Data bio d1.1-agriculture-pilot-definition_v1.0_2017-06-30_lespro

This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant 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.
Project Acronym: DataBio
Grant Agreement number: 732064 (H2020-ICT-2016-1 – Innovation Action)
Project Full Title: Data-Driven Bioeconomy
Project Coordinator: INTRASOFT International
DELIVERABLE
D1.1 – Agriculture Pilot Definition
Dissemination level PU -Public
Type of Document Report
Contractual date of delivery M06 – 30/6/2017
Deliverable Leader LESPRO
Status - version, date Final – v1.0, 30/6/2017
WP / Task responsible WP1
Keywords: Agriculture, pilot, big data, modelling, user analysis,
user requirements, stakeholders

H2020 Contract No. 732064 Final – v1.0, 30/6/2017
Dissemination level: PU -Public
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Executive Summary
The objective of WP1 Agriculture pilot is to demonstrate how the Big data technologies will
be integrated into the pilots, in order to validate the Big data technologies on practical cases
from agriculture and how it can fulfil the end user communities’ expectations. The Big
technologies will be tested in three areas: arable farming, horticulture and Subsidies an
insurance, where every area will be tested in in pilots with different topics and running in
different countries.
Task 1.1 Co-innovative preparations deal with user understanding specifying the needs of
users and different stakeholders and its main objective is to analyse a set of functional and
non-functional requirements specified from the analysis of the pilot cases. Opportunities for
different solution technologies were reviewed with stakeholders and users are used as an
input and a set of scenarios are described within the bio-economy domain related to the
agriculture sector. Functional requirements are defined and used as input for the application
specification, development and piloting. User and stakeholder study to specify the (most
beneficial) areas of interest from different point-of-views and resulting to detailed scenario
building of the application scenarios from which use cases are defined. This subtask feeds
from user and stakeholder study as input.
The results are the pilot cases definitions including requirements specifications and
evaluation plans.
The organizations that were planned to participate in this task, and their respective planned
work effort in person-months, are Lespro (2), Intrasoft (5), VTT (3), IBM (2), Softeam (2),
Limetri (2), CREA (2), Fraunhofer (6), Vito (6), Tragsa (6), NP (2), Federu (6), CSEM (2), Rikola
(4), Novam (6), EXUS (6), CERTH (6), CITOLIVA (3), GAIA (9), ZETOR (8), CAC (6)
The deliverable D1.1 Agriculture Pilot Definition specifies the pilot case definitions,
requirement specifications, as well as implementation and evaluation plans.

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Deliverable Leader: Karel Charvát (LESPRO)
Contributors:
Karel Charvát jr (LESPRO), Šárka Horáková (LESPRO), Savvas
Rogotis (NP), Antonella Cattuci (e-GEOS), Per Gunnar Auran
(SINTEF Fishery), Athanasios Poulakidas (INTRASOFT), Ephrem
Habyarimana (CREA), Pilot leaders
Reviewers:
Fabiana Fournier (IBM), Tomas Mildorf (UWB), Caj Södergård
(VTT)
Approved by: Athanasios Poulakidas (INTRASOFT)
Document History
Version Date Contributor(s) Description
0.1 20/04/2017 Initial draft
0.2 18/06/2017 Content transferred to new template
0.3 20/06/2017
Pilot descriptions inserted, ArchiMate
diagrams added
0.4 29/06/2017
Final completing all chapters and
formatting
1.0 30/06/2017 Compliance to submission format and
minor changes.

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Table of Contents
EXECUTIVE SUMMARY.....................................................................................................................................2
TABLE OF CONTENTS........................................................................................................................................4
TABLE OF FIGURES ...........................................................................................................................................8
LIST OF TABLES ................................................................................................................................................9
DEFINITIONS, ACRONYMS AND ABBREVIATIONS...........................................................................................11
INTRODUCTION ....................................................................................................................................13
1.1 PROJECT SUMMARY.....................................................................................................................................13
1.2 DOCUMENT SCOPE......................................................................................................................................16
1.3 DOCUMENT STRUCTURE ...............................................................................................................................16
SUMMARY............................................................................................................................................17
2.1 OVERVIEW.................................................................................................................................................17
2.2 PILOT INTRODUCTIONS .................................................................................................................................17
2.3 OVERVIEW OF PILOT CASES............................................................................................................................18
2.4 AGRICULTURE DATASETS UTILIZED IN PILOTS......................................................................................................22
2.5 REPRESENTATION OF PILOT CASES...................................................................................................................22
2.6 PILOT MODELLING FRAMEWORK.....................................................................................................................22
PILOT 1 [A1.1] PRECISION AGRICULTURE IN OLIVES, FRUITS, GRAPES...................................................27
3.1 PILOT OVERVIEW.........................................................................................................................................27
3.1.1 Pilot introduction ..........................................................................................................................27
3.1.2 Pilot overview................................................................................................................................27
3.2 PILOT CASE DEFINITION.................................................................................................................................29
3.2.1 Stakeholder and user stories.........................................................................................................32
3.2.2 Motivation and strategy ...............................................................................................................32
3.3 PILOT MODELLING WITH ARCHIMATE..............................................................................................................33
3.3.1 Agriculture pilot A1.1 Motivation view.........................................................................................33
3.3.2 Agriculture pilot A1.1 Strategy view .............................................................................................33
3.4 PILOT EVALUATION PLAN..............................................................................................................................34
3.4.1 High level goals and KPI's .............................................................................................................34
3.4.2 Initial roadmap .............................................................................................................................35
3.5 BIG DATA ASSETS.........................................................................................................................................35
PILOT 2 [A1.2] PRECISION AGRICULTURE IN VEGETABLE SEED CROPS...................................................37
4.1 PILOT OVERVIEW.........................................................................................................................................37
4.1.2 Pilot overview................................................................................................................................37
4.4.2 Initial roadmap .............................................................................................................................43

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4.5 BIG DATA ASSETS.........................................................................................................................................43
PILOT 3 [A1.3] PRECISION AGRICULTURE IN VEGETABLES_2 (POTATOES) .............................................45
5.1 PILOT OVERVIEW.........................................................................................................................................45
5.1.2 Pilot overview................................................................................................................................45
5.4.2 Initial roadmap .............................................................................................................................51
5.5 BIG DATA ASSETS.........................................................................................................................................52
PILOT 4 [A2.1] BIG DATA MANAGEMENT IN GREENHOUSE ECO-SYSTEM..............................................54
6.1 PILOT OVERVIEW.........................................................................................................................................54
6.1.2 Pilot overview................................................................................................................................54
6.4.2 Initial roadmap .............................................................................................................................63
6.5 BIG DATA ASSETS.........................................................................................................................................64
PILOT 5 [B1.1] CEREALS AND BIOMASS CROP .......................................................................................65
7.1 PILOT OVERVIEW.........................................................................................................................................65
7.1.2 Pilot overview................................................................................................................................65
7.3.1 Agriculture pilot B1.1 motivation view .........................................................................................69
7.3.2 Agriculture pilot B1.1 strategy view..............................................................................................70
7.4.2 Initial roadmap .............................................................................................................................72
7.5 BIG DATA ASSETS.........................................................................................................................................73
PILOT 6 [B1.2] CEREALS AND BIOMASS CROP_2 ...................................................................................74
8.1 PILOT OVERVIEW.........................................................................................................................................74
8.1.2 Pilot overview................................................................................................................................74

