SlideShare a Scribd company logo
1 of 28
Download to read offline
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
1
This project has received funding from
the European Union’s Horizon 2020
research and innovation programme
under grant agreement No 732064
This project is part
of BDV PPP
PROJECT OVERVIEW & MAIN RESULTS
Dr Thanasis Poulakidas
INTRASOFT Intl
DataBio Coordinator
BDVA Webinar
17 December 2019
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
2
Project at a glance
2017 2018 2019
27
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
3
Flows and expected outcomes
AGRICULTURE
(13 pilots)
FORESTRY
(8 pilots)
FISHERY
(6 pilots)
Big Data Sources
and Big Data Types
Structured and unstructured data
Spatio-temporal data
Machine generated data
Image/sensor data
Geospatial data
Genomics data
Data
Management
Collection
Preparation
Curation
Linking
Access
Data
Processing
Batch
Interactive
Streaming
Real-time
Data Analytics
Classification
Clustering
Regression
Deep learning
Optimization
Simulation
Raw material production
for Food and Energy
Biomaterials
Responsible
production
Sustainability
Data Visualization and User Interaction
1D, 2D, 3D + temporal
Virtual and Augmented Reality
Validation
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
4
Overall achievements
27diverse pilots -described in next slides
95technology components
(60 used in trials), 38datasets,
16pipelines
Lead project in defining the
BDVA Reference Model
DataBioHub cataloguing
DataBio book (under preparation)
180+events
4360LinkedIn members
611Twitter followers
31exploitable results
Customized business plans
2017 2018 2019
Agriculture Pilot
Forestry Pilot
Fisheries Pilot
DataBio Platform with Pilot
Support
Earth Observation and
GeoSpatial Data and Services
Dissemination and Training
Exploitation and Business
Planning
Specified
pilots
Developed
components
and platform
Executed
pilots
trial 1
Final
docume
ntation
v2
Dissemination and Training
Exploitation and Business Planning
Executing
pilots
trial 2
Final
report
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
5
Agriculture pilots
• Scope
• Olives, fruits, grapes, vegetables
• Genomics in greenhouses
• Cereal, biomass and fiber crops
• Agriculture machinery
• Insurance
• CAP support
• AI/Big Data technologies and added value
➢ Holistic approaches combining Earth Observation, meteo, IoT (incl. drones and machinery) and genomic data
➢ Descriptive and prescriptive analytics with anomaly maps for irrigation, fertilization and pest management
➢ Predictive analytics for the development of data-driven yield models; predictive feedback (monitoring), real-time
streaming data analytics to alert and provide operational recommendations
➢ Genomics prediction models: predictive analytics for genetic merit
➢ Descriptive and predictive analytics on optimized tractor utilization and fault prediction/detection
➢ Diagnostic and descriptive damage assessment based on remote data
➢ Precise crop identification using EO data
13 agriculture pilots
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
6
Forestry pilots
• Scope
• Multisource and data crowdsourcing
/e-services
• Forest health / Remote/crowd sensing,
Invasive species/damage
• Forest data management
• AI/Big Data technologies and added value
• Holistic data sharing platform combining IoT and remote sensing data
with crowdsourcing
• Analytics and apps assessing forest damage
• Feedback analysis on work quality
• Analytics on combos of remote data for detecting pests
• Analytics combining EO data with ecological and ecosystem
factors for risk assessing invasive alien species
8 forestry pilots
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
7
Fishery pilots
• Scope:
• AI/Big Data technologies and added value
• Predictive analytics for engine performance and energy consumption, based in weather and
sea condition predictions using also ship models
• Predictive analytics for data-driven near-real-time decision support optimizing vessel operations
• Machine learning for predicting best fishing grounds
• Real-time CEP to optimize fish catch / fuel consumption
• Hybrid analytics for assessing fish stock from multiple data sources
• Predictive analytics for data-driven projection models of price trends
6 fishery pilots
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
9
Indicative results from pilots
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
10
Precision Agriculture in Olives, Fruits, Grapes @ Greece
PILOT SUMMARY
• Smart Farming pilot focusing on the
exploitation of heterogeneous data,
facts and scientific knowledge to
facilitate decisions and their
application in the field,
• The pilot will promote sustainable
farming practices through the
provision of irrigation, fertilization
and pest/disease management
advices,
• The farmer will directly benefit from
the provided big-data technologies
and advisory services by better
managing the natural resources,
optimizing the use of agricultural
inputs and increasing farm yields. 3 Pilot Sites….
…and 4 Data
Dimensions
DATABIO PARTNERS INVOLVED
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
11
28/11/2017 - The Greek Minister
of Digital Policy, Telecommunications
and Media visits DataBio’s pilot site on
peaches and gets informed about the
benefits gained through the adoption
of big-data technologies.
This visit served as a stepping stone,
as the ministry launched a 28M euros
call for covering the whole Greek
territory with agro-climate sensors,
thus, enabling the provision of Smart
Farming services to all Greek farmers
PILOT INITIAL RESULTS
http://www.ypaithros.gr/en/yannis-olive-grove-
reduction-by-30-in-production-costs-and-
parallel-increase-of-sales/
SUCCESS STORIES
Chalkidiki Pilot
avgcostofspraying
(euros/ha)
avgcostofirrigation
(euros/ha)
Veroia Pilot
Nitrogen
Under/Over
fertilization(%)
0
500
1000
1500
Olive
Trees
Grapes Peaches
Baseline Value Measured Value
0
1000
2000
3000
4000
Olive
Trees
Grapes Peaches
Baseline Value Measured Value
-100
-50
0
50
Olive Trees Grapes
Baseline Value Measured Value
Precision Agriculture in Olives, Fruits, Grapes @ Greece
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
12
Precision agriculture in vegetable seed crops @ Italy
Today: experienced fieldsmen of CAC monitor seed crops
and decide when to start seed harvesting according to
empiric observations
Objective: seed crops monitoring and assessment of right time
for seed harvesting using data from satellites and field sensors
This should result in:
- Better seed quality
- Cost reduction: less field visits
- Additional services for growers
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
13
Precision agriculture in vegetable seed crops @ Italy
• Near real time crop monitoring
• Onions and cabbages: models not very reliable
• The destruction of male lines after pollination
reduces the vegetative covering of soil
• Much better results on sugar beet seed crops,
sunflower and soybean: coherence between
models and the maturity stage of the crops
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
14
Cereal, Biomass and Cotton Crops @ Greece
fieldremote
eye farm
Big
data
Increase Profits, minimize
environmental footprint
PILOT SUMMARY
• Smart farming pilot focusing on the exploitation
of heterogeneous data, facts and scientific
knowledge to facilitate decisions and their
application in the field,
• The pilot will promote sustainable farming
practices through the provision of irrigation
advices,
• Evapotranspiration monitoring is being explored
in order to provide useful information on the
farm’s water availability,
• The farmer will directly benefit from the provided
big-data technologies and advisory services by
better managing the natural resources.
1 pilot site (Kileler, Greece)
1 targeted crop (cotton)
1 advisory service (irrigation)
4 data sources
Data fusion
Advice generation and extrapolation
Decision Support
DATABIO PARTNERS INVOLVED
Amountof
freshwaterused
(m3/ha)
2670
2379
2200
2400
2600
2800
Cotton
Baseline Value Measured Value
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
15
Systemic risk
detection through
agro-climatic
measurements
Vegetation-index continuous
monitoring at parcel level
Crop models generated
through statistical or ML
methodologies used as
baseline
Detection of anomalies and
Reporting after ~2 weeks
Insurance @ Greece
PILOT SUMMARY
• EO-based pilot dedicated for the agriculture
insurance market that eliminates the need for
on-the-spot checks for damage assessment
and promotes rapid pay outs,
• Collaboration with INTERAMERICAN, a high
profile insurance company in Greece,
• Focus on annual crops (tomato, rice, maize,
cotton) and regions (Thessaly, Evros) with
significant economic footprint on the Greek
agri-food sector,
• Comes as a response to specific climate-
related systemic perils (flood, drought,
high/low temperature).
DATABIO PARTNERS INVOLVED
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
16
SUCCESS STORIES
