydro-1-benzoxepin-3-ol, and (3aR,4S,5R,7aS)-4,5-dihydroxy-6-((R)-3′-methylbuta-1′,3′-dien-1′-ylidene)hexahydrobenzo[d][1,3]dioxol-2-one. These compounds were extracted from fermentation broths using silica gel column chromatography and high-performance liquid chromatography (HPLC). Their structures were elucidated through extensive 1D and 2D NMR spectroscopy, along with high-resolution electrospray ionization mass spectrometry (HRESIMS). Compounds were evaluated for phytotoxicity against Phaseolus vulgaris, with only eulatagalactoside A producing white spots after 48 h. Additionally, the antibacterial activity against Escherichia coli, Staphylococcus aureus, and Klebsiella pneumoniae of selected compounds was tested. The compounds (R)-2-(4′-hydroxy-3′-methylbut-1′-yn-1′-yl)-4-(hydroxymethyl)phenol and (S)-7-(hydroxymethyl)-3-methyl-2,3-dihydrobenzo[b]oxepin-3-ol showed the most significant antimicrobial activity against Gram-positive bacteria, inhibiting S. aureus by over 75%, with IC₅₀ values of 511.4 µg/mL and 617.9 µg/mL, respectively. Full article

Navigating unstructured roads in vineyards with weak satellite signals presents significant challenges for robotic systems. This research introduces DoubleNet, an innovative deep-learning model designed to generate navigation lines for such conditions. To improve the model’s ability to extract image features, DoubleNet incorporates several key innovations, such as a unique multi-head self-attention mechanism (Fused-MHSA), a modified activation function (SA-GELU), and a specialized operation block (DNBLK). Based on them, DoubleNet is structured as an encoder–decoder network that includes two parallel subnetworks: one dedicated to processing 2D feature maps and the other focused on 1D tensors. These subnetworks interact through two feature fusion networks, which operate in both the encoder and decoder stages, facilitating a more integrated feature extraction process. Additionally, we utilized a specially annotated dataset comprising images fused with RGB and mask, with five navigation points marked to enhance the accuracy of point localization. As a result of these innovations, DoubleNet achieves a remarkable 95.75% percentage of correct key points (PCK) and operates at 71.16 FPS on our dataset, with a combined performance that outperformed several well-known key point detection algorithms. DoubleNet demonstrates strong potential as a competitive solution for generating effective navigation routes for robots operating in vineyards with unstructured roads. Full article

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. Full article

The microbial communities of the rhizospheres of vineyards have been subject to a considerable body of research, but it is still unclear how the applied soil cultivation methods are able to change the structure, composition, and level of diversity of their communities. Rhizosphere samples were collected from three neighbouring vineyards with the same time of planting and planting material (rootstock: Teleki 5C; Vitis vinifera: Müller Thurgau). Our objective was to examine the diversity occurring in bacterial community structures in vineyards that differ only in the methods of tillage procedure applied, namely intensive (INT), extensive (EXT), and abandoned (AB). For that we took samples from two depths (10–30 cm (shallow = S) and 30–50 cm (deep = D) of the grape rhizosphere in each vineyard and the laboratory and immediately prepared the slices of the roots for DNA-based analysis of the bacterial communities. Bacterial community structure was assessed by means of PCR-DGGE analysis carried out on the v3 region of 16S rRNA gene. Based on the band composition of the DGGE profiles thus obtained, the diversity of the microbial communities was evaluated and determined by the Shannon–Weaver index (H′). Between the AB and EXT vineyards at the S depth, the similarity of the community structure was 55%; however, the similarity of the D samples was more than 80%, while the difference between the INT samples and the other two was also higher than 80%. Based on our results, we can conclude that intensive cultivation strongly affects the structure and diversity of the bacterial community. Full article

