Precision Viticulture is experiencing substantial growth thanks to the availability of improved and cost-effective instruments and methodologies for data acquisition and analysis, such as Unmanned Aerial Vehicles (UAV), that demonstrated to compete with traditional acquisition platforms, such as satellite and aircraft, due to low operational costs, high operational flexibility and high spatial resolution of imagery. In order to optimize the use of these technologies for precision viticulture, their technical, scientific and economic performances need to be assessed. The aim of this work is to compare NDVI surveys performed with UAV, aircraft and satellite, to assess the capability of each platform to represent the intra-vineyard vegetation spatial variability. NDVI images of two Italian vineyards were acquired simultaneously from different multi-spectral sensors onboard the OPEN ACCESS Remote Sens. 2015, 7 2972 three platforms, and a spatial statistical framework was used to assess their degree of similarity. Moreover, the pros and cons of each technique were also assessed performing a cost analysis as a function of the scale of application. Results indicate that the different platforms provide comparable results in vineyards characterized by coarse vegetation gradients and large vegetation clusters. On the contrary, in more heterogeneous vineyards, low-resolution images fail in representing part of the intra-vineyard variability. The cost analysis showed that the adoption of UAV platform is advantageous for small areas and that a break-even point exists above five hectares; above such threshold, airborne and then satellite have lower imagery cost.

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The experiments were conducted in a fully-productive olive orchard (cv. Frantoio) at the experimental farm of University of Pisa at Venturina (Italy) in 2015 to assess the ability of an unmanned aerial vehicle (UAV) equipped with RGB-NIR cameras to estimate leaf area index (LAI), tree height, canopy diameter and canopy volume of olive trees that were either irrigated or rainfed. Irrigated trees received water 4–5 days a week (1348 m3 ha-1), whereas the rainfed ones received a single irrigation of 19 m3 ha-1 to relieve the extreme stress. The flight altitude was 70 m above ground level (AGL), except for one flight (50 m AGL). The Normalized Difference Vegetation Index (NDVI) was calculated by means of the map algebra technique. Canopy volume, canopy height and diameter were obtained from the digital surface model (DSM) obtained through automatic aerial triangulation, bundle block adjustment and camera calibration methods. The NDVI estimated on the day of the year (DOY) 130 was linearly correlated with both LAI and leaf chlorophyll measured on the same date (R2 = 0.78 and 0.80, respectively). The correlation between the on ground measured canopy volumes and the ones by the UAV-RGB camera techniques yielded an R2 of 0.71–0.86. The monthly canopy volume increment estimated from UAV surveys between (DOY) 130 and 244 was highly correlated with the daily water stress integral of rainfed trees (R2 = 0.99). The effect of water stress on the seasonal pattern of canopy growth was detected by these techniques in correspondence of the maximum level of stress experienced by the rainfed trees. The highest level of accuracy (RMSE = 0.16 m) in canopy height estimation was obtained when the flight altitude was 50 m AGL, yielding an R2 value of 0.87 and an almost 1:1 ratio of measured versus estimated canopy height.

Background and Aims:The goal of this study was to investigate the relationships between NDVI values at different phenological stages and measurements of grape parameters at two different harvest dates. Methods and Results: The research was done on a Sangiovese vineyard in Central Italy. Over four seasons, airborne NDVI measurements acquired between June and August were related to grape parameters (yield per vine, pH,°Brix, anthocyanins and polyphenols) at technological harvest (H1) and two weeks later (H2). Correlations were higher at H1 and decreased at H2 with a different rate depending on the parameter.°Brix and pH correlations showed a moderate rate of variation between H1 and H2; bigger differences and a different inter-annual dynamic were observed in anthocyanins and polyphenols between H1 and H2. Conclusions: The ability of NDVI to discriminate different grape classes was confirmed, but its efficacy substantially varies depending on the harvest date. These results suggest the existence, within the same vineyard, of different grape populations having specific timing and shape of ripening curve; as a consequence, distinct vigour zones of the vineyard show a different evolution of the content of grape parameters between the two harvests thus influencing the degree of correlation between grape quality and NDVI measurements. Significance of the Study: This is the first study in which harvest date has been considered for its influence on the predictive skill of RS. It therefore highlights not only the importance of spatial variation within the single vineyard, but also the importance of ripening dynamics. AbbreviationsH1 harvest date 1; H2 harvest date 2; NDVI normalised difference vegetation index; PAB photosynthetically active biomass; PV precision viticulture; RS remote sensing.

In the last few years, high-resolution imaging of vineyards, obtained by unmanned aerial vehicle recognitions, has provided new opportunities to obtain valuable information for precision farming applications. While available semi-automatic image processing algorithms are now able to detect parcels and extract vine rows from aerial images, the identification of single plant inside the rows is a problem still unaddressed. This study presents a new methodology for the segmentation of vine rows in virtual shapes, each representing a real plant. From the virtual shapes, an extensive set of features is discussed, extracted and coupled to a statistical classifier, to evaluate its performance in missing plant detection within a vineyard parcel. Passing from continuous images to a discrete set of individual plants results in a crucial simplification of the statistical investigation of the problem. ARTICLE HISTORY

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Winemaking is a dynamic process, where microbiological and chemical effects may strongly differentiate products from the same vineyard and even between wine vats. This high variability means an increase in work in terms of control and process management. The winemaking process therefore requires a site-specific approach in order to optimize cellar practices and quality management, suggesting a new concept of winemaking, identified as Precision Enology. The Institute of Biometeorology of the Italian National Research Council has developed a wireless monitoring system, consisting of a series of nodes integrated in barrel bungs with sensors for the measurement of wine physical and chemical parameters in the barrel. This paper describes an open-source evolution of the preliminary prototype, using Arduino-based technology. Results have shown good performance in terms of data transmission and accuracy, minimal size and power consumption. The system has been designed to create a low-cost product, which allows a remote and real-time control of wine evolution in each barrel, minimizing costs and time for sampling and laboratory analysis. The possibility of integrating any kind of sensors makes the system a flexible tool that can satisfy various monitoring needs.