Heat wave, 1/7/2017, Larisa, Greece
Insurance @ Greece
Floods, 3/4/2018, Evros, Greece
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
17
MAIN
STAKEHOLDER
Agency for Payments
and Intervention in
Agriculture (APIA)
GOAL
providing Services in support to the National
and Local Paying Agencies for a more
accurate and complete control of the
farmers’ declaration related to the obligation
introduces by the current Common
Agriculture Policy.
CAP SUPPORT MONITORING WORKFLOW
CAP support @ Romania –Copernicus data for agri monitoring
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
18
VALIDATION
Overall pixel-based
performance
(validated against
APIA data)
97.28% for 32 crops,
16.000+ plots,
60.000+ ha
• Solid results supported by the results of the validation campaigns
• More field campaigns (2019 - 2020)
• Harmonization with the official workflows
• Fine tuning for the continuation of the implementation at national level
• Trials in other countries / geographical areas
CONCLUSIONS & PERSPECTIVES
CAP support @ Romania –Monitoring results
CAP support monitoring
service trials during
2017, 2018 & 2019
for 10 000 sqkm AOI
in Southeastern Romania
Example of observed
crop type maps
32 classes crop maps
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
19
CAP Support @ Greece
PILOT SUMMARY
• Pilot in N.Greece that will evaluate a set of EO-based
services designed appropriately to support specific
needs of the CAP value chain stakeholders and more
specifically to lead to an improved “Greening”
compliance,
• The ambition of the current pilot is to deal effectively
with CAP demands for agricultural crop type
identification, systematic observation, tracking and
assessment of eligibility conditions over a period of
time,
• The pilot is fully aligned with the main concepts of the
new agricultural monitoring system and adopts a
technology-driven traffic light methodology .
DATABIO PARTNERS INVOLVED
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
20
SUCCESS STORIES
Farming Service Center of Thessaloniki, Greece, 2018 Greening Aid Eligibility Compliance
CAP Support @ Greece
Crop group AP Assessment Traffic
Light
Area
Determined
(ha)
AP ID Declared Detected Status Categorization
001 Crop
group 1
Crop group
1
Assessed Compliant 8,3
002 Crop
group 2
Crop group
2
Assessed Compliant 5,1
003 Crop
group 1
Crop group
2
Assessed Non-compliant 4,2
004 Crop
group 1
??? Assessed Insufficient
evidence
2,3
005 Crop
group 3
Crop group
3
Assessed Compliant 10.5
006 Crop
group 2
Not
Assessed
No results
available
0.2
Total 30.4
• Targeted Crops: All major annual regional crops like rice,
wheat, cotton, maize, tobacco, sugar beet, fallow (~20% of
total cultivated area coverage)
• Targeted Parcels: >10ha that need to be checked for the
Greening requirement related to crop diversification
Our approach
• Different ML methodologies have been examined (e.g.
Decision Trees, Random Forest, Support Vector Machines)
• Random Forest exhibited the best overall accuracy (~90%)
in addition to providing the fastest response
• Next steps will focus on classifying crops in new and
unseen data (of different cultivating period), in order to
address challenges imposed by inter-year changes in crop
cultivation periods
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
21
Private forest management platform
• Innovations
• Introduced new XML-based standard for forest management plan
which enables easy data sharing
• Developed mobile app
• For ‘field work quality monitoring’ and ‘forest threat data collection’
• Also collects forest threat data at field (crowd-sourcing)
• Developed new standard interfaces on Wuudis platform to integrate
different forest data sources
• From drone monitoring, very high-resolution satellite data etc.
• Finnish Forestry Center made the forest resource data openly available
• data on timber stock and growth conditions
• data on forest compartments
• Impact
• Current customers of the platform confirm that a savings of 70% working time is
achieved
• Deployed in Finland, business-tested in Spain (Galicia), Belgium (Wallonia) and Czechia
• On the Finnish open data service, over 16TB were downloaded since March 2018
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
22
Web-mapping service for the government decision making
Goals:
• Lack of accurate and timely information
about forest health trends
(bark beetle calamity etc.)
• Identification of cadastres with severe forest dieback and first symptoms of dieback
• Will be used by the Ministry of Agriculture of Czech Republic, regional authorities for rationalization of the
use of available harvesting resources for pro-active management in forests
• Optimization of timber harvesting and processing resources (lack of capacity) to areas where their use is
the most effective example to limit or even stop the bark beetle calamity
• Directing subsidies on this background
Distribution subsidy, legislative measures etc.
Timber harvesting and
processing resources (limited)
Web-mapping service
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
23
Web-mapping service for the government decision making
Total amount of subsidies in Czechia to forestry mainly to mitigate results of bark beetle calamity, climate change etc.:
Results:
• Web-mapping service for government decision making
• According to this map, the Ministry of Agriculture issued „Public decree“ to help forest owners by reducing the
regulation of their obligations so that they can manage the bark beetle calamity (on 4/2019, updated on 9/2019)
• Based to this map and the legislation instrument, the forest owner can modify forest management (optimization of
timber harvesting and processing resources, partial release of regulation for the transfer of seeds etc.)
• All these measures will help reduce the overall loss to forest owners due to climate change and the ongoing bark
beetle calamity in the Czech Republic
• The overall loss may be close to hundreds of millions of euros
2018 2019 2020 2021
25490196EUR 47058824EUR 78431373EUR 98039216EUR
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
24
Oceanic tuna fisheries immediate operational choices
• Goals:
• Improve vessel energy efficiency
• Predict machinery faults
• Vessel loading to minimize fuel consumption
• Improvement 1 of 3: Analysis and visualizations with OpenVA on vessel
energy performance and for comparison with other vessels
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
25
Oceanic tuna fisheries immediate operational choices
• Improvement 2 of 3: Fault detection via
comparing with new engine model
• Improvement 3 of 3: Event detection in advance
with PROTON CEP
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
26
Oceanic tuna fisheries planning
• Goal: Fuel consumption optimization by the use of tools that aid in trip
planning by presenting historical catch data as well as attempting to
forecast where the fish might be in the near future
• Early results: 19% average reduction of fuel consumption per kg of
catch
• High contribution to a chapter of an upcoming FAO report
Green/red cycles: Successful/failed fishing attempts
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
27
Small pelagic fisheries planning
• Norwegian spring spawning herring is a key species in the North Sea, both economically and ecologically
• In DataBio we built a forecast tool based on a marine ecosystem model including migration mechanisms
for the fish
• Migration model for herring in the
dynamic 3D ocean model SINMOD
• The model is particle based and
controlled by food supply,
temperature and ocean current
• Data from fish catch and sea surface
temperature from satellites are used
to improve the forecasts
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
28
Small pelagic market predictions and traceability
• Goal: Finding and catching the right species efficiently and at the
right time and place, while taking into account strategic
considerations like market fluctuations and multiple fisheries
seasons
• In DataBio we have developed a fisheries web portal for the
Norwegian pelagic fisheries for supporting such decisions
• Enables “digging” in historic catch data, in particular to investigate how
prices and catches historically has depended upon season, location and
species
• Enables finding how environmental factors have affected catches, in
particular, covariance between zooplankton concentrations and pelagic
catches
• Shows forecasts for the
marine environment
• After fishermen have found
interesting historical
covariations between
environment and catches,
predictions of the
environment can benefit
choice of fishing grounds
for the next days
This document is part of a project that has received funding
from the European Union’s Horizon 2020 research and innovation programme
under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or
reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu.
29
W www.databio.eu
E info@databio.eu
agriXchange / DataBio
@DataBio_eu
DataBioProject