A three-year survey was conducted to estimate the incidence of grapevine trunk diseases (GTDs) in Greece and identify fungi associated with the disease complex. In total, 310 vineyards in different geographical regions in northern, central, and southern Greece were surveyed, and 533 fungal strains were isolated from diseased vines. Morphological, physiological and molecular (5.8S rRNA gene-ITS sequencing) analyses revealed that isolates belonged to 35 distinct fungal genera, including well-known (e.g., Botryosphaeria sp., Diaporthe spp., Eutypa sp., Diplodia sp., Fomitiporia sp., Phaeoacremonium spp., Phaeomoniella sp.) and lesser-known (e.g., Neosetophoma sp., Seimatosporium sp., Didymosphaeria sp., Kalmusia sp.) grapevine wood inhabitants. The GTDs-inducing population structure differed significantly among the discrete geographical zones. Phaeomoniella chlamydospora (26.62%, n = 70), Diaporthe spp. (18.25%, n = 48) and F. mediterranea (10.27%, n = 27) were the most prevalent in Heraklion, whereas D. seriata, Alternaria spp., P. chlamydospora and Fusarium spp. were predominant in Nemea (central Greece). In Amyntaio and Kavala (northern Greece), D. seriata was the most frequently isolated species (>50% frequency). Multi-genes (rDNA-ITS, LSU, tef1-α, tub2, act) sequencing of selected isolates, followed by pathogenicity tests, revealed that Neosetophoma italica, Seimatosporium vitis, Didymosphaeria variabile and Kalmusia variispora caused wood infection, with the former being the most virulent. To the best of our knowledge, this is the first report of N. italica associated with GTDs worldwide. This is also the first record of K. variispora, S. vitis and D. variabile associated with wood infection of grapevine in Greece. The potential associations of disease indices with vine age, cultivar, GTD-associated population structure and the prevailing meteorological conditions in different viticultural zones in Greece are presented and discussed. Full article

Fungicide applications constitute a management practice that reduces the size of fungal populations and by acting as a genetic drift factor, may affect pathogen evolution. In a previous study, we showed that the farming system influenced the population structure of the Aspergillus section Nigri species in Greek vineyards. The current study aimed to test the hypothesis that the differences in the population structure may be associated with the selection of fungicide-resistant strains within the black aspergilli populations. To achieve this, we determined the sensitivity of 102, 151, 19, and 22 for the A. uvarum, A. tubingensis, A. niger, and A. carbonarious isolates, respectively, originating either from conventionally-treated or organic vineyards to the fungicides fluxapyroxad-SDHIs, pyraclostrobin-QoIs, tebuconazole-DMIs, and fludioxonil-phenylpyrroles. The results showed widespread resistance to all four fungicides tested in the A. uvarum isolates originating mostly from conventional vineyards. In contrast, all the A. tubingensis isolates tested were sensitive to pyraclostrobin, while moderate frequencies of only lowly resistant isolates were identified for tebuconazole, fludioxonil, and fluxapyroxad. Sequencing analysis of the corresponding fungicide target encoding genes revealed the presence of H270Y, H65Q/S66P, and G143A mutations in the sdhB, sdhD, and cytb genes of A. uvarum resistant isolates, respectively. No mutations in the Cyp51A and Cyp51B genes were detected in either the A. uvarum or A. tubingensis isolates exhibiting high or low resistance levels to DMIs, suggesting that other resistance mechanisms are responsible for the observed phenotype. Our results support the initial hypothesis for the contribution of fungicide resistance in the black aspergilli population structure in conventional and organic vineyards, while this is the first report of A. uvarum resistance to SDHIs and the first documentation of H270Y or H65Q/S66P mutations in sdhB, sdhD, and of the G143A mutation in the cytb gene of this fungal species. Full article

In this paper, we present a complete and efficient solution of guidance, navigation and control for a quadrotor platform to accomplish 3D coverage flight missions in mapped vineyard terrains. Firstly, an occupancy grid map of the terrain is used to generate a safe guiding coverage path using an Iterative Structured Orientation planning algorithm. Secondly, way-points are extracted from the generated path and added to them trajectory’s velocities and accelerations constraints. The constrained way-points are fed into a Linear Quadratic Regulator algorithm so as to generate global minimum snap optimal trajectory while satisfying both the pointing and the corridor constraints. Then, when facing unexpected obstacles, the quadrotor tends to re-plan its path in real-time locally using an Improved Artificial Potential Field algorithm. Finally, a geometric trajectory tracking controller is developed on the Special Euclidean group $SE\left(3\right)$. The aim of this controller is to track the generated trajectory while pointing towards predetermined direction using the vector measurements provided by the inertial unit. The performance of the proposed method is demonstrated through several simulation results. In particular, safe guiding paths are achieved. Obstacle-free optimal trajectories that satisfy the way-point position, the pointing direction, and the corridor constraints, are successfully generated with optimized platform snap. Besides, the implemented geometric controller can achieve higher trajectory tracking accuracy with an absolute value of the maximum error in the order of ${10}^{-3}$ m. Full article

