Wines & Vines

June 2018 Enology & Viticulture Issue

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June 2018 WINES&VINES 69 WINE EAST GRAPEGROWING sprays, and/or the use of mechanical or hand labor for pruning or harvesting. The basic premise of precision agriculture is that inputs to farming practices are in re- sponse to information gathered with the intent of affecting outputs through an "information feedback-loop system." 5 Applied to viticulture, there is a focus on understanding the spatial and temporal (time-based) variability in wine grape production. 9 Grape growers traditionally have accepted variability within vineyards as normal, which is a basis for the terroir effect. With years of experience, zones in vineyards can be subdivided into individually rated vine- yards of higher or lower quality. Increased availability of geomatics software has allowed grape growers to apply information from vine- yards to precision agriculture, and to target inputs to specific regions of their vineyards. Precision viticulture has been evaluated in New World regions California, 12 Australia 5 and New Zealand, 32 as well as Old World regions such as Spain 35-38 and France. 1 In Ontario, geo- matic technologies were used to identify zones of different water status in Cabernet Franc, 28 Pinot Noir 15 and Riesling. 25-27 Zones of lowest water status were associated with highest monoterpenes in Riesling berries 16 and highest anthocyanins and phenols in Cabernet Franc. 25-27 Data acquisition is central to the imple- mentation of precision viticulture. Tradition- ally, data were acquired on the ground by thorough sampling of various canopy and/or soil variables. This kind of extensive sampling is both time-consuming and expensive. As with any sampling, the accuracy of the data is dependent on the size of the sample popula- tion. Remote sensing techniques have been developed to help mitigate drawbacks of tra- ditional sampling. Early remote sensing "platforms" were sat- ellite- or conventional aircraft-based. These were used to acquire visible, multispectral, hyperspectral and thermal aerial images of vineyards utilizing various cameras and sen- sors. Multispectral sensors have several band measurements (e.g., green, red, near infrared [NIR]) in each pixel of the images they pro- duce. Hyperspectral sensors measure energy in narrower and more numerous bands (up to 200) than multispectral sensors, which provide a continuous spectral measurement across the electromagnetic spectrum. Hyperspectral sen- sors are more sensitive to subtle variations in reflected energy, but they are also very expensive. The usefulness of traditional aerial images initially was limited because of the images' low spatial resolution. Conventional aircraft, and to a lesser extent satellites, still are being used for research and precision viticulture, but the newest, very promising remote sensing tech- nology that has emerged over the past decade, is the unmanned aerial vehicle (UAV) or drone. Drones can collect much more detailed infor- mation, and sometimes at a lower cost, than is possible from aircraft or satellites, mainly because they allow for acquisition of aerial images of a higher spatial resolution than other remote sensing methods. Once data from UAVs are gathered and processed, it can then be used along with ground-based measurements for precision viticulture. This is revolutionizing precision agriculture and promises to assist in defining wine-quality zones at lower costs com- pared to traditional remote sensing methods. Proximal sensing A simpler technology also might have potential use in vineyards and other crops. The recent introduction of GreenSeeker and other proxi- mal sensing (ground-based) technologies could allow growers to identify unique zones within vineyards without use of aircraft, satellites or UAVs. 19 If these zones can be identified easily from the ground, and associated with clear differences in berry composition, it is possible that different wines of varying price points could be created from these zones at minimal cost. Data validation is still necessary, as with remote sensing, to determine if there are rela- tionships between proximally sensed data and other variables of agricultural relevance, but the proximally-sensed data are relatively easy to access. These ground-based technologies generally have been limited to agronomic crops such as corn. 3 In Ontario, GreenSeeker was used in six vineyards to find relationships between the normalized difference vegetative index (NDVI) values and variables such as yield, berry com- position and vine water status. 25-27 Relation- ships were apparent between NDVI data collected by GreenSeeker vs. soil moisture and leaf water potential ( ψ ), in addition to yield components and berry composition. Furthermore, zones indicative of virus infec- tion such as grapevine leafroll-associated virus 3 (GLRaV3) and grapevine red blotch virus (GRBV) could be defined using this technology. Leafroll is one of the most destructive and wide- spread diseases in all grape-growing regions, causing poor color development, and significant reductions in vine growth, size of clusters and berries, and Brix. 8 Red blotch is a recently dis- covered virus characterized by reddening of the leaf area between the main veins. 17 There has been limited experience in measuring unique vegetation indices ("spectral signa- tures") beyond NDVI in virus-infected plants, including one study for GLRaV 22 and another with GRBV. 20 Remote sensing–UAV platforms UAVs have been used for agricultural purposes only relatively recently. Satellites and manned aerial vehicles are still used to obtain aerial images for research and agricultural purposes. There are advantages and disadvantages to each of these types of remote sensing. The time flexibility varies for the different types of remote sensing platforms. Use of manned aircraft depends on the availability of aircraft, and satellites are limited by their cov- erages. 3 Manned aircraft flights have fewer time constraints than satellites but can be ex- pensive and difficult to organize. 4 The spatial resolution of the airborne platforms is also of great importance, depending on the applica- tion of the aerial images. Satellites can take Figures 1a and 1b show examples of UAVs. Figure 1a (left) is a fixed wing SenseFly eBee; Figure 1b (right) is a multi-rotor type UAV flying over a vineyard.

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