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8.3.1 Agriculture pilot B1.2 Motivation view .........................................................................................80
8.3.2 Agriculture pilot B1.2 Strategy view .............................................................................................81
8.4.2 Initial roadmap .............................................................................................................................82
8.5 BIG DATA ASSETS.........................................................................................................................................83
PILOT 7 [B1.3] CEREAL AND BIOMASS CROPS_3....................................................................................84
9.1 PILOT OVERVIEW.........................................................................................................................................84
9.1.2 Pilot overview................................................................................................................................84
9.3.1 Agriculture pilot B1.3 Motivation view .........................................................................................92
9.3.2 Agriculture pilot B1.3 Strategy view .............................................................................................93
9.4.2 Initial roadmap .............................................................................................................................94
9.5 BIG DATA ASSETS.........................................................................................................................................95
PILOT 8 [B1.4] CEREALS AND BIOMASS CROPS_4..................................................................................97
10.1 PILOT OVERVIEW ....................................................................................................................................97
10.1.1 Pilot introduction......................................................................................................................97
10.1.2 Pilot overview...........................................................................................................................97
10.2 PILOT CASE DEFINITION............................................................................................................................98
10.2.1 Stakeholder and user stories..................................................................................................100
10.2.2 Motivation and strategy ........................................................................................................101
10.3 PILOT MODELLING WITH ARCHIMATE .......................................................................................................102
10.3.1 Agriculture pilot B1.4 Motivation view ..................................................................................102
10.3.2 Agriculture pilot B1.4 Strategy view.......................................................................................103
10.4 PILOT EVALUATION PLAN .......................................................................................................................103
10.4.1 High level goals and KPI's.......................................................................................................103
10.4.2 Initial roadmap.......................................................................................................................103
10.5 BIG DATA ASSETS..................................................................................................................................104
PILOT 9 [B2.1] MACHINERY MANAGEMENT........................................................................................105
11.1 PILOT OVERVIEW ..................................................................................................................................105
11.1.1 Pilot introduction....................................................................................................................105
11.1.2 Pilot overview.........................................................................................................................105
11.2 PILOT CASE DEFINITION..........................................................................................................................107
11.3.1 Agriculture pilot B2.1 Motivation view.................................................................................111
11.3.2 Agriculture Pilot B2.1 Strategy view.......................................................................................112

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11.4.2 Initial roadmap.......................................................................................................................113
11.5 BIG DATA ASSETS..................................................................................................................................113
PILOT 10 [C1.1] INSURANCE (GREECE).................................................................................................114
12.1 PILOT OVERVIEW ..................................................................................................................................114
12.1.2 Pilot overview.........................................................................................................................114
12.3.1 Agriculture pilot C1.1 Motivation view ..................................................................................119
12.3.2 Agriculture C1.1 Strategy view...............................................................................................120
12.4.2 Initial roadmap.......................................................................................................................121
12.5 BIG DATA ASSETS..................................................................................................................................122
PILOT 11 [C1.2] FARM WEATHER INSURANCE ASSESSMENT ...............................................................123
13.1 PILOT OVERVIEW ..................................................................................................................................123
13.1.2 Pilot overview.........................................................................................................................123
13.3.2 Agriculture pilot C1.2 Strategy view.......................................................................................130
13.4.2 Initial roadmap.......................................................................................................................131
13.5 BIG DATA ASSETS..................................................................................................................................132
PILOT 12 [C2.1] CAP SUPPORT ............................................................................................................133
14.1 PILOT OVERVIEW ..................................................................................................................................133
14.1.2 Pilot overview.........................................................................................................................133
14.4.2 Initial roadmap.......................................................................................................................142
14.5 BIG DATA ASSETS..................................................................................................................................143
PILOT 13 [C.2.2] CAP SUPPORT (GREECE) ............................................................................................144
15.1.2 Pilot overview.........................................................................................................................144

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15.4.2 Initial roadmap.......................................................................................................................152
15.5 BIG DATA ASSETS..................................................................................................................................153
CONCLUSION ......................................................................................................................................154
REFERENCES .......................................................................................................................................155
Table of Figures
FIGURE 1: ARCHIMATE 3.0 MODELLING FRAMEWORK......................................................................................................23
FIGURE 2: RELATIONSHIPS OF THE MOTIVATION ELEMENTS................................................................................................26
FIGURE 3: RELATIONSHIPS OF THE STRATEGY ELEMENTS....................................................................................................26
FIGURE 4: AGRICULTURE PILOT A1.1 MOTIVATION VIEW ..................................................................................................33
FIGURE 5: AGRICULTURE PILOT A1.1 STRATEGY VIEW.......................................................................................................34
FIGURE 6: AGRICULTURE PILOT A1.1 INITIAL ROADMAP ....................................................................................................35
FIGURE 7: AGRICULTURE PILOT A1.1 BDVA REFERENCE MODEL.........................................................................................36
FIGURE 8: AGRICULTURE PILOT A1.2 MOTIVATION VIEW ..................................................................................................42
FIGURE 9: AGRICULTURE PILOT A1.2 STRATEGY VIEW.......................................................................................................42
FIGURE 10: AGRICULTURE PILOT A1.2 INITIAL ROADMAP ..................................................................................................43
FIGURE 11: AGRICULTURE PILOT A1.2 BDVA REFERENCE MODEL.......................................................................................44
FIGURE 12: AGRICULTURE PILOT A1.3 MOTIVATION VIEW ................................................................................................50
FIGURE 13: AGRICULTURE PILOT A1.3 STRATEGY VIEW.....................................................................................................51
FIGURE 15:AGRICULTURE PILOT A1.3 BDVA REFERENCE MODEL .......................................................................................53
FIGURE 16: AGRICULTURE PILOT A2.1 MOTIVATION VIEW ................................................................................................61
FIGURE 17: AGRICULTURE PILOT A2.1 STRATEGY VIEW.....................................................................................................62
FIGURE 19: AGRICULTURE PILOT A2.1 BDVA REFERENCE MODEL.......................................................................................64
FIGURE 20: AGRICULTURE PILOT B1.1 TRAGSA MOTIVATION VIEW ..................................................................................70
FIGURE 21: AGRICULTURE PILOT B1.1 STRATEGY VIEW.....................................................................................................71
FIGURE 22: AGRICULTURE PILOT B1.1 INITIAL ROADMAP ..................................................................................................72
FIGURE 23: AGRICULTURE PILOT B1.1 BDVA REFERENCE MODEL.......................................................................................73
FIGURE 24: AGRICULTURE PILOT B1.2 MOTIVATION VIEW ................................................................................................80
FIGURE 27: AGRICULTURE PILOT B1.2 BDVA REFERENCE MODEL.......................................................................................83
FIGURE 28: AGRICULTURE PILOT B1.3 MOTIVATION VIEW ................................................................................................92
FIGURE 31: AGRICULTURE PILOT B1.3 BDVA REFERENCE MODEL FOR IOT ...........................................................................95
FIGURE 32: AGRICULTURE PILOT B1.3 BDVA REFERENCE MODEL FOR SATELLITE DATA...........................................................96
FIGURE 33: AGRICULTURE PILOT B1.4 MOTIVATION VIEW ..............................................................................................102
FIGURE 34: AGRICULTURE PILOT B1.4 STRATEGY VIEW...................................................................................................103
FIGURE 35: AGRICULTURE PILOT B1.4 INITIAL ROADMAP ................................................................................................104