More Related Content

What's hot

What's hot (20)

05 WP2 Forestry Pilots
05 WP2 Forestry Pilots05 WP2 Forestry Pilots
05 WP2 Forestry Pilots
 
04 WP1 Agriculture Pilots
04 WP1 Agriculture Pilots04 WP1 Agriculture Pilots
04 WP1 Agriculture Pilots
 
Iot and big data technologies for bio industry data bio
Iot and big data technologies for bio industry   data bioIot and big data technologies for bio industry   data bio
Iot and big data technologies for bio industry data bio
 
06 WP3 Fishery pilots
06 WP3 Fishery pilots06 WP3 Fishery pilots
06 WP3 Fishery pilots
 
08 WP7 Exploitation Opportunities
08 WP7 Exploitation Opportunities08 WP7 Exploitation Opportunities
08 WP7 Exploitation Opportunities
 
BDV Webinar Series - Caj - Big Data Breakthroughs for Global Bio-economy Busi...
BDV Webinar Series - Caj - Big Data Breakthroughs for Global Bio-economy Busi...BDV Webinar Series - Caj - Big Data Breakthroughs for Global Bio-economy Busi...
BDV Webinar Series - Caj - Big Data Breakthroughs for Global Bio-economy Busi...
 
H2020 big data and fiware an d iot
H2020 big data and fiware an d iotH2020 big data and fiware an d iot
H2020 big data and fiware an d iot
 
02 agriculture challenges, existing standardisation efforts and data bio agri...
02 agriculture challenges, existing standardisation efforts and data bio agri...02 agriculture challenges, existing standardisation efforts and data bio agri...
02 agriculture challenges, existing standardisation efforts and data bio agri...
 
Big data and ai enhance production of bio resources. Aamu areena Caj Södergå...
Big data and ai enhance production of bio resources.  Aamu areena Caj Södergå...Big data and ai enhance production of bio resources.  Aamu areena Caj Södergå...
Big data and ai enhance production of bio resources. Aamu areena Caj Södergå...
 
01 introduction mildorf
01 introduction mildorf01 introduction mildorf
01 introduction mildorf
 
01 DataBio Workshop in Rome
01 DataBio Workshop in Rome01 DataBio Workshop in Rome
01 DataBio Workshop in Rome
 
Linked data publication pipelines for agri-related use cases
Linked data publication pipelines for agri-related use casesLinked data publication pipelines for agri-related use cases
Linked data publication pipelines for agri-related use cases
 
TOOP project: Once Only Principle
TOOP project: Once Only PrincipleTOOP project: Once Only Principle
TOOP project: Once Only Principle
 
European Data Spaces
European Data SpacesEuropean Data Spaces
European Data Spaces
 
H2020 sc2 calls 2020 mcv
H2020 sc2 calls 2020 mcvH2020 sc2 calls 2020 mcv
H2020 sc2 calls 2020 mcv
 
BDE SC2 Workshop 3: Building a European Data Economy
BDE SC2 Workshop 3: Building a European Data EconomyBDE SC2 Workshop 3: Building a European Data Economy
BDE SC2 Workshop 3: Building a European Data Economy
 
EIPagri y maa mcv
EIPagri y maa mcvEIPagri y maa mcv
EIPagri y maa mcv
 
Towards Open Science in Agriculture & Food
Towards Open Science in Agriculture & FoodTowards Open Science in Agriculture & Food
Towards Open Science in Agriculture & Food
 