Grapevine virus A (GVA) is a phloem-restricted virus (genus Vitivirus, family Betaflexiviridae) that cause crop losses of 5–22% in grapevine cultivars, transmitted by different species of pseudococcid mealybugs, the mealybug Heliococcus bohemicus, and by the scale insect Neopulvinaria innumerabilis. In this work, we studied the genetic structure and molecular variability of GVA, ascertaining its presence and spread in different commercial vineyards of four Sicilian provinces (Italy). In total, 11 autochthonous grapevine cultivars in 20 commercial Sicilian vineyards were investigated, for a total of 617 grapevine samples. Preliminary screening by serological (DAS-ELISA) analysis for GVA detection were conducted and subsequently confirmed by molecular (RT-PCR) analysis. Results showed that 10 out of the 11 cultivars analyzed were positive to GVA, for a total of 49 out of 617 samples (8%). A higher incidence of infection was detected on ‘Nerello Mascalese’, ‘Carricante’, ‘Perricone’ and ‘Nero d’Avola’ cultivars, followed by ‘Alicante’, ‘Grecanico’, ‘Catarratto’, ‘Grillo’, ‘Nerello Cappuccio’ and ‘Zibibbo’, while in the ‘Moscato’ cultivar no infection was found. Phylogenetic analyses carried out on the coat protein (CP) gene of 16 GVA sequences selected in this study showed a low variability degree among the Sicilian isolates, closely related with other Italian isolates retrieved in GenBank, suggesting a common origin, probably due to the exchange of infected propagation material within the Italian territory. Full article

In the field of computer vision, 3D reconstruction of crops plays a crucially important role in agriculture. On-ground assessment of geometrical features of vineyards is of vital importance to generate valuable information that enables producers to take the optimum actions in terms of agricultural management. A training system of vines (Vitis vinifera L.), which involves pruning and a trellis system, results in a particular vine architecture, which is vital throughout the phenological stages. Pruning is required to maintain the vine’s health and to keep its productivity under control. The creation of 3D models of vineshoots is of crucial importance for management planning. Volume and structural information can improve pruning systems, which can increase crop yield and improve crop management. In this experiment, an RGB-D camera system, namely Kinect v2, was used to reconstruct 3D vine models, which were used to determine shoot volume on eight differentiated vineyard training systems: Lyre, GDC (Geneva Double Curtain), Y-Trellis, Pergola, Single Curtain, Smart Dyson, VSP (Vertical Shoot Positioned), and the head-trained Gobelet. The results were compared with dry biomass ground truth-values. Dense point clouds had a substantial impact on the connection between the actual biomass measurements in four of the training systems (Pergola, Curtain, Smart Dyson and VSP). For the comparison of actual dry biomass and RGB-D volume and its associated 3D points, strong linear fits were obtained. Significant coefficients of determination (R² = 0.72 to R² = 0.88) were observed according to the number of points connected to each training system separately, and the results revealed good correlations with actual biomass and volume values. When comparing RGB-D volume to weight, Pearson’s correlation coefficient increased to 0.92. The results reveal that the RGB-D approach is also suitable for shoot reconstruction. The research proved how an inexpensive optical sensor can be employed for rapid and reproducible 3D reconstruction of vine vegetation that can improve cultural practices such as pruning, canopy management and harvest. Full article