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FIGURE 36: AGRICULTURE PILOT B1.4 BDVA REFERENCE MODEL.....................................................................................104
FIGURE 37: ZETOR TRACTORS ....................................................................................................................................106
FIGURE 38: AGRICULTURE PILOT B2.1 MOTIVATION VIEW ..............................................................................................111
FIGURE 40: AGRICULTURE PILOT B2.1 INITIAL ROADMAP................................................................................................113
FIGURE 42: AGRICULTURE PILOT C1.1 MOTIVATION VIEW ..............................................................................................119
FIGURE 43: AGRICULTURE PILOT C1.1 STRATEGY VIEW...................................................................................................120
FIGURE 44: AGRICULTURE PILOT C1.1 INITIAL ROADMAP ................................................................................................121
FIGURE 45: AGRICULTURE PILOT C1.1 BDVA REFERENCE MODEL.....................................................................................122
FIGURE 53: AGRICULTURE PILOT C2.1 BVDA REFERENCE MODEL.....................................................................................143
FIGURE 55: AGRICULTURE PILOT C2.2 STRATEGY VIEW ...................................................................................................151
List of Tables
TABLE 1: THE DATABIO CONSORTIUM PARTNERS.............................................................................................................13
TABLE 2: OVERVIEW OF AGRICULTURE PILOT CASES ..........................................................................................................18
TABLE 3: ARCHIMATE MOTIVATION AND STRATEGY VIEWS................................................................................................23
TABLE 4: ELEMENTS USED IN THE ARCHIMATE MOTIVATION AND STRATEGY VIEWS................................................................24
TABLE 5: AGRICULTURE PILOT A1.1 OVERVIEW OF PILOT ACTIVITIES....................................................................................27
TABLE 6: SUMMARY OF PILOT A1.1 (ISO JTC1 WG9 USE CASE TEMPLATE) .........................................................................29
TABLE 7: AGRICULTURE PILOT A1.1 STAKEHOLDERS AND USER STORIES................................................................................32
TABLE 8: SUMMARY OF PILOT A1.2 (ISO JTC1 WG9 USE CASE TEMPLATE) .........................................................................38
TABLE 9: AGRICULTURE PILOT A1.2 STAKEHOLDERS AND USER STORIES................................................................................41
TABLE 10: SUMMARY OF PILOT A1.3 (ISO JTC1 WG9 USE CASE TEMPLATE) .......................................................................46
TABLE 11: AGRICULTURE PILOT A1.3 STAKEHOLDERS AND USER STORIES..............................................................................49
TABLE 12: SUMMARY OF PILOT A2.1 (ISO JTC1 WG9 USE CASE TEMPLATE) .......................................................................56
TABLE 13: AGRICULTURE PILOT A2.1 STAKEHOLDERS AND USER STORIES..............................................................................59
TABLE 14: SUMMARY OF PILOT B1.1 (ISO JTC1 WG9 USE CASE TEMPLATE) .......................................................................65
TABLE 15: AGRICULTURE PILOT B1.1 STAKEHOLDERS AND USER STORIES..............................................................................68
TABLE 16: AGRICULTURE PILOT B1.2 OVERVIEW OF PILOT ACTIVITIES ..................................................................................74
TABLE 19: AGRICULTURE PILOT B1.3 OVERVIEW OF PILOT ACTIVITIES..................................................................................85
TABLE 23: AGRICULTURE PILOT B1.4 STAKEHOLDERS AND USER STORIES............................................................................100
TABLE 24: SUMMARY OF PILOT B2.1 (ISO JTC1 WG9 USE CASE TEMPLATE) .....................................................................107
TABLE 25: AGRICULTURE PILOT B2.1 STAKEHOLDERS AND USER STORIES............................................................................110

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TABLE 26: AGRICULTURE PILOT C1.1 OVERVIEW OF PILOT ACTIVITIES ................................................................................114
TABLE 27: SUMMARY OF PILOT C1.1 (ISO JTC1 WG9 USE CASE TEMPLATE)......................................................................116
TABLE 28: AGRICULTURE PILOT C1.1 STAKEHOLDERS AND USER STORIES ............................................................................118

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Definitions, Acronyms and Abbreviations
Acronym/
Abbreviation
Title
BDVA Big Data Value Association
BDT Big Data Technology
CAP Common Agricultural Policy
CEN European Committee for Standardization
EO Earth Observation
ESA European Space Agency
EAGF European Agricultural Guarantee Fund
EU European Union
FAO Food and Agriculture Organisation of the United Nations
fAPAR fraction of Absorbed Photosynthetically Active Radiation
FAS Farm Advisory System
GAEC Good Agricultural and Environmental Conditions
GEOSS Group on Earth Observations
GPRS General Packet Radio Service
GS Genomic Selection
HPC High Performance Computing
IACS Integrated Administration and Control System
ICT Information and Communication Technologies
IoT Internet of Things
ISO International organization for Standardisation
KPI Key Performance Indicator
LPIS Land Parcel Identification System
NDVI Normalized Difference Vegetation Index
NGS Next-Generation Sequencing
NUTS Nomenclature of Territorial Units for Statistic
PC Personal Computer
PF Precision Farming
PU Public
RPAS Remotely Piloted Aircraft System
RTK Real Time Kinematic
SMEs Small and medium-sized enterprises
TRL Technology Readiness Level
UAV Unmanned Aerial Vehicle
UI User Interface
UVA, UVB (UV) ultraviolet rays, (A) long wave, (B) short wave
VRA Variable Rate Application
WP Work Package

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Term Definition
Big Data A term of data sets that are so large or complex that traditional data
processing application software is inadequate to dealing with them
In situ Latin phrase translated “on site” or “on position”- it means “locally” or “in
place” to describe an event where it takes place
NDVI A simple graphical indicator that can be used to analyse remote sensing
measurements
WP (Work
Package)
A building block of the work breakdown structure that allows the project
management to define the steps necessary for completion of the work

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Introduction
1.1 Project Summary
The data intensive target sector on which the
DataBio project focuses is the Data-Driven
Bioeconomy. DataBio focuses on utilizing Big
Data to contribute to the production of the
best possible raw materials from agriculture,
forestry and fishery (aquaculture) for the
bioeconomy industry, as well as their further
processing into food, energy and
biomaterials, while taking into account various accountability and sustainability issues.
DataBio will deploy state-of-the-art big data technologies and existing partners’ infrastructure
and solutions, linked together through the DataBio Platform. These will aggregate Big Data
from the three identified sectors (agriculture, forestry and fishery), intelligently process them
and allow the three sectors to selectively utilize numerous platform components, according
to their requirements. The execution will be through continuous cooperation of end user and
technology provider companies, bioeconomy and technology research institutes, and
stakeholders from the big data value PPP programme.
DataBio is driven by the development, use and evaluation of a large number of pilots in the
three identified sectors, where associated partners and additional stakeholders are also
involved. The selected pilot concepts will be transformed to pilot implementations utilizing
co-innovative methods and tools. The pilots select and utilize the best suitable market-ready
or almost market-ready ICT, Big Data and Earth Observation methods, technologies, tools and
services to be integrated to the common DataBio Platform.
Based on the pilot results and the new DataBio Platform, new solutions and new business
opportunities are expected to emerge. DataBio will organize a series of trainings and
hackathons to support its uptake and to enable developers outside the consortium to design
and develop new tools, services and applications based on and for the DataBio Platform.
The DataBio consortium is listed in Table 1. For more information about the project see [REF-
01].
Table 1: The DataBio consortium partners
Number Name Short name Country
1 (CO) INTRASOFT INTERNATIONAL SA INTRASOFT Belgium
2 LESPROJEKT SLUZBY SRO LESPRO Czech Republic
3 ZAPADOCESKA UNIVERZITA V PLZNI UWB Czech Republic

