Works matching DE "PRECISION viticulture"
Results: 22
A combined geophysical-pedological approach for precision viticulture in the Chianti hills.
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- Bollettino di Geofisica Teorica ed Applicata, 2013, v. 54, n. 2, p. 165, doi. 10.4430/bgta0079
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Proximal sensing mapping method to generate field maps in vineyards.
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- Agricultural Engineering International: CIGR Journal, 2013, v. 15, n. 2, p. 47
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Spatio-temporal dynamics of grape anthocyanin accumulation in a Tempranillo vineyard monitored by proximal sensing.
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- Australian Journal of Grape & Wine Research, 2012, v. 18, n. 2, p. 173, doi. 10.1111/j.1755-0238.2012.00186.x
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Vineyard variability in Marlborough, New Zealand: characterising variation in vineyard performance and options for the implementation of Precision Viticulture.
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- Australian Journal of Grape & Wine Research, 2011, v. 17, n. 1, p. 83, doi. 10.1111/j.1755-0238.2010.00119.x
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An evaluation of EO-1 hyperspectral Hyperion data for chlorophyll content and leaf area index estimation.
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- International Journal of Remote Sensing, 2010, v. 31, n. 4, p. 1079, doi. 10.1080/01431160903252335
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The Importance of Regional and Local Origin in the Choice of Wine: Hedonic Models of Portuguese Wines in Portugal.
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- Journal of Wine Research, 2009, v. 20, n. 1, p. 27, doi. 10.1080/09571260902978527
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Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes.
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- Agriculture; Basel, 2018, v. 8, n. 7, p. 94, doi. 10.3390/agriculture8070094
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Relationships between Water Status, Leaf Chlorophyll Content and Photosynthetic Performance in Tempranillo Vineyards.
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- South African Journal of Enology & Viticulture, 2016, v. 37, n. 2, p. 149, doi. 10.21548/37-2-1004
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Vine Signal Extraction -- an Application of Remote Sensing in Precision Viticulture.
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- South African Journal of Enology & Viticulture, 2010, v. 31, n. 2, p. 65
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Spatial variability in Ontario Cabernet franc vineyards III. Relationships among berry composition variables and soil and vine water status.
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- Journal of Applied Horticulture, 2014, v. 16, n. 3, p. 167, doi. 10.37855/jah.2014.v16i03.30
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Using an Automatic Resistivity Profiler Soil Sensor On-The-Go in Precision Viticulture.
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- Sensors (14248220), 2013, v. 13, n. 1, p. 1121, doi. 10.3390/s130101121
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Test of sampling methods to optimize the calibration of vine water status spatial models.
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- Precision Agriculture, 2018, v. 19, n. 2, p. 365, doi. 10.1007/s11119-017-9523-8
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A comparison between multispectral aerial and satellite imagery in precision viticulture.
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- Precision Agriculture, 2018, v. 19, n. 2, p. 195, doi. 10.1007/s11119-017-9510-0
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Vineyard zone delineation by cluster classification based on annual grape and vine characteristics.
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- Precision Agriculture, 2017, v. 18, n. 4, p. 525, doi. 10.1007/s11119-016-9475-4
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Setting the optimal length to be scanned in rows of vines by using mobile terrestrial laser scanners.
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- Precision Agriculture, 2017, v. 18, n. 2, p. 145, doi. 10.1007/s11119-016-9451-z
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Are precision agriculture tools and methods relevant at the whole-vineyard scale?
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- Precision Agriculture, 2013, v. 14, n. 1, p. 2, doi. 10.1007/s11119-012-9268-3
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Using apparent soil electrical conductivity (EC) to characterize vineyard soils of high clay content.
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- Precision Agriculture, 2011, v. 12, n. 6, p. 775, doi. 10.1007/s11119-011-9220-y
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Variations of soil properties affect the vegetative growth and yield components of 'Tempranillo' grapevines.
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- Precision Agriculture, 2011, v. 12, n. 5, p. 762, doi. 10.1007/s11119-011-9219-4
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Within-season temporal variation in correlations between vineyard canopy and winegrape composition and yield.
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- Precision Agriculture, 2011, v. 12, n. 1, p. 103, doi. 10.1007/s11119-010-9159-4
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Precision Agriculture: Trends, Issues, and Application to Production in the U.S.
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- Western Economics Forum, 2021, v. 19, n. 2, p. 3
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Detection of Flavescence dorée Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery.
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- Remote Sensing, 2017, v. 9, n. 4, p. 308, doi. 10.3390/rs9040308
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Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data.
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- Remote Sensing, 2016, v. 8, n. 3, p. 235, doi. 10.3390/rs8030235
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