The EC strategy to enable data sharing spaces
The EC strategy to enable data sharing spacesThe EC strategy to enable data sharing spaces
The EC strategy to enable data sharing spaces
 
OpenAIRE factsheet: H2020 Open Data Pilot
OpenAIRE factsheet: H2020 Open Data PilotOpenAIRE factsheet: H2020 Open Data Pilot
OpenAIRE factsheet: H2020 Open Data Pilot
 

Similar to BDV Webinar Series - Thanasis - Big Data Breakthroughs for Global Bio-economy Business

DataBio Architecture for Big Data and Big Data Visualisation
DataBio Architecture for Big Data and Big Data VisualisationDataBio Architecture for Big Data and Big Data Visualisation
DataBio Architecture for Big Data and Big Data Visualisation
plan4all
 

Similar to BDV Webinar Series - Thanasis - Big Data Breakthroughs for Global Bio-economy Business (16)

DataBio Architecture for Big Data and Big Data Visualisation
DataBio Architecture for Big Data and Big Data VisualisationDataBio Architecture for Big Data and Big Data Visualisation
DataBio Architecture for Big Data and Big Data Visualisation
 
eROSA Policy WS1: Databio Project Overview
eROSA Policy WS1: Databio Project OvervieweROSA Policy WS1: Databio Project Overview
eROSA Policy WS1: Databio Project Overview
 
Integration of Open Land Use, Smart Point of Interest and Open Transport Map ...
Integration of Open Land Use, Smart Point of Interest and Open Transport Map ...Integration of Open Land Use, Smart Point of Interest and Open Transport Map ...
Integration of Open Land Use, Smart Point of Interest and Open Transport Map ...
 
Linked data publication pipelines for agri-related use cases
Linked data publication pipelines for agri-related use casesLinked data publication pipelines for agri-related use cases
Linked data publication pipelines for agri-related use cases
 
E-infrastructure for open agri-food sciences: Vision & Roadmap
E-infrastructure for open agri-food sciences: Vision & RoadmapE-infrastructure for open agri-food sciences: Vision & Roadmap
E-infrastructure for open agri-food sciences: Vision & Roadmap
 
Forestry Pilot
Forestry PilotForestry Pilot
Forestry Pilot
 
Publication of INSPIRE-based agricultural linked data
Publication of INSPIRE-based agricultural linked dataPublication of INSPIRE-based agricultural linked data
Publication of INSPIRE-based agricultural linked data
 
Linked Data Publication Pipelines for Agri-Related use cases
Linked Data Publication Pipelines for Agri-Related use casesLinked Data Publication Pipelines for Agri-Related use cases
Linked Data Publication Pipelines for Agri-Related use cases
 
FOODIE Project Expo 2015
FOODIE Project Expo 2015 FOODIE Project Expo 2015
FOODIE Project Expo 2015
 
MEASURE H2020 project presented at OW2con'19, June 12-13, 2019, Paris.
 MEASURE H2020 project presented at OW2con'19, June 12-13, 2019, Paris.  MEASURE H2020 project presented at OW2con'19, June 12-13, 2019, Paris.
MEASURE H2020 project presented at OW2con'19, June 12-13, 2019, Paris.
 
MUSHNOMICS Presentation at ICT-AGRI-FOOD Seminar
MUSHNOMICS Presentation at ICT-AGRI-FOOD SeminarMUSHNOMICS Presentation at ICT-AGRI-FOOD Seminar
MUSHNOMICS Presentation at ICT-AGRI-FOOD Seminar
 
D4.2.2 advanced rich interfaces
D4.2.2 advanced rich interfacesD4.2.2 advanced rich interfaces
D4.2.2 advanced rich interfaces
 
2nd e-ROSA Stakeholder workshop: Keizer, Vision Paper
2nd e-ROSA Stakeholder workshop: Keizer, Vision Paper2nd e-ROSA Stakeholder workshop: Keizer, Vision Paper
2nd e-ROSA Stakeholder workshop: Keizer, Vision Paper
 
2017 11 wageningen-keizer
2017 11 wageningen-keizer2017 11 wageningen-keizer
2017 11 wageningen-keizer
 
InfoWeek Digitization Day - The e-ROSA project
InfoWeek Digitization Day - The e-ROSA projectInfoWeek Digitization Day - The e-ROSA project
InfoWeek Digitization Day - The e-ROSA project
 
Digital Innovation Hubs – Digital Transformation of Agriculture at a Regional...
Digital Innovation Hubs – Digital Transformation of Agriculture at a Regional...Digital Innovation Hubs – Digital Transformation of Agriculture at a Regional...
Digital Innovation Hubs – Digital Transformation of Agriculture at a Regional...
 

More from Big Data Value Association

More from Big Data Value Association (20)

Data Privacy, Security in personal data sharing
Data Privacy, Security in personal data sharingData Privacy, Security in personal data sharing
Data Privacy, Security in personal data sharing
 
Key Modules for a trsuted and privacy preserving personal data marketplace
Key Modules for a trsuted and privacy preserving personal data marketplaceKey Modules for a trsuted and privacy preserving personal data marketplace
Key Modules for a trsuted and privacy preserving personal data marketplace
 
GDPR and Data Ethics considerations in personal data sharing
GDPR and Data Ethics considerations in personal data sharingGDPR and Data Ethics considerations in personal data sharing
GDPR and Data Ethics considerations in personal data sharing
 
Intro - Three pillars for building a Smart Data Ecosystem: Trust, Security an...
Intro - Three pillars for building a Smart Data Ecosystem: Trust, Security an...Intro - Three pillars for building a Smart Data Ecosystem: Trust, Security an...
Intro - Three pillars for building a Smart Data Ecosystem: Trust, Security an...
 
Three pillars for building a Smart Data Ecosystem: Trust, Security and Privacy
Three pillars for building a Smart Data Ecosystem: Trust, Security and PrivacyThree pillars for building a Smart Data Ecosystem: Trust, Security and Privacy
Three pillars for building a Smart Data Ecosystem: Trust, Security and Privacy
 
Market into context - Three pillars for building a Smart Data Ecosystem: Trus...
Market into context - Three pillars for building a Smart Data Ecosystem: Trus...Market into context - Three pillars for building a Smart Data Ecosystem: Trus...
Market into context - Three pillars for building a Smart Data Ecosystem: Trus...
 