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4
FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER
ANGEWANDTEN FORSCHUNG E.V. Fraunhofer Germany
5 ATOS SPAIN SA ATOS Spain
6 STIFTELSEN SINTEF SINTEF ICT Norway
7 SPACEBEL SA SPACEBEL Belgium
8
VLAAMSE INSTELLING VOOR TECHNOLOGISCH
ONDERZOEK N.V. VITO Belgium
9
INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ
AKADEMII NAUK PSNC Poland
10 CIAOTECH Srl CiaoT Italy
11 EMPRESA DE TRANSFORMACION AGRARIA SA TRAGSA Spain
12 INSTITUT FUR ANGEWANDTE INFORMATIK (INFAI) EV INFAI Germany
13 NEUROPUBLIC AE PLIROFORIKIS & EPIKOINONION NP Greece
14
Ústav pro hospodářskou úpravu lesů Brandýs nad
Labem UHUL FMI Czech Republic
15 INNOVATION ENGINEERING SRL InnoE Italy
16 Teknologian tutkimuskeskus VTT Oy VTT Finland
17 SINTEF FISKERI OG HAVBRUK AS
SINTEF
Fishery Norway
18 SUOMEN METSAKESKUS-FINLANDS SKOGSCENTRAL METSAK Finland
19 IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD IBM Israel
20 MHG SYSTEMS OY - MHGS MHGS Finland
21 NB ADVIES BV NB Advies Netherlands
22
CONSIGLIO PER LA RICERCA IN AGRICOLTURA E
L'ANALISI DELL'ECONOMIA AGRARIA CREA Italy
23 FUNDACION AZTI - AZTI FUNDAZIOA AZTI Spain
24 KINGS BAY AS KingsBay Norway
25 EROS AS Eros Norway
26 ERVIK & SAEVIK AS ESAS Norway
27 LIEGRUPPEN FISKERI AS LiegFi Norway
28 E-GEOS SPA e-geos Italy

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29 DANMARKS TEKNISKE UNIVERSITET DTU Denmark
30 FEDERUNACOMA SRL UNIPERSONALE Federu Italy
31
CSEM CENTRE SUISSE D'ELECTRONIQUE ET DE
MICROTECHNIQUE SA - RECHERCHE ET
DEVELOPPEMENT CSEM Switzerland
32 UNIVERSITAET ST. GALLEN UStG Switzerland
33 NORGES SILDESALGSLAG SA Sildes Norway
34 EXUS SOFTWARE LTD EXUS
United
Kingdom
35 CYBERNETICA AS CYBER Estonia
36
GAIA EPICHEIREIN ANONYMI ETAIREIA PSIFIAKON
YPIRESION GAIA Greece
37 SOFTEAM Softeam France
38
FUNDACION CITOLIVA, CENTRO DE INNOVACION Y
TECNOLOGIA DEL OLIVAR Y DEL ACEITE CITOLIVA Spain
39 TERRASIGNA SRL TerraS Romania
40
ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS
ANAPTYXIS CERTH Greece
41
METEOROLOGICAL AND ENVIRONMENTAL EARTH
OBSERVATION SRL MEEO Italy
42 ECHEBASTAR FLEET SOCIEDAD LIMITADA ECHEBF Spain
43 NOVAMONT SPA Novam Italy
44 SENOP OY Senop Finland
45
UNIVERSIDAD DEL PAIS VASCO/ EUSKAL HERRIKO
UNIBERTSITATEA EHU/UPV Spain
46
OPEN GEOSPATIAL CONSORTIUM (EUROPE) LIMITED
LBG OGCE
United
Kingdom
47 ZETOR TRACTORS AS ZETOR Czech Republic
48
COOPERATIVA AGRICOLA CESENATE SOCIETA
COOPERATIVA AGRICOLA CAC Italy

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1.2 Document Scope
Deliverable D1.1 – Agriculture Pilot Definition (due M06) specifies the pilot case descriptions,
requirement specifications, and implementation and evaluation plans. The document
describes 13 pilots and it will serve as basis for implementation of agriculture pilots, which
will be described in Agriculture Pilots intermediate report - Pilot results and feedback from
users in Month 24.
1.3 Document Structure
This document is comprised of the following chapters:
Chapter 1 presents an introduction to the project and the document.
Chapter 2 gives a general overview of the Agriculture Pilots t and summarises key points of
the pilot cases.
Chapters 3 to 17 describe the individual pilot cases.

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Summary
2.1 Overview
The agriculture sector is of strategic importance for the European society and economy. Due
to its complexity, agri-food operators have to manage many different and heterogeneous
sources of information. Agriculture is facing many economic challenges in terms of
productivity or cost-effectiveness, as well as an increasing labour shortage partly due to
depopulation of rural areas. Current systems still have significant drawbacks in areas such as
flexibility, efficiency, robustness, sustainability, high operator cost and capital investment.
Furthermore, reliable detection, accurate identification and proper quantification of
pathogens and other factors, affecting plant health, common agriculture policy, insurance,
are critical to be kept under control so as to reduce economy expenditures, trade disruptions
and even human health risks. Agriculture requires collection, storage, sharing and analysis of
large quantities of spatially and non-spatially referenced data. These data flows currently
hinder the adoption of precision agriculture as the multitude of data models, formats,
interfaces and reference systems in use result in incompatibilities. In order to plan and make
economically and environmentally sound decisions a combination and management of
information is needed.
2.2 Pilot introductions
Big data technology (BDT) is a new technological paradigm that is driving the entire economy,
including low-tech industries such as agriculture where it is implemented under the banner
of precision farming (PF) [REF-03]. BDT in agriculture builds on geo-coded maps of agricultural
fields and the real-time monitoring of activities on the farm in order to increase the efficiency
of resource use, reduce the uncertainty of management decisions [REF-04]. Under PF, yield is
increased due particularly to the precise selection and application of exact types and doses of
agricultural inputs (crop varieties, fertilizers, pesticides, herbicides, irrigation water) for
optimum crop growth and development.
In terms of technology readiness level (TRL), the agriculture pilots are mostly positioned at
the sixth and seventh TRL. Improved technologies such as new elite varieties were developed,
big data such as weather, soil, crop (phenotypic data), and other environmental data are
routinely collected and meta-analysed, and technological and managerial services are already
offered to farmers in a few nations for a number of crops, although not in a scale that would
enable the application of big data analytics. There also exist experiences with farm telemetry
or utilization of satellite data (Earth Observation) in some countries. In addition, the required
skills are available in the organizations participating in the pilots, and the organizations are
ready to change their internal and external business processes, which is a key factor for
adopting the new technology.
The European farming system represents a mixture of small and big farms [REF-05]. In order
for WP1 pilots to account for both small and bigger farms, agriculture data serving as an input
into the big data analytics system will be gathered on a finer and a larger scale. The finer scale

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is tailored to both farm sizes but with a particular focus on bigger farms with more financial
resources. Finer scale data include (1) data collected manually on soil, plants and other
agriculturally relevant factors, and through surveys and interviews; (2) historic big agriculture
and meteorological datasets; and (3) field-bound wireless sensor networks. Larger scale data
will be mainly derived from earth observation (EO) and include agriculturally relevant
information collected using remote sensing technologies and earth surveying techniques, and
from data coming from agriculture machinery. EO and finer scale information will be used
through big data analytics (WP4) to monitor and assess the status of, and changes in, the
agriculture pilots implemented in this project all across the European Union. Big data analytics
components and tools will then provide pilot managers with highly localized descriptive
(better and more advanced way of analysing an operation), prescriptive (timely
recommendations for operation improvement i.e., seed, fertilizer and other agricultural
inputs application rates, soil analysis, and localized weather and disease/pest reports, based
on real-time and historical data), and predictive plans (use current and historical data sets to
forecast future localized events and returns).
2.3 Overview of pilot cases
The agriculture pilot cases are divided into three main topics as shown in the table below. For
all the pilots, co-innovative requirements (Task 1.1) were defined within the first six months
(M1-M6) of the project. Pilots activities under real production environment conditions will be
run over two to three cropping seasons (M6-M34) depending upon the plant species of
interest. (Tasks 1.2, 1.3, 1.4)
Table 2: Overview of agriculture pilot cases
Task (topic) Subtask Pilot group Pilot
T1.2 (A) Precision
Horticulture including
vine and olives
T1.2.1 A1: Precision agriculture
in olives, fruits, grapes
and vegetables
A1.1: Precision
agriculture in olives,
fruits, grapes
A1.2: Precision
agriculture in vegetable
seed crops
A1.3: Precision
agriculture in vegetables
-2 (Potatoes)

