EU Funded DataBio Project Results

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
OVERVIEW OF DATABIO PROJECT RESULTS
Dr Thanasis Poulakidas
INTRASOFT Intl
DataBio Coordinator
EBDVF 2019
Helsinki, 14 October 2019

2
Project at a glance
2017 2018 2019
27

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
Overall achievements
27diverse pilots -results in next slides!
95technology components
(60 used in trials), 38datasets,
15pipelines
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
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
Forestry pilots
• Scope
• Multisource and data crowdsourcing
/e-services
• Forest health / Remote/crowd sensing,
Invasive species/damage
• Forest data management
• 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
Fishery pilots
• Scope:
• 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

9
Indicative results from pilots

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

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
-100
-50
0
50
Olive Trees Grapes
Precision Agriculture in Olives, Fruits, Grapes @ Greece

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

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

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
Amountof
freshwaterused
(m3/ha)
2670
2379
2200
2400
2600
2800
Cotton

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).

16
SUCCESS STORIES
Heat wave, 1/7/2017, Larisa, Greece
Insurance @ Greece
Floods, 3/4/2018, Evros, Greece

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

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

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 .

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

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, since March 2018 there were over 3,5M visits and
more than 10,5TB were downloaded

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

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

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

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

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 (2019 trial is ongoing): 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

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

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

29
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EU Funded DataBio Project Results

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