BDV Skills Accreditation - Future of digital skills in Europe reskilling and ...
BDV Skills Accreditation - Future of digital skills in Europe reskilling and ...BDV Skills Accreditation - Future of digital skills in Europe reskilling and ...
BDV Skills Accreditation - Future of digital skills in Europe reskilling and ...
 
BDV Skills Accreditation - Big Data skilling in Emilia-Romagna
BDV Skills Accreditation - Big Data skilling in Emilia-Romagna BDV Skills Accreditation - Big Data skilling in Emilia-Romagna
BDV Skills Accreditation - Big Data skilling in Emilia-Romagna
 
BDV Skills Accreditation - EIT labels for professionals
BDV Skills Accreditation - EIT labels for professionalsBDV Skills Accreditation - EIT labels for professionals
BDV Skills Accreditation - EIT labels for professionals
 
BDV Skills Accreditation - Recognizing Data Science Skills with BDV Data Scie...
BDV Skills Accreditation - Recognizing Data Science Skills with BDV Data Scie...BDV Skills Accreditation - Recognizing Data Science Skills with BDV Data Scie...
BDV Skills Accreditation - Recognizing Data Science Skills with BDV Data Scie...
 
BDV Skills Accreditation - Objectives of the workshop
BDV Skills Accreditation - Objectives of the workshopBDV Skills Accreditation - Objectives of the workshop
BDV Skills Accreditation - Objectives of the workshop
 
BDV Skills Accreditation - Welcome introduction to the workshop
BDV Skills Accreditation - Welcome introduction to the workshopBDV Skills Accreditation - Welcome introduction to the workshop
BDV Skills Accreditation - Welcome introduction to the workshop
 
BDV Skills Accreditation - Definition and ensuring of digital roles and compe...
BDV Skills Accreditation - Definition and ensuring of digital roles and compe...BDV Skills Accreditation - Definition and ensuring of digital roles and compe...
BDV Skills Accreditation - Definition and ensuring of digital roles and compe...
 
BigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector Webinar
BigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector WebinarBigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector Webinar
BigDataPilotDemoDays - I BiDaaS Application to the Manufacturing Sector Webinar
 
BigDataPilotDemoDays - I-BiDaaS Application to the Financial Sector Webinar
BigDataPilotDemoDays - I-BiDaaS Application to the Financial Sector WebinarBigDataPilotDemoDays - I-BiDaaS Application to the Financial Sector Webinar
BigDataPilotDemoDays - I-BiDaaS Application to the Financial Sector Webinar
 
Virtual BenchLearning - Data Bench Framework
Virtual BenchLearning - Data Bench FrameworkVirtual BenchLearning - Data Bench Framework
Virtual BenchLearning - Data Bench Framework
 
Virtual BenchLearning - DeepHealth - Needs & Requirements for Benchmarking
Virtual BenchLearning - DeepHealth - Needs & Requirements for BenchmarkingVirtual BenchLearning - DeepHealth - Needs & Requirements for Benchmarking
Virtual BenchLearning - DeepHealth - Needs & Requirements for Benchmarking
 
Virtual BenchLearning - I-BiDaaS - Industrial-Driven Big Data as a Self-Servi...
Virtual BenchLearning - I-BiDaaS - Industrial-Driven Big Data as a Self-Servi...Virtual BenchLearning - I-BiDaaS - Industrial-Driven Big Data as a Self-Servi...
Virtual BenchLearning - I-BiDaaS - Industrial-Driven Big Data as a Self-Servi...
 
Policy Cloud Data Driven Policies against Radicalisation - Technical Overview
Policy Cloud Data Driven Policies against Radicalisation - Technical OverviewPolicy Cloud Data Driven Policies against Radicalisation - Technical Overview
Policy Cloud Data Driven Policies against Radicalisation - Technical Overview
 
Policy Cloud Data Driven Policies against Radicalisation - Participatory poli...
Policy Cloud Data Driven Policies against Radicalisation - Participatory poli...Policy Cloud Data Driven Policies against Radicalisation - Participatory poli...
Policy Cloud Data Driven Policies against Radicalisation - Participatory poli...
 

Recently uploaded

一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
pyhepag
 
一比一原版西悉尼大学毕业证成绩单如何办理
一比一原版西悉尼大学毕业证成绩单如何办理一比一原版西悉尼大学毕业证成绩单如何办理
一比一原版西悉尼大学毕业证成绩单如何办理
pyhepag
 
Abortion pills in Dammam Saudi Arabia// +966572737505 // buy cytotec
Abortion pills in Dammam Saudi Arabia// +966572737505 // buy cytotecAbortion pills in Dammam Saudi Arabia// +966572737505 // buy cytotec
Abortion pills in Dammam Saudi Arabia// +966572737505 // buy cytotec
Abortion pills in Riyadh +966572737505 get cytotec
 
一比一原版阿德莱德大学毕业证成绩单如何办理
一比一原版阿德莱德大学毕业证成绩单如何办理一比一原版阿德莱德大学毕业证成绩单如何办理
一比一原版阿德莱德大学毕业证成绩单如何办理
pyhepag
 
Fuzzy Sets decision making under information of uncertainty
Fuzzy Sets decision making under information of uncertaintyFuzzy Sets decision making under information of uncertainty
Fuzzy Sets decision making under information of uncertainty
RafigAliyev2
 
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
pyhepag
 
一比一原版纽卡斯尔大学毕业证成绩单如何办理
一比一原版纽卡斯尔大学毕业证成绩单如何办理一比一原版纽卡斯尔大学毕业证成绩单如何办理
一比一原版纽卡斯尔大学毕业证成绩单如何办理
cyebo
 
一比一原版麦考瑞大学毕业证成绩单如何办理
一比一原版麦考瑞大学毕业证成绩单如何办理一比一原版麦考瑞大学毕业证成绩单如何办理
一比一原版麦考瑞大学毕业证成绩单如何办理
cyebo
 

Recently uploaded (20)

AI Imagen for data-storytelling Infographics.pdf
AI Imagen for data-storytelling Infographics.pdfAI Imagen for data-storytelling Infographics.pdf
AI Imagen for data-storytelling Infographics.pdf
 
一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
一比一原版(Monash毕业证书)莫纳什大学毕业证成绩单如何办理
 