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T1.2.2 A2: Big Data management
in greenhouse eco-
systems
A2.1: Big Data
management in
greenhouse eco-
systems
T1.3 (B) Arable
Precision Farming
T1.3.1 B1: Cereals and biomass
crops
B1.1: Cereals and
biomass crops
B1.2: Cereals and
biomass crops 2
B1.3: Cereals and
biomass crops 3
B1.4: Cereals and
biomass crops 4
T1.3.2 B2: Machinery
management
B2.1: Machinery
management
T1.4 (C) Subsidies and
insurance
T1.4.1 C1: Insurance C1.1: Insurance (Greece)
C1.2: Farm Weather
Insurance Assessment
T1.4.2 C2: CAP support C2.1: CAP Support
C2.2: CAP Support
(Greece)
The topics are defined as follows:
A. Precision Horticulture including vine and olives led by NP: In our days, farmers face a
series of challenges in their business. Resistant crop diseases and climate change
affects their crop production. At the same time, as the global demand for commodities
increases, farmers are forced to maximize their production. Following the rules of the
modern agro-food market, farmers and cooperatives that wish to export their
products abroad, need to follow smart agriculture practices.

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B. Arable Precision Farming led by Vito: The overall objective is to implement big data
technology tools for precision and resilient farming of the food crop species of interest
including durum wheat, corn, grapes, etc. Focus of this pilot will be not only on
production aspects, but also on protection of water and soil as well as on energy
saving.
C. CAP support and insurance lead institution led by e-GEOS: The focus will be on using
Earth Observation data for the purpose of insurance and EU Common Agricultural
Policy.
Each topic includes two pilot groups:
Pilot group A1: Precision agriculture in olives, fruits, grapes and vegetables (NEUROPUBLIC,
VITO, GAIA, InfAI and CAC)
The following services will be offered:
• Remote plant disease diagnosis and assessment based on the processing of Satellite
images;
• weather condition alert system which will result in the decision taking of specific
actions; (e.g., crop protection);
• provision of automated irrigation systems based in precision irrigation enabling in this
way an efficient water resource management system;
• support of efficient soil fertilization and spray practices consistent with the specific
needs of the farm and the protection of the environment;
• advisory services regarding crop diversification will be also provided to the farmers
directing them in more productive and resilient cultivations.
It will be focused on combined use of soil data, weather data, map data, satellite (LR, HR, VHR,
SAR), farm logs, UAV, farm profile data, and data collected by mobile audio-visual devices.
Pilot group A2: Big Data management in greenhouse eco-systems (CERTH, CREA)
The overall objective of the proposed pilot is to provide knowledge, know‐how & tools related
to the information flow, management and data analytics in greenhouse horticulture. To this
purpose, genomics, metabolomics and phenomics data will be combined. During this project,
it will be used already produced genomic data which will be integrated with new ones in order
to assess the genetic potential of new tomato varieties and their performance in greenhouses.
The aim is to integrate metabolomics and genomics data to obtain a complete identity of the
varieties for breeding applications. Liquid chromatography - mass spectrometry (LC-MS), Gas
chromatography - mass spectrometry (GS-MS), High-performance liquid chromatography
(HPLC) will be used to collect the metabolomics data. Market potential and industry interests:
Tomato is among the top cultivated crops in greenhouses, with billions of euros turnover
worldwide. Tomato is considered one of the most nutritive solanum vegetables due to its high
content in sugars, vitamins and antioxidants and its consumption is steadily increasing. The
pilot is expected to leverage the productivity and the quality of tomato.

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Pilot group B1: Cereals and biomass crops (Vito, Lesprojekt, NEUROPUBLIC, Federunacoma,
CREA, NOVAMONT, ZETOR, GAIA, CERTH, NB Advies, CiaoT, ASTER, InfAI, Lesprojekt,
Federunacoma, e-GEOS, PSNC, TRAGSA)
This pilot aims to provide information for precision agriculture, mainly based on time series
of high resolution (Sentinel-2 type) satellite images, complemented with UAV images, metro
and field (sensor) data. The information can be used as input for farm management
(operational decisions, tactical decisions). Information layers may include: - Vegetation
indices (NDVI, fAPAR, …) and derived anomaly maps. Anomaly maps can be used to set
priorities for field visits (local/regional level). Pilots on durum wheat will be conducted in
different environments in Italy in collaboration with Horta Srl, (private company), CNR-Ibimet
(public research institute) and local Producer organization and cooperatives in Italy using in
addition to the tools listed above. Pilots on precision irrigation in corn will be conducted in a
NEUROPUBLIC pilot site in Kalampaki area, in Drama Greece. The pilots will run in partnership
with end users GAIA EPICHEIREIN and the local Agricultural Association, representing the local
corn producers. Biomass crops (CREA, VITO, CERTH, NB Advies, CiaoT, ASTER, InfAI,
Lesprojekt, Federunacoma, e-GEOS, PSNC, TRAGSA). Biomass crops including biomass
sorghum, fiber hemp and milk thistle can be used for several purposes including, respectively,
biofuel, fiber, and biochemicals, with a high macroeconomic impact. The pilots on these crops
will be run in collaboration between CREA and private companies (end-users) Cooperativa
Agricola Cesenate (seed company), Novamont (Bio-based company), and Centro Ricerche
Produzioni Animali, and another 15 agricultural firms distributed across the Italian territory.
Pilot group B2: Machinery management (Lesprojekt, Federunacoma, ZETOR)
From technical point of view the monitoring system involves tracking of the vehicles’ position
using GPS combined with acquisition of information from on-board terminal (CAN-BUS) and
their online or offline transfer to GIS environment. Such systems collect large amounts of
data. The monitoring system will be done in large, medium-sized and small farms based on
the level of information processing and their interaction with other farm data, three use cases
will be handled.
Pilot group C1: Insurance (e-GEOS, VITO, NEUROPUBLIC, NB Advies, CSEM)
The objective of this pilot is the provision and assessment on a test area of services for
agriculture insurance market, based on the usage of Copernicus satellite data series also
integrated with meteorological data, and other ground available data.
Pilot group C2: CAP support (e-GEOS, CSEM, NEUROPUBLIC, GAIA)
The objective of the pilot is the provision of products and services, based on specialized highly
automated processors processing big data, in support to the CAP and relying on multi-
temporal series of free and open EO data, with focus on Copernicus Sentinel 2 data. Products
and services will be tuned in order to fulfil requirements from the 2015-20 EU CAP policy, and
will be general information layers and indicators on EU territory with different level of

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aggregation and detail up to farm level. The proposed pilot project has been tailored on the
specific needs of three end users, one operating at National level (Romania Agriculture
Ministry), one operating at Regional level (AVEPA Paying Agency) in one of the most
important agricultural regions in Italy, and one operating in Greece.
2.4 Agriculture datasets utilized in pilots
The datasets used by the agriculture pilots can be coarsely divided into four distinct
categories. In situ measurements are data obtained by sensors in the field, Machinery
Measurements are coming from sensors in agriculture machinery. Remote measurements are
measurements which may cover a greater geographical area, such as measurements from
satellites. VGI data and data collected by farmers. The biggest data sets will come from Earth
Observation and Machine monitoring. The current experience from Czech Republic
demonstrate that machinery monitoring in Czech Republic is yearly able to generate more
than 20 TB of data and the needs of satellite data is approximately 5 TB per year. The data
from unmanned aerial vehicles (UAV) will be much larger.
2.5 Representation of pilot cases
Each pilot is described in following structure:
● PILOT OVERVIEW
o Pilot introduction
o Pilot overview
● PILOT CASE DEFINITION
o Stakeholder and user stories
o Motivation and strategy
● PILOT MODELLING WITH ARCHIMATE
o Motivation view
o Strategy view
● PILOT EVALUATION PLAN
o High level goals and KPI's
o Initial roadmap
● BIG DATA ASSETS
2.6 Pilot modelling framework
The pilot cases are modelled using the ArchiMate 3.0 modelling framework. Figure 1
summarizes the overall ArchiMate 3.0 framework. The figure also depicts the input provided
by the domain WPs (WP1, WP2, WP3 and their pilots) and that provided by the technology
WPs (WP4, WP5), which will be correlated in the next stages of modelling process.

