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

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Data Bio Agriculture Pilot Analysis

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

  1. 1. 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
  2. 2. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 2 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.
  3. 3. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 3 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.
  4. 4. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 4 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 4.1.1 Pilot introduction ..........................................................................................................................37 4.1.2 Pilot overview................................................................................................................................37 4.2 PILOT CASE DEFINITION.................................................................................................................................38 4.2.1 Stakeholder and user stories.........................................................................................................41 4.2.2 Motivation and strategy ...............................................................................................................41 4.3 PILOT MODELLING WITH ARCHIMATE..............................................................................................................41 4.3.1 Agriculture pilot A1.2 Motivation view.........................................................................................41 4.3.2 Agriculture pilot A1.2 Strategy view .............................................................................................42 4.4 PILOT EVALUATION PLAN..............................................................................................................................43 4.4.1 High level goals and KPI's .............................................................................................................43 4.4.2 Initial roadmap .............................................................................................................................43
  5. 5. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 5 4.5 BIG DATA ASSETS.........................................................................................................................................43 PILOT 3 [A1.3] PRECISION AGRICULTURE IN VEGETABLES_2 (POTATOES) .............................................45 5.1 PILOT OVERVIEW.........................................................................................................................................45 5.1.1 Pilot introduction ..........................................................................................................................45 5.1.2 Pilot overview................................................................................................................................45 5.2 PILOT CASE DEFINITION.................................................................................................................................46 5.2.1 Stakeholder and user stories.........................................................................................................48 5.2.2 Motivation and strategy ...............................................................................................................49 5.3 PILOT MODELLING WITH ARCHIMATE..............................................................................................................49 5.3.1 Agriculture pilot A1.3 Motivation view.........................................................................................50 5.3.2 Agriculture pilot A1.3 Strategy view .............................................................................................50 5.4 PILOT EVALUATION PLAN..............................................................................................................................51 5.4.1 High level goals and KPI's .............................................................................................................51 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 6.1.1 Pilot introduction ..........................................................................................................................54 6.1.2 Pilot overview................................................................................................................................54 6.2 PILOT CASE DEFINITION.................................................................................................................................56 6.2.1 Stakeholder and user stories.........................................................................................................59 6.2.2 Motivation and strategy ...............................................................................................................60 6.3 PILOT MODELLING WITH ARCHIMATE..............................................................................................................60 6.3.1 Agriculture pilot A2.1 Motivation view.........................................................................................60 6.3.2 Agriculture pilot A2.1 Strategy view .............................................................................................61 6.4 PILOT EVALUATION PLAN..............................................................................................................................62 6.4.1 High level goals and KPI's .............................................................................................................62 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 7.1.1 Pilot introduction ..........................................................................................................................65 7.1.2 Pilot overview................................................................................................................................65 7.2 PILOT CASE DEFINITION.................................................................................................................................68 7.2.1 Stakeholder and user stories.........................................................................................................68 7.2.2 Motivation and strategy ...............................................................................................................69 7.3 PILOT MODELLING WITH ARCHIMATE..............................................................................................................69 7.3.1 Agriculture pilot B1.1 motivation view .........................................................................................69 7.3.2 Agriculture pilot B1.1 strategy view..............................................................................................70 7.4 PILOT EVALUATION PLAN..............................................................................................................................71 7.4.1 High level goals and KPI's .............................................................................................................71 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 8.1.1 Pilot introduction ..........................................................................................................................74 8.1.2 Pilot overview................................................................................................................................74 8.2 PILOT CASE DEFINITION.................................................................................................................................76
  6. 6. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 6 8.2.1 Stakeholder and user stories.........................................................................................................78 8.2.2 Motivation and strategy ...............................................................................................................79 8.3 PILOT MODELLING WITH ARCHIMATE..............................................................................................................80 8.3.1 Agriculture pilot B1.2 Motivation view .........................................................................................80 8.3.2 Agriculture pilot B1.2 Strategy view .............................................................................................81 8.4 PILOT EVALUATION PLAN..............................................................................................................................82 8.4.1 High level goals and KPI's .............................................................................................................82 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 9.1.1 Pilot introduction ..........................................................................................................................84 9.1.2 Pilot overview................................................................................................................................84 9.2 PILOT CASE DEFINITION.................................................................................................................................87 9.2.1 Stakeholder and user stories.........................................................................................................90 9.2.2 Motivation and strategy ...............................................................................................................91 9.3 PILOT MODELLING WITH ARCHIMATE..............................................................................................................92 9.3.1 Agriculture pilot B1.3 Motivation view .........................................................................................92 9.3.2 Agriculture pilot B1.3 Strategy view .............................................................................................93 9.4 PILOT EVALUATION PLAN..............................................................................................................................93 9.4.1 High level goals and KPI's .............................................................................................................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 11.2.1 Stakeholder and user stories..................................................................................................110 11.2.2 Motivation and strategy ........................................................................................................110 11.3 PILOT MODELLING WITH ARCHIMATE .......................................................................................................111 11.3.1 Agriculture pilot B2.1 Motivation view.................................................................................111 11.3.2 Agriculture Pilot B2.1 Strategy view.......................................................................................112 11.4 PILOT EVALUATION PLAN .......................................................................................................................112 11.4.1 High level goals and KPI's.......................................................................................................112