Generative AI for Trailblazers_ Unlock the Future of AI.pdf
Generative AI for Trailblazers_ Unlock the Future of AI.pdfGenerative AI for Trailblazers_ Unlock the Future of AI.pdf
Generative AI for Trailblazers_ Unlock the Future of AI.pdf
 
一比一原版西悉尼大学毕业证成绩单如何办理
一比一原版西悉尼大学毕业证成绩单如何办理一比一原版西悉尼大学毕业证成绩单如何办理
一比一原版西悉尼大学毕业证成绩单如何办理
 
Abortion pills in Dammam Saudi Arabia// +966572737505 // buy cytotec
Abortion pills in Dammam Saudi Arabia// +966572737505 // buy cytotecAbortion pills in Dammam Saudi Arabia// +966572737505 // buy cytotec
Abortion pills in Dammam Saudi Arabia// +966572737505 // buy cytotec
 
一比一原版阿德莱德大学毕业证成绩单如何办理
一比一原版阿德莱德大学毕业证成绩单如何办理一比一原版阿德莱德大学毕业证成绩单如何办理
一比一原版阿德莱德大学毕业证成绩单如何办理
 
Fuzzy Sets decision making under information of uncertainty
Fuzzy Sets decision making under information of uncertaintyFuzzy Sets decision making under information of uncertainty
Fuzzy Sets decision making under information of uncertainty
 
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
一比一原版加利福尼亚大学尔湾分校毕业证成绩单如何办理
 
一比一原版纽卡斯尔大学毕业证成绩单如何办理
一比一原版纽卡斯尔大学毕业证成绩单如何办理一比一原版纽卡斯尔大学毕业证成绩单如何办理
一比一原版纽卡斯尔大学毕业证成绩单如何办理
 
Artificial_General_Intelligence__storm_gen_article.pdf
Artificial_General_Intelligence__storm_gen_article.pdfArtificial_General_Intelligence__storm_gen_article.pdf
Artificial_General_Intelligence__storm_gen_article.pdf
 
Formulas dax para power bI de microsoft.pdf
Formulas dax para power bI de microsoft.pdfFormulas dax para power bI de microsoft.pdf
Formulas dax para power bI de microsoft.pdf
 
How I opened a fake bank account and didn't go to prison
How I opened a fake bank account and didn't go to prisonHow I opened a fake bank account and didn't go to prison
How I opened a fake bank account and didn't go to prison
 
Atlantic Grupa Case Study (Mintec Data AI)
Atlantic Grupa Case Study (Mintec Data AI)Atlantic Grupa Case Study (Mintec Data AI)
Atlantic Grupa Case Study (Mintec Data AI)
 
Machine Learning for Accident Severity Prediction
Machine Learning for Accident Severity PredictionMachine Learning for Accident Severity Prediction
Machine Learning for Accident Severity Prediction
 
Supply chain analytics to combat the effects of Ukraine-Russia-conflict
Supply chain analytics to combat the effects of Ukraine-Russia-conflictSupply chain analytics to combat the effects of Ukraine-Russia-conflict
Supply chain analytics to combat the effects of Ukraine-Russia-conflict
 
一比一原版麦考瑞大学毕业证成绩单如何办理
一比一原版麦考瑞大学毕业证成绩单如何办理一比一原版麦考瑞大学毕业证成绩单如何办理
一比一原版麦考瑞大学毕业证成绩单如何办理
 
2024 Q2 Orange County (CA) Tableau User Group Meeting
2024 Q2 Orange County (CA) Tableau User Group Meeting2024 Q2 Orange County (CA) Tableau User Group Meeting
2024 Q2 Orange County (CA) Tableau User Group Meeting
 
Easy and simple project file on mp online
Easy and simple project file on mp onlineEasy and simple project file on mp online
Easy and simple project file on mp online
 
2024 Q1 Tableau User Group Leader Quarterly Call
2024 Q1 Tableau User Group Leader Quarterly Call2024 Q1 Tableau User Group Leader Quarterly Call
2024 Q1 Tableau User Group Leader Quarterly Call
 
社内勉強会資料  Mamba - A new era or ephemeral
社内勉強会資料   Mamba - A new era or ephemeral社内勉強会資料   Mamba - A new era or ephemeral
社内勉強会資料  Mamba - A new era or ephemeral
 

BDV Webinar Series - Thanasis - Big Data Breakthroughs for Global Bio-economy Business