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Figure 1: ArchiMate 3.0 modelling framework.
The modelling presented in this deliverable focuses on the “Motivation” and “Strategy” views.
The “Motivation” view models the reasons that guide the design of the architecture. The
“Strategy” view adds how the course of action is realized. Table 3 provides an extended
description of the two views. After the completion of this deliverable, the plan is to extend
the modelling with other views, while investigating the correlations with the technology WP
input.
Table 3: ArchiMate Motivation and Strategy views.
View name Description
Motivation
view
Motivation elements are used to model the motivations, or reasons, that guide the
design or change of an Enterprise Architecture. It is essential to understand the
factors, often referred to as drivers, which influence other motivation elements.
They can originate from either inside or outside the enterprise. Internal drivers, also
called concerns, are associated with stakeholders, which can be some individual
human being or some group of human beings, such as a project team, enterprise, or
society. Examples of such internal drivers are customer satisfaction, compliance to
legislation, or profitability.

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Strategy
view
The immediate decision support system is built on top of a data collection and
distribution system. The data collection and distribution system is used to collect
sensor data from the on-board systems and makes them available in a single system.
The data distribution system ensures that the decision support system only interface
with a single system, instead of multiple sensors. The decision support system
presents the data from the data distribution system and collect them in an internal
storage system for presentation of current performance vs. historic performance.
The main elements used in the above views are explained in Table 4. Their relationships are
shown in Figure 2and Figure 3. For further information see [REF-02].
Table 4: Elements used in the ArchiMate Motivation and Strategy views
Element Definition Notation
Stakeholder The role of an individual, team,
or organization (or classes
thereof) that represents their
interests in the outcome of the
architecture.
Driver An external or internal condition
that motivates an organization
to define its goals and
implement the changes
necessary to achieve them.
Assessment The result of an analysis of the
state of affairs of the enterprise
with respect to some driver.
Goal A high-level statement of intent,
direction, or desired end state
for an organization and its
stakeholders.
Outcome An end result that has been
achieved.

Page 25
Principle A qualitative statement of intent
that should be met by the
architecture.
Requirement A statement of need that must
be met by the architecture.
Constraint A factor that prevents or
obstructs the realization of
goals.
Meaning The knowledge or expertise
present in, or the interpretation
given to, a core element in a
particular context.
Value The relative worth, utility, or
importance of a core element or
an outcome.
Resource An asset owned or controlled by
an individual or organization.
Capability An ability that an active
structure element, such as an
organization, person, or system,
possesses.
Course of
action
An approach or plan for
configuring some capabilities
and resources of the enterprise,
undertaken to achieve a goal.

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Figure 2: Relationships of the Motivation elements
Figure 3: Relationships of the Strategy elements

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Pilot 1 [A1.1] Precision agriculture in olives,
fruits, grapes
3.1 Pilot overview
3.1.1 Pilot introduction
The world population is expected to reach 9 billion by 2050 and feeding that population will
require a 70 percent increase in food production (FAO 2009) [REF-06]. At the same time,
farmers are facing a series of challenges in their businesses that affect their farm production,
such as crop pests and diseases with increased resistance along with drastic changes due to
the effects of the climate change. These factors lead to rising food prices that have pushed
over 40 million people into poverty since 2010, a fact that highlights the need for more
effective interventions in agriculture (World Bank 2011) [REF-07]. In this context, agri-food
researchers are working on approaches that aim at maximizing agricultural production and
reducing yield risk. The benefits of the ICT-based revolution have already significantly
improved agricultural productivity; however, there is a demonstrable need for a new
revolution that will contribute to “smart” farming and help addressing all the aforementioned
problems (World Bank 2011) [REF-07].
There is a need for services that are powered by scientific knowledge, driven by facts and
offer inexpensive yet valuable advice to farmers. In this context, smart farming is expected to
reduce production costs, increase production (quantitatively) and improve its quality, protect
the environment and minimize farmers’ risks.
3.1.2 Pilot overview
The main focus of this pilot is to offer smart farming services dedicated for olives, fruits and
grapes, based on a set of complementary monitoring technologies. Smart farming services
comprise irrigation, fertilization and pest/disease management advice provided through
flexible mechanisms and UIs (web, mobile, tablet compatible). The pilot will target towards
promoting the adoption of technological advances (IoT, Big Data analytics, EO data) and
collaborating with certified professionals to optimize farm management procedures. NP and
GAIA Epicheirein will support the activities for the execution of the full life-cycle of the pilot.
The following table provides an overview of the pilot activities.
Table 5: Agriculture pilot A1.1 Overview of pilot activities
Pilot Site A Pilot Site B Pilot Site C
Location Chalkidiki, Greece Stimagka, Greece Veria, Greece
Area Size 600ha 3,000ha 10,000ha
Targeted Crops Olive Trees Grapes Peaches

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End-Users Single farmer,
Agronomists
Farming
organization,
Agronomists
Farming
cooperative,
Agronomists
The underlying reason for the selection of these particular crop types is the significant
economic impact that they share in the Greek farming landscape. Olive tree cultivation
accounts for nearly 2 billion euros in annual net income, while peach and grape cultivations
reach close to 460 million and 390 million annual net income respectively.
Method
This pilot is targeting towards providing a set of smart farming services to the farmer utilizing
available precision agriculture techniques. The services will be provided as advices, which
need many prerequisites and primary material in order to be accurate. Data is the raw
material and there are three different means of collecting data, which will be exploited within
the pilot activities. Data directly from the field, collected from a network of telemetric IoT
stations called GAIAtrons; remotely with image sensors on in-orbit platforms; and by
monitoring the application of inputs and outputs in the farm (e.g. in-situ measurements, farm
logs, farm profile). Every data source has unique characteristics with relevant impact on the
very content of this data. Field sensing provides real-time accurate direct measures of many
physical parameters of the soil (soil temperature, humidity), atmosphere microclimate of the
field crop and plant (ambient temperature, humidity, barometric pressure, solar radiation,
leaf wetness, rainfall volume, wind speed and direction) with temporal continuity. Remote
sensing provides indirect measures of some physical properties of plants and soil with spatial
continuity in medium to large spatial scale. Combining this information can provide a good
knowledge of the most important physical parameters of soil, microclimate, plants and water
(which are all the environmental resources, which govern farming) in both spatial and
temporal dimensions. Monitoring the application of inputs and outputs on the farm is a data
element that is necessary to assess the correctness of the given advice and use it as feedback
to improve the system over time. This pilot will combine advanced data handling techniques
(i.e. assimilation, fusion and spatio-temporal interpolation) to transform the collected data
into actionable advice. In order for this advice to reflect the actual situation at a given field,
we will deploy scientific models and we will seek to incorporate the human experience of the
farmer or certified advisors.
Relevance to and availability of Big Data and Big Data infrastructure
NP has already started collecting field-sensing data through its network of telemetric IoT
stations, called GAIAtrons. GAIAtrons offer configurable data collection and transmission
rates. Since 01/03/2016 over 1M samples have been collected and stored to NP’s cloud
infrastructure that refer to atmospheric and soil measurements from various agricultural
areas of Greece. Moreover, within the same cloud infrastructure (GAIA cloud), remote sensing
data from the new Sentinel 2 optical products are being extracted and stored since the
beginning of 2016. This comprises both raw and processed (corrected products, extracted