  7. 7. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 7 11.4.2 Initial roadmap.......................................................................................................................113 11.5 BIG DATA ASSETS..................................................................................................................................113 PILOT 10 [C1.1] INSURANCE (GREECE).................................................................................................114 12.1 PILOT OVERVIEW ..................................................................................................................................114 12.1.1 Pilot introduction....................................................................................................................114 12.1.2 Pilot overview.........................................................................................................................114 12.2 PILOT CASE DEFINITION..........................................................................................................................116 12.2.1 Stakeholder and user stories..................................................................................................118 12.2.2 Motivation and strategy ........................................................................................................119 12.3 PILOT MODELLING WITH ARCHIMATE .......................................................................................................119 12.3.1 Agriculture pilot C1.1 Motivation view ..................................................................................119 12.3.2 Agriculture C1.1 Strategy view...............................................................................................120 12.4 PILOT EVALUATION PLAN .......................................................................................................................121 12.4.1 High level goals and KPI's.......................................................................................................121 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 13.1.1 Pilot introduction....................................................................................................................123 13.1.2 Pilot overview.........................................................................................................................123 13.2 PILOT CASE DEFINITION..........................................................................................................................126 13.2.1 Stakeholder and user stories..................................................................................................127 13.2.2 Motivation and strategy ........................................................................................................127 13.3 PILOT MODELLING WITH ARCHIMATE .......................................................................................................128 13.3.1 Agriculture pilot C1.2 Motivation view ..................................................................................128 13.3.2 Agriculture pilot C1.2 Strategy view.......................................................................................130 13.4 PILOT EVALUATION PLAN .......................................................................................................................131 13.4.1 High level goals and KPI's.......................................................................................................131 13.4.2 Initial roadmap.......................................................................................................................131 13.5 BIG DATA ASSETS..................................................................................................................................132 PILOT 12 [C2.1] CAP SUPPORT ............................................................................................................133 14.1 PILOT OVERVIEW ..................................................................................................................................133 14.1.1 Pilot introduction....................................................................................................................133 14.1.2 Pilot overview.........................................................................................................................133 14.2 PILOT CASE DEFINITION..........................................................................................................................137 14.2.1 Stakeholder and user stories..................................................................................................139 14.2.2 Motivation and strategy ........................................................................................................139 14.3 PILOT MODELLING WITH ARCHIMATE .......................................................................................................139 14.3.1 Agriculture pilot C2.1 Motivation view ..................................................................................139 14.3.2 Agriculture pilot C2.1 Strategy view.......................................................................................141 14.4 PILOT EVALUATION PLAN .......................................................................................................................142 14.4.1 High level goals and KPI's.......................................................................................................142 14.4.2 Initial roadmap.......................................................................................................................142 14.5 BIG DATA ASSETS..................................................................................................................................143 PILOT 13 [C.2.2] CAP SUPPORT (GREECE) ............................................................................................144 15.1.1 Pilot introduction....................................................................................................................144 15.1.2 Pilot overview.........................................................................................................................144 15.2 PILOT CASE DEFINITION..........................................................................................................................146
  8. 8. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 8 15.2.1 Stakeholder and user stories..................................................................................................148 15.2.2 Motivation and strategy ........................................................................................................149 15.3 PILOT MODELLING WITH ARCHIMATE .......................................................................................................149 15.3.1 Agriculture pilot C2.2 Motivation view ..................................................................................149 15.3.2 Agriculture pilot C2.2 Strategy view.......................................................................................150 15.4 PILOT EVALUATION PLAN .......................................................................................................................152 15.4.1 High level goals and KPI's.......................................................................................................152 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 14: AGRICULTURE PILOT A1.3 INITIAL ROADMAP ..................................................................................................52 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 18: AGRICULTURE PILOT A2.1 INITIAL ROADMAP ..................................................................................................63 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 25: AGRICULTURE PILOT B1.2 STRATEGY VIEW.....................................................................................................81 FIGURE 26: AGRICULTURE PILOT B1.2 INITIAL ROADMAP ..................................................................................................82 FIGURE 27: AGRICULTURE PILOT B1.2 BDVA REFERENCE MODEL.......................................................................................83 FIGURE 28: AGRICULTURE PILOT B1.3 MOTIVATION VIEW ................................................................................................92 FIGURE 29: AGRICULTURE PILOT B1.3 STRATEGY VIEW.....................................................................................................93 FIGURE 30: AGRICULTURE PILOT B1.3 INITIAL ROADMAP ..................................................................................................94 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
  9. 9. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 9 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 39: AGRICULTURE PILOT B2.1 STRATEGY VIEW...................................................................................................112 FIGURE 40: AGRICULTURE PILOT B2.1 INITIAL ROADMAP................................................................................................113 FIGURE 41: AGRICULTURE PILOT B2.1 STRATEGY VIEW...................................................................................................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 46: AGRICULTURE PILOT C1.2 MOTIVATION VIEW ..............................................................................................129 FIGURE 47: AGRICULTURE PILOT C1.2 STRATEGY VIEW...................................................................................................130 FIGURE 48: AGRICULTURE PILOT C1.2 INITIAL ROADMAP ................................................................................................131 FIGURE 49: AGRICULTURE PILOT