  • 1. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 1 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732064 This project is part of BDV PPP PROJECT OVERVIEW & MAIN RESULTS Dr Thanasis Poulakidas INTRASOFT Intl DataBio Coordinator BDVA Webinar 17 December 2019
  • 2. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 2 Project at a glance 2017 2018 2019 27
  • 3. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 3 Flows and expected outcomes AGRICULTURE (13 pilots) FORESTRY (8 pilots) FISHERY (6 pilots) Big Data Sources and Big Data Types Structured and unstructured data Spatio-temporal data Machine generated data Image/sensor data Geospatial data Genomics data Data Management Collection Preparation Curation Linking Access Data Processing Batch Interactive Streaming Real-time Data Analytics Classification Clustering Regression Deep learning Optimization Simulation Raw material production for Food and Energy Biomaterials Responsible production Sustainability Data Visualization and User Interaction 1D, 2D, 3D + temporal Virtual and Augmented Reality Validation
  • 4. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 4 Overall achievements 27diverse pilots -described in next slides 95technology components (60 used in trials), 38datasets, 16pipelines Lead project in defining the BDVA Reference Model DataBioHub cataloguing DataBio book (under preparation) 180+events 4360LinkedIn members 611Twitter followers 31exploitable results Customized business plans 2017 2018 2019 Agriculture Pilot Forestry Pilot Fisheries Pilot DataBio Platform with Pilot Support Earth Observation and GeoSpatial Data and Services Dissemination and Training Exploitation and Business Planning Specified pilots Developed components and platform Executed pilots trial 1 Final docume ntation v2 Dissemination and Training Exploitation and Business Planning Executing pilots trial 2 Final report
  • 5. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 5 Agriculture pilots • Scope • Olives, fruits, grapes, vegetables • Genomics in greenhouses • Cereal, biomass and fiber crops • Agriculture machinery • Insurance • CAP support • AI/Big Data technologies and added value ➢ Holistic approaches combining Earth Observation, meteo, IoT (incl. drones and machinery) and genomic data ➢ Descriptive and prescriptive analytics with anomaly maps for irrigation, fertilization and pest management ➢ Predictive analytics for the development of data-driven yield models; predictive feedback (monitoring), real-time streaming data analytics to alert and provide operational recommendations ➢ Genomics prediction models: predictive analytics for genetic merit ➢ Descriptive and predictive analytics on optimized tractor utilization and fault prediction/detection ➢ Diagnostic and descriptive damage assessment based on remote data ➢ Precise crop identification using EO data 13 agriculture pilots
  • 6. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 6 Forestry pilots • Scope • Multisource and data crowdsourcing /e-services • Forest health / Remote/crowd sensing, Invasive species/damage • Forest data management • AI/Big Data technologies and added value • Holistic data sharing platform combining IoT and remote sensing data with crowdsourcing • Analytics and apps assessing forest damage • Feedback analysis on work quality • Analytics on combos of remote data for detecting pests • Analytics combining EO data with ecological and ecosystem factors for risk assessing invasive alien species 8 forestry pilots
  • 7. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 7 Fishery pilots • Scope: • AI/Big Data technologies and added value • Predictive analytics for engine performance and energy consumption, based in weather and sea condition predictions using also ship models • Predictive analytics for data-driven near-real-time decision support optimizing vessel operations • Machine learning for predicting best fishing grounds • Real-time CEP to optimize fish catch / fuel consumption • Hybrid analytics for assessing fish stock from multiple data sources • Predictive analytics for data-driven projection models of price trends 6 fishery pilots
  • 8. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 9 Indicative results from pilots
  • 9. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 10 Precision Agriculture in Olives, Fruits, Grapes @ Greece PILOT SUMMARY • Smart Farming pilot focusing on the exploitation of heterogeneous data, facts and scientific knowledge to facilitate decisions and their application in the field, • The pilot will promote sustainable farming practices through the provision of irrigation, fertilization and pest/disease management advices, • The farmer will directly benefit from the provided big-data technologies and advisory services by better managing the natural resources, optimizing the use of agricultural inputs and increasing farm yields. 3 Pilot Sites…. …and 4 Data Dimensions DATABIO PARTNERS INVOLVED
  • 10. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 11 28/11/2017 - The Greek Minister of Digital Policy, Telecommunications and Media visits DataBio’s pilot site on peaches and gets informed about the benefits gained through the adoption of big-data technologies. This visit served as a stepping stone, as the ministry launched a 28M euros call for covering the whole Greek territory with agro-climate sensors, thus, enabling the provision of Smart Farming services to all Greek farmers PILOT INITIAL RESULTS http://www.ypaithros.gr/en/yannis-olive-grove- reduction-by-30-in-production-costs-and- parallel-increase-of-sales/ SUCCESS STORIES Chalkidiki Pilot avgcostofspraying (euros/ha) avgcostofirrigation (euros/ha) Veroia Pilot Nitrogen Under/Over fertilization(%) 0 500 1000 1500 Olive Trees Grapes Peaches Baseline Value Measured Value 0 1000 2000 3000 4000 Olive Trees Grapes Peaches Baseline Value Measured Value -100 -50 0 50 Olive Trees Grapes Baseline Value Measured Value Precision Agriculture in Olives, Fruits, Grapes @ Greece
  • 11. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 12 Precision agriculture in vegetable seed crops @ Italy Today: experienced fieldsmen of CAC monitor seed crops and decide when to start seed harvesting according to empiric observations Objective: seed crops monitoring and assessment of right time for seed harvesting using data from satellites and field sensors This should result in: - Better seed quality - Cost reduction: less field visits - Additional services for growers
  • 12. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 13 Precision agriculture in vegetable seed crops @ Italy • Near real time crop monitoring • Onions and cabbages: models not very reliable • The destruction of male lines after pollination reduces the vegetative covering of soil • Much better results on sugar beet seed crops, sunflower and soybean: coherence between models and the maturity stage of the crops
  • 13. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 14 Cereal, Biomass and Cotton Crops @ Greece fieldremote eye farm Big data Increase Profits, minimize environmental footprint PILOT SUMMARY • Smart farming pilot focusing on the exploitation of heterogeneous data, facts and scientific knowledge to facilitate decisions and their application in the field, • The pilot will promote sustainable farming practices through the provision of irrigation advices, • Evapotranspiration monitoring is being explored in order to provide useful information on the farm’s water availability, • The farmer will directly benefit from the provided big-data technologies and advisory services by better managing the natural resources. 1 pilot site (Kileler, Greece) 1 targeted crop (cotton) 1 advisory service (irrigation) 4 data sources Data fusion Advice generation and extrapolation Decision Support DATABIO PARTNERS INVOLVED Amountof freshwaterused (m3/ha) 2670 2379 2200 2400 2600 2800 Cotton Baseline Value Measured Value
  • 14. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 15 Systemic risk detection through agro-climatic measurements Vegetation-index continuous monitoring at parcel level Crop models generated through statistical or ML methodologies used as baseline Detection of anomalies and Reporting after ~2 weeks Insurance @ Greece PILOT SUMMARY • EO-based pilot dedicated for the agriculture insurance market that eliminates the need for on-the-spot checks for damage assessment and promotes rapid pay outs, • Collaboration with INTERAMERICAN, a high profile insurance company in Greece, • Focus on annual crops (tomato, rice, maize, cotton) and regions (Thessaly, Evros) with significant economic footprint on the Greek agri-food sector, • Comes as a response to specific climate- related systemic perils (flood, drought, high/low temperature). DATABIO PARTNERS INVOLVED