In precision viticulture, the intra-field spatial variability characterization is a crucial step to efficiently use natural resources by lowering the environmental impact. In recent years, technologies such as Unmanned Aerial Vehicles (UAVs), Mobile Laser Scanners (MLS), multispectral sensors, Mobile Apps (MA) and Structure from Motion (SfM) techniques enabled the possibility to characterize this variability with low efforts. The study aims to evaluate, compare and cross-validate the potentiality and the limits of several tools (UAV, MA, MLS) to assess the vine canopy size parameters (thickness, height, volume) by processing 3D point clouds. Three trials were carried out to test the different tools in a vineyard located in the Chianti Classico area (Tuscany, Italy). Each test was made of a UAV flight, an MLS scanning over the vineyard and a MA acquisition over 48 geo-referenced vines. The Leaf Area Index (LAI) were also assessed and taken as reference value. The results showed that the analyzed tools were able to correctly discriminate between zones with different canopy size characteristics. In particular, the R² between the canopy volumes acquired with the different tools was higher than 0.7, being the highest value of R² = 0.78 with a RMSE = 0.057 m³ for the UAV vs. MLS comparison. The highest correlations were found between the height data, being the highest value of R² = 0.86 with a RMSE = 0.105 m for the MA vs. MLS comparison. For the thickness data, the correlations were weaker, being the lowest value of R² = 0.48 with a RMSE = 0.052 m for the UAV vs. MLS comparison. The correlation between the LAI and the canopy volumes was moderately strong for all the tools with the highest value of R² = 0.74 for the LAI vs. V_MLS data and the lowest value of R² = 0.69 for the LAI vs. V_UAV data. Full article

More than 100 grapevine varieties are registered as suitable for wine production in “Douro” and “Trás-os-Montes” Protected Designations of Origin regions; however, only a few are actually used for winemaking. The identification of varieties cultivated in past times can be an important step to take advantage of all the potential of these regions grape biodiversity. The conservation of the vanishing genetic resources boosts greater product diversification, and it can be considered strategic in the valorisation of these wine regions. Hence, one goal of the present study was to prospect and characterise, through molecular markers, 310 plants of 11 old vineyards that constitute a broad representation of the grape genetic patrimony of “Douro” and “Trás-os-Montes” wine regions; 280 samples, grouped into 52 distinct known varieties, were identified through comparison of their genetic profiles generated via 6 nuclear SSR and 43 informative SNP loci amplification; the remaining 30 samples, accounting for 13 different genotypes, did not match with any profile in the consulted databases and were considered as new genotypes. This study also aimed at evaluating the population structure among the 65 non-redundant genotypes identified, which were grouped into two ancestral genetic groups. The mean probability of identity values of 0.072 and 0.510 (for the 6 SSR and 226 SNP sets, respectively) were determined. Minor differences were observed between frequencies of chlorotypes A and D within the non-redundant genotypes studied. Twenty-seven pedigrees were confirmed and nine new trios were established. Ancestors of eight genotypes remain unknown. Full article

In viticulture, information about vine vigour is a key input for decision-making in connection with production targets. Pruning weight (PW), a quantitative variable used as indicator of vegetative vigour, is associated with the quantity and quality of the grapes. Interest has been growing in recent years around the use of unmanned aerial vehicles (UAVs) or drones fitted with remote sensing facilities for more efficient crop management and the production of higher quality wine. Current research has shown that grape production, leaf area index, biomass, and other viticulture variables can be estimated by UAV imagery analysis. Although SfM lowers costs, saves time, and reduces the amount and type of resources needed, a review of the literature revealed no studies on its use to determine vineyard pruning weight. The main objective of this study was to predict PW in vineyards from a 3D point cloud generated with RGB images captured by a standard drone and processed by SfM. In this work, vertical and oblique aerial images were taken in two vineyards of Godello and Mencía varieties during the 2019 and 2020 seasons using a conventional Phantom 4 Pro drone. Pruning weight was measured on sampling grids comprising 28 calibration cells for Godello and 59 total cells for Mencía (39 calibration cells and 20 independent validation). The volume of vegetation (V) was estimated from the generated 3D point cloud and PW was estimated by linear regression analysis taking V as predictor variable. When the results were leave-one-out cross-validated (LOOCV), the R² was found to be 0.71 and the RMSE 224.5 (g) for the PW estimate in Mencía 2020, calculated for the 39 calibration cells on the grounds of oblique images. The regression analysis results for the 20 validation samples taken independently of the rest (R² = 0.62; RMSE = 249.3 g) confirmed the viability of using the SfM as a fast, non-destructive, low-cost procedure for estimating pruning weight. Full article