Page 29
indices) data represented in raster formats that are being handled and distributed using
optimal big data management methodologies. Finally, through flexible work calendars, NP
has collected more than 120000 records related to work plans of the farmers that can be used
in the context of the pilot activities.
Benefit of pilot
The pilot is expected to have a direct impact on farm profitability in three (3) major crop types
of Greece, from an economic perspective. This will ensure that the proposed solutions can be
replicated to other crop types and market segments in the near future. The holistic approach
that is being proposed will significantly improve the capacity of the responsible partners in
providing smart farming advisory services. In addition, it would lead to improvements in a)
NP’s GAIA cloud’s stability, availability, security, interoperability and overall maturity, b) NP’s
GAIABus DataSmart functionality in terms of real-time analytics, data stream and decision
support processes, multi-temporal object-based monitoring, cloud-based services that
integrate earth observation with image processing, machine learning and spatial modelling,
c) advancing the current system by fusing telemetry IoT stations’ data with remote sensing
data and incorporating advanced visualization and event-based capabilities.
3.2 Pilot case definition
Table 6: Summary of pilot A1.1 (ISO JTC1 WG9 use case template)
Use case title Precision agriculture in olives, fruits, grapes
Vertical (area) Agriculture
Author/company/email NP, GAIA Epicheirein
Actors/stakeholders and
their roles and
responsibilities
● Single Farmer/Farming Organization or Cooperative,
responsible for performing farming activities
● Agronomists, involved in providing relevant and up-to-
date advices to the farmers
Goals Provide smart farming advisory services (focusing on irrigation,
fertilization and pest/disease management), based on a set of
complementary monitoring technologies, in order to increase
farm profitability and promote sustainable farming practises.
Use case description Refer to the pilot case definition section and diagrams in the pilot
modelling sections.
Current
solutions
Compute(System) System is based on IoT data, farm logs,
work calendars and in-situ
measurements. Expert knowledge is
provided through static scientific

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models that offer insight about
optimal farm management.
Storage All available data are stored in a cloud
infrastructure.
Networking Web-based UIs and dashboards
available for monitoring farm
activities.
Software Real-time analytics, data stream
processes and decision support
system
Big data
characteristics
Data source
(distributed/centralized)
Centralized (within GAIA Cloud): Field
sensing data from GAIAtrons, Remote
sensing (Earth observation) data,
Farm data
Volume (size) ● ~5.5 TB/year for remote
sensing data, including raw
data and extracted biophysical
and vegetation indices for the
pilot areas
● several GBs/year field sensing
data collected by the
deployed GAIAtrons (related
to the number of GAIAtrons to
be used within the pilot
activities)
● Hundreds of thousands of
records related to farm
activities/profiles/measureme
nts
Velocity
(e.g. real time)
Configurable data transmission for
field sensing (a new set of
measurements is being sent every 10
minutes in present configuration).
Every 10 days new EO products
available. Within 2018 EO products
will be available every 5 days.
Variety
(multiple datasets,
mashup)
Field Sensing: Soil temperature,
humidity (multi-depth), ambient
temperature, humidity, barometric
pressure, solar radiation, leaf wetness,

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rainfall volume, wind speed and
direction
Remote Sensing: 13 spectral bands
Variability (rate of
change)
Same as above, rate of change
depends very much on data
source/type.
Big data science
(collection, curation,
analysis,
action)
Veracity (Robustness
Issues, semantics)
Need for a system that can constantly
provide relevant and up-to-date
advices to its end-users
Visualization Spatio-temporal information
visualization for improving farm
management and facilitating the
decision-making process
Data quality (syntax) The quality of field sensing data is
controlled by several filtering, outlier
detection and stream processing
mechanisms. The integrity of remote
sensing data quality is being assessed
by a hash check upon product
download.
Data types Remote sensing data provided in
raster format (.jp2). Field sensing data
provided as time series unstructured
data with configurable frequency
Data analytics Descriptive and prescriptive analytics
for the provision of irrigation,
fertilization and pest management
advices.
Big data specific
challenges (Gaps)
There is a need for smarter fusion of the heterogeneous data
types that are being collected towards providing accurate insights.
To this end, it is important to explore mechanisms that could
combine raster and vector data at parcel level (polygon) and
station level (point).
Big data specific
challenges in bio-
economy
In order to facilitate the adoption of the big data technologies by
the farmers, imposed barriers in data visualization should be
encountered (e.g. give more emphasis to vector data,
improvement of the aggregation mechanism (drill down, zoom in,
roll up, zoom out)).

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Security and privacy
technical considerations
A system intended to collect data from field sensors, installed in
remote locations, is definitely going to face network connectivity
challenges. In order to provide up-to-date and relevant advices,
the system should be able to exhibit high availability and accuracy
in its sensor readings and transmission mechanisms. Moreover,
field sensing data should be securely transmitted to the cloud
infrastructure and protected against various types of attacks that
might set the system at risk.
Highlight issues for
generalizing this Use
case (e.g. for ref.
architecture)
EO data management mechanisms can be exploited for other use
cases where EO data might provide valuable insights.
3.2.1 Stakeholder and user stories
Table 7: Agriculture pilot A1.1 Stakeholders and user stories
Stakeholders User story Motivation
Farmer As a farmer I want to reduce costs and
improve farm productivity
Increase my profits following
sustainable agriculture practices
Agronomists As an agronomist I want to have a
comparative advantage in a highly
competitive market and to offer the
best possible services to my clients
Increase my profits by providing
better advices based on evidences,
well-established arguments and
scientific knowledge.
3.2.2 Motivation and strategy
The main motivation for this pilot is:
• to raise the awareness of the farmers, agronomists, agricultural advisors, farmer
cooperatives and organizations (e.g. group of producers) on how new technological
tools could optimize farm profitability and offer a significant advantage on a highly
competitive sector.
• to promote sustainable farming practises over a better control and management of
the resources (water, fertilizers, etc.).
• to increase the technological capacity of the involved partners through a set of pilot
activities that involves management of big data for high value crops.
The pilot motivation and strategy is summarized using ArchiMate diagrams in the next
section, while goals and KPIs are addressed in the successive evaluation plan.

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3.3 Pilot modelling with ArchiMate
The current section presents the "Agriculture A.1.1 modelling with ArchiMate" view point
described using the ArchiMate standard.
3.3.1 Agriculture pilot A1.1 Motivation view
This section provides the "Agriculture A1.1 Motivation view" view defined in the "Agriculture
A.1.1 modelling with ArchiMate" view point.
Figure 4: Agriculture pilot A1.1 Motivation view
Farmers want cost reduction and improved productivity in order to increase their profits
following sustainable agriculture practices.
3.3.2 Agriculture pilot A1.1 Strategy view
This section provides the "Agriculture A1.1 Strategy view" view defined in the "Agriculture
A.1.1 modelling with ArchiMate" view point.

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Figure 5: Agriculture pilot A1.1 Strategy view
The main focus of this pilot is to offer smart farming services dedicated for olives, fruits and
grapes, based on a set of complementary monitoring technologies. Smart farming services
comprise irrigation, fertilization and pest/disease management advice provided through
flexible mechanisms and UIs (web, mobile, tablet compatible). The pilot will target towards
promoting the adoption of technological tools (IoT, Big Data analytics, EO data) and
collaborating with certified professionals to boost/optimize farm productivity.
3.4 Pilot Evaluation Plan
3.4.1 High level goals and KPI's
Two relevant KPIs have been identified so far, namely:
• %Reduction potential in operational costs for performing the same farming activities
(through better management of resources) following the advisory irrigation,
fertilization, pest/disease management services vs what would be the operational
costs following standard farming practices based on historical data: Quantify
%reduction potential in operational costs for all three crop types (in fresh
water/fertilizer usage, sprays following the aforementioned advisory services).