  • 15. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 16 SUCCESS STORIES Heat wave, 1/7/2017, Larisa, Greece Insurance @ Greece Floods, 3/4/2018, Evros, Greece
  • 16. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 17 MAIN STAKEHOLDER Agency for Payments and Intervention in Agriculture (APIA) GOAL providing Services in support to the National and Local Paying Agencies for a more accurate and complete control of the farmers’ declaration related to the obligation introduces by the current Common Agriculture Policy. CAP SUPPORT MONITORING WORKFLOW CAP support @ Romania –Copernicus data for agri monitoring
  • 17. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 18 VALIDATION Overall pixel-based performance (validated against APIA data) 97.28% for 32 crops, 16.000+ plots, 60.000+ ha • Solid results supported by the results of the validation campaigns • More field campaigns (2019 - 2020) • Harmonization with the official workflows • Fine tuning for the continuation of the implementation at national level • Trials in other countries / geographical areas CONCLUSIONS & PERSPECTIVES CAP support @ Romania –Monitoring results CAP support monitoring service trials during 2017, 2018 & 2019 for 10 000 sqkm AOI in Southeastern Romania Example of observed crop type maps 32 classes crop maps
  • 18. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 19 CAP Support @ Greece PILOT SUMMARY • Pilot in N.Greece that will evaluate a set of EO-based services designed appropriately to support specific needs of the CAP value chain stakeholders and more specifically to lead to an improved “Greening” compliance, • The ambition of the current pilot is to deal effectively with CAP demands for agricultural crop type identification, systematic observation, tracking and assessment of eligibility conditions over a period of time, • The pilot is fully aligned with the main concepts of the new agricultural monitoring system and adopts a technology-driven traffic light methodology . DATABIO PARTNERS INVOLVED
  • 19. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 20 SUCCESS STORIES Farming Service Center of Thessaloniki, Greece, 2018 Greening Aid Eligibility Compliance CAP Support @ Greece Crop group AP Assessment Traffic Light Area Determined (ha) AP ID Declared Detected Status Categorization 001 Crop group 1 Crop group 1 Assessed Compliant 8,3 002 Crop group 2 Crop group 2 Assessed Compliant 5,1 003 Crop group 1 Crop group 2 Assessed Non-compliant 4,2 004 Crop group 1 ??? Assessed Insufficient evidence 2,3 005 Crop group 3 Crop group 3 Assessed Compliant 10.5 006 Crop group 2 Not Assessed No results available 0.2 Total 30.4 • Targeted Crops: All major annual regional crops like rice, wheat, cotton, maize, tobacco, sugar beet, fallow (~20% of total cultivated area coverage) • Targeted Parcels: >10ha that need to be checked for the Greening requirement related to crop diversification Our approach • Different ML methodologies have been examined (e.g. Decision Trees, Random Forest, Support Vector Machines) • Random Forest exhibited the best overall accuracy (~90%) in addition to providing the fastest response • Next steps will focus on classifying crops in new and unseen data (of different cultivating period), in order to address challenges imposed by inter-year changes in crop cultivation periods
  • 20. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 21 Private forest management platform • Innovations • Introduced new XML-based standard for forest management plan which enables easy data sharing • Developed mobile app • For ‘field work quality monitoring’ and ‘forest threat data collection’ • Also collects forest threat data at field (crowd-sourcing) • Developed new standard interfaces on Wuudis platform to integrate different forest data sources • From drone monitoring, very high-resolution satellite data etc. • Finnish Forestry Center made the forest resource data openly available • data on timber stock and growth conditions • data on forest compartments • Impact • Current customers of the platform confirm that a savings of 70% working time is achieved • Deployed in Finland, business-tested in Spain (Galicia), Belgium (Wallonia) and Czechia • On the Finnish open data service, over 16TB were downloaded since March 2018
  • 21. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 22 Web-mapping service for the government decision making Goals: • Lack of accurate and timely information about forest health trends (bark beetle calamity etc.) • Identification of cadastres with severe forest dieback and first symptoms of dieback • Will be used by the Ministry of Agriculture of Czech Republic, regional authorities for rationalization of the use of available harvesting resources for pro-active management in forests • Optimization of timber harvesting and processing resources (lack of capacity) to areas where their use is the most effective example to limit or even stop the bark beetle calamity • Directing subsidies on this background Distribution subsidy, legislative measures etc. Timber harvesting and processing resources (limited) Web-mapping service
  • 22. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 23 Web-mapping service for the government decision making Total amount of subsidies in Czechia to forestry mainly to mitigate results of bark beetle calamity, climate change etc.: Results: • Web-mapping service for government decision making • According to this map, the Ministry of Agriculture issued „Public decree“ to help forest owners by reducing the regulation of their obligations so that they can manage the bark beetle calamity (on 4/2019, updated on 9/2019) • Based to this map and the legislation instrument, the forest owner can modify forest management (optimization of timber harvesting and processing resources, partial release of regulation for the transfer of seeds etc.) • All these measures will help reduce the overall loss to forest owners due to climate change and the ongoing bark beetle calamity in the Czech Republic • The overall loss may be close to hundreds of millions of euros 2018 2019 2020 2021 25490196EUR 47058824EUR 78431373EUR 98039216EUR
  • 23. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 24 Oceanic tuna fisheries immediate operational choices • Goals: • Improve vessel energy efficiency • Predict machinery faults • Vessel loading to minimize fuel consumption • Improvement 1 of 3: Analysis and visualizations with OpenVA on vessel energy performance and for comparison with other vessels
  • 24. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 25 Oceanic tuna fisheries immediate operational choices • Improvement 2 of 3: Fault detection via comparing with new engine model • Improvement 3 of 3: Event detection in advance with PROTON CEP
  • 25. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 26 Oceanic tuna fisheries planning • Goal: Fuel consumption optimization by the use of tools that aid in trip planning by presenting historical catch data as well as attempting to forecast where the fish might be in the near future • Early results: 19% average reduction of fuel consumption per kg of catch • High contribution to a chapter of an upcoming FAO report Green/red cycles: Successful/failed fishing attempts
  • 26. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 27 Small pelagic fisheries planning • Norwegian spring spawning herring is a key species in the North Sea, both economically and ecologically • In DataBio we built a forecast tool based on a marine ecosystem model including migration mechanisms for the fish • Migration model for herring in the dynamic 3D ocean model SINMOD • The model is particle based and controlled by food supply, temperature and ocean current • Data from fish catch and sea surface temperature from satellites are used to improve the forecasts
  • 27. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 28 Small pelagic market predictions and traceability • Goal: Finding and catching the right species efficiently and at the right time and place, while taking into account strategic considerations like market fluctuations and multiple fisheries seasons • In DataBio we have developed a fisheries web portal for the Norwegian pelagic fisheries for supporting such decisions • Enables “digging” in historic catch data, in particular to investigate how prices and catches historically has depended upon season, location and species • Enables finding how environmental factors have affected catches, in particular, covariance between zooplankton concentrations and pelagic catches • Shows forecasts for the marine environment • After fishermen have found interesting historical covariations between environment and catches, predictions of the environment can benefit choice of fishing grounds for the next days
  • 28. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 29 W www.databio.eu E info@databio.eu agriXchange / DataBio @DataBio_eu DataBioProject