BigDataGrapes_Table and Wine Grapes Pilot

WWW.BIGDATAGRAPES.EU
BigDataGrapes - Big Data to Enable Global Disruption of the
Grapevine-powered Industries has received funding from the
European Union’s Horizon 2020 research and innovation programme
under grant agreement No 780751.
Table and Wine Grapes
Pilot
“Big Data for the Grapevine Industries” Workshop |
Pisa , Italy, 08/03/2019
Aikaterini Kasimati | Laboratory of Precision Agriculture
Maritina Stavrakaki | Laboratory of
Viticulture
Agricultural University of Athens
“Big Data for the Grapevine Industries”
Workshop

Tables and Wine Grapes Pilot
Introduction & Specific Goals
Technical Guidelines and Methodology
• Site Description
• Equipment Used and Measurements
 Data Collected
 Expected Timeline
 Envisaged Outcomes
“Big Data for the Grapevine Industries” Workshop 2
Presentation Outline

Pilot
3
Introduction & Specific Goals
“Big Data for the Grapevine Industries” Workshop

Table and Wine Grapes Pilot
Introduction

This pilot will continuously collect and monitor sensor, farming and
phenological data derived from all test sites located in Greece.
Pilot’s goal
 Denote associations and correlations between precision agriculture
information and phenological data and grape chemical analysis
Ultimate goal
 Correlate the aforementioned data with earth observation data to
examine the effectiveness of applying machine learning techniques and
eventually train the relevant machine learning components
5
Table and Wine Grapes Pilot
Specific Goals

Pilot
6
Technical Guidelines and
Methodology

WWW.BIGDATAGRAPES.EU 7
Three test sites in the
north-eastern part of
Peloponnese, Greece:
• Palivou Estate
• Kontogiannis
Estate
• Fasoulis Estate
Site Description

• Nemea
• Vitis vinifera L. cv.
‘Agiorgitiko’ and ‘Merlot’
for winemaking
• northeast-southwest
row orientation
• VSP - cane
pruning, double Guyot
training/trellis system
Site Description
Palivou Estate

• Ancient Corinth
• ‘Roditis’, ‘Savatiano’,
‘Mavroudi’ and
‘Soultanina’ for
winemaking north-south
row orientation
• VSP - cane
pruning, double Guyot
or double Royat
training/trellis system
Site Description
Kontogiannis Estate

Site Description
Fasoulis Estate
• Nemea
• 22 different table grape
varieties
• Southeast- Northwest
row orientation

 HiPer V RTK GPS
Topographical data: field boundary points and elevation data
Equipment Used and Measurements
Topographical and Elevation Mapping

 EM38-MK2 probe
Soil electrical conductivity (ECa) at 0.5 and 1.0 m depth (mS/m)
https://photos.app.goo.gl/5LzP3WpcbsRvJEAe9
Geo-referenced Apparent Soil Electrical Conductivity

 Crop Circle ACS-470
Basic reflectance information from plant canopies and classic spectral
vegetative index data (NDVI, NDRE etc.)
Canopy Characteristics and Vegetation Indices

 Crop Circle RapidSCAN CS-45
Basic reflectance information from plant canopies and classic spectral
vegetative index data (NDVI, NDRE etc.)

 SpectroSense2+ GPS
Leaf Area Index (LAI) and NDVI vegetation indices

 Two Phantom 4 Pro drones Parrot Sequoia+ Multispectral sensor and
FLIR Vue Pro thermal infrared sensor
Aerial imagery data, vegetation indices, water activity maps
Drones with Multispectral and Thermal Sensors

 Two Vantage Pro 2 weather stations
Rain sensor, anemometer to measure wind speed and direction, air
temperature sensor, air and soil humidity sensor
Weather and Soil Data

WWW.BIGDATAGRAPES.EUWP8 - Grapevine-powered Industry Application Pilots 18
 ATAGO N1-a refractometer w/ 0-32 Brix measurement range
Soluble solids
 Titration with a 0.1 N NaOH solution
Total titratable acidity -expressed as tartaric acid-
 HPLC Shimadzu Nexera (gradient pump Shimadzu Nexera X2, ProStar
model 410 AutoSampler, and ProStar model 330 Photodiode Array Detector)
Quantitative and qualitative analysis of the substances
 Modified colorimetric method
Antioxidant activity (2,2-diphenyl-1-picrylhydrazyl, DPPH)
 UV/Vis spectrophotometer
Reduction of the DPPH radical @ 517 nm and the absorption of the
antioxidant activity @ 593 nm
Qualitative and Quantitative Data

Expected Timeline

• Identification of grapevine varieties
• Remote sensing for spatial data, topographical and elevation
mapping
• Geo-referenced apparent soil electrical conductivity (ECa)
• Canopy characteristics and vegetation indices
• Water activity and photosynthesis and chlorophyll data
• Qualitative and quantitative characters for wine and table
grapes
• Full phenolic profile of grapevine varieties
• Yield mapping
• Soil, weather and farming data
Data Collected

Pilot
Envisaged Outcomes
21“Big Data for the Grapevine Industries” Workshop

 The collection of datasets for BigDataGrapes will serve
as the basis for carrying out research and technical
work
 These data will contribute to a data marketplace
demonstrator that will serve as the project’s
experimentation environment
 The data pool will be continuously enriched in volume
and range, in accordance with the needs and
requirements of the project
Envisaged Outcomes

Aikaterini Kasimati
Maritina Stavrakaki
AUA
akasimati@aua.gr
maritina@aua.gr
@BigDataGrapes
https://www.linkedin.com/groups/13574473
Thank you!!
23

BigDataGrapes_Table and Wine Grapes Pilot

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