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• %Increase in farm yield following the advisory irrigation, fertilization, pest/ disease
management services vs what would be the yield following standard farming practices
based on historical data: Quantify %increase in farm yield for all three crop types.
3.4.2 Initial roadmap
A coarse roadmap with important milestones for the pilot is included below. It has been
adapted to the two scheduled iterations of the DataBio platform and depends on these
internal project deliveries from work package 4 (WP4).
Figure 6: Agriculture pilot A1.1 initial roadmap
3.5 Big data assets
The diagram below summarizes Big Data technology components used in this pilot using the
extended BDVA reference model. Where applicable, specific partner components have been
indicated in the list using the component ids (DataBio project specific) that are likely to be
used, or evaluated for use, by this pilot.

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Figure 7: Agriculture pilot A1.1 BDVA reference model

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Pilot 2 [A1.2] Precision agriculture in
vegetable seed crops
4.1 Pilot overview
4.1.1 Pilot introduction
Eastern Italy is by tradition one of the areas in the world where seed production is at its best.
Seed Companies from all over the world produce on contract with local growers’ vegetables,
sugar beets, alfa-alfa and many other species.
One of the key factor for the achievement of seeds of good quality depends on the choice of
the right time of harvesting: if too early the vigour of the seed harvested will be affected; if
too late the mature seeds are going to drop to the ground and the best part of harvest get
lost.
The pilot will concentrate its main focus in monitoring the maturity of seed crops of different
species with satellite imagery. There will be an on-land observation of the crop development
which will be matched with satellite images in order to check the possibility to establish a
correspondence between images and the maturity stage of each crop.
In first growing season, the crop monitored will be sugar beet for seed production, with the
aim to expand the observation to other seed crops.
4.1.2 Pilot overview
Location: 5 farms, Region Emilia Romagna, for the total acreage of 14,79 hectares in the first
year.
To be expanded to other crops in the same Region and in Region Marche.
Method
This pilot will use satellite imagery (Sentinel-2) and telemetry IoT for crop monitoring and
yield/seed maturity estimation. The pilots will be run by C.A.C. in collaboration with VITO.
The crop involved in first year is sugar beet; according to the results achieved the model may
be expanded to other seed crops, namely cabbage and onion. VITO will use satellite data to
monitor the crops and will develop yield/seed maturity models. Telemetry IoT technology
will be implemented by C.A.C. on 5 farms located in Emilia Romagna and Marche.
Specifically, as part of pilot innovative solution, an online platform will be used to provide
satellite imagery, weather and soil data and yield/seed maturity predictions. VITO, in
collaboration with a number of Belgian partners, has developed a web application
“WatchITgrow®” for potato monitoring and yield prediction in Belgium. The existing
WatchITgrow® application will “filled” with satellite, weather and soil data for the Italian pilot
sites. To be able to provide maturity estimates developments are needed and it is necessary
to collect field data. The data will be collected by C.A.C.

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The farmer and pilot owners can use the satellite imagery (biomass index, 10m resolution) to
monitor and benchmark the maturity curve of seed crops till harvesting in correlation with
weather and microclimatic conditions recorded on site through dedicated meteorological
units.
A weather station will be installed in the vicinity of each field with sensors for air moisture
and temperature, soil temperature, rainfall – remote monitored.
Telemetry IoT stations will transmit data to the cloud infrastructure in the process of crop
monitoring, biotic and abiotic stress diagnostic, alert and operational recommendations.
Benefit of pilot
The solution that will be developed will be for the benefit of the co-operative which is
organising the production with its associated growers.
Each crop gets in maturity stage according to the cycle of the variety, microclimate, land
conditions, water supply etc.
The aim is to monitor the stage of maturity of each crop using satellite imagery (and possibly
telemetry IoT). This information can help fieldsmen to organise efficiently their time in
assisting the growers.
The fieldsman and the farmers who are participating in the pilot will have access to satellite
images, weather and soil data and information on seed maturity via an online platform. The
farmers will provide crop data about their fields for system learning.
4.2 Pilot case definition
Table 8: Summary of pilot A1.2 (ISO JTC1 WG9 use case template)
Use case title Precision agriculture in seed crops
Vertical (area) Agriculture
Author/company/email Stefano Balestri / C.A.C. / balestriacseeds.it
Isabelle Piccard / VITO
Actors/stakeholders
and their roles and
responsibilities
Fieldsmen, Growers and their co-operatives
Goals To produce a modelling in order to predict the maturity of seed
crops in order to organize harvest in the most efficient way and
get mature, high quality seeds
Use case description Refer to the pilot case definition section and diagrams in the pilot
modelling sections.

Page 39
Current
solutions
Compute(System) On spot decisions made on the
empiric experience of fieldsmen
Storage Local system + Company information
system
Networking “Crop report” application on web,
chat groups on Wats’app
Software Mobile application
Big data
characteristics
Data source
(distributed/centralized)
Availability of Sentinel-2 data (derived
vegetation indices).
Scientific modelling – built phenology
model.
Visualization – Processed data and
model results are published in an
intuitive way.
Volume (size) Hundreds of terabytes per year when
all sources of data are considered.
Velocity
(e.g. real time)
Satellite data: Sentinel-2A+B images
are acquired with a time step of 5
days. The images are pre-processed
and distributed by ESA within 24 hours
after acquisition. Further processing
by VITO starts as soon as the images
are available from ESA. Generally, the
final information products become
available for the end-users between
24 and 48 hours after image
acquisition.
Telemetry IoT data: Time step for data
collection is customizable, 1-60
minutes; big data: air temperature, air
moisture, rainfall, soil temperature.
Phenotypic data are collected each
cropping season.
Variety
(multiple datasets,
mashup)
Great variety. (1) Satellite: imagery,
multispectral data, indices (soil,
water, vegetation, biophysical), (2)
Telemetry IOT: air temperature, air
moisture, rainfall, soil temperature.
(3) analytics and phenotypic data.
Variability (rate of
change)
Same as above, rate of change
depends very much on data
source/type.

Page 40
Big data science
(collection, curation,
analysis,
action)
Veracity (Robustness
Issues, semantics)
Need to have tools to produce and
process ground-truth data for satellite
data calibration.
Visualization Visualization of crop monitoring
output at least bi-weekly during the
cropping season, indices and
predictions; real-time monitoring
output, alerts, and recommendations.
Data quality (syntax) Data validity filtering w.r.t.
completeness. Data fusion and
modelling of heterogeneous data (EO
data, telemetry IoT data, field data)
Data types Imagery, graphics, vector, numbers,
analytical results, measurements,
metadata, geolocations, spectra, time
series.
Data analytics Predictive analytics for the
development of data-driven yield
models; predictive feedback
(monitoring), real-time streaming
data analytics to alert and provide
operational recommendations using
cloud-based crop management
analytics including web portal cloud
solution.
Big data specific
challenges (Gaps)
There is a need for: (1) improving analytic and modelling systems
that provide reliable and robust statistical estimated using large
size of heterogeneous data; (2) reduced uncertainty of
management decisions.
Big data specific
challenges in
bioeconomy
Delivering content and services to various computing platforms
from Windows desktops to Android and iOS mobile devices
Security and privacy
technical considerations
Farm owner and geolocalization are highly sensitive, should be
anonymized
Highlight issues for
generalizing this Use
case (e.g. for ref.
architecture)
Real-time streaming data analytics and predictive analytics using
machine learning for crop monitoring and developing yield
models based on big data are universal solutions with domain
agnostic applications.
More information
(URLs)
www.databio.eu
<other URLs to be added later if relevant>
Note:

Data bio d1.1-agriculture-pilot-definition_v1.0_2017-06-30_lespro

Data bio d1.1-agriculture-pilot-definition_v1.0_2017-06-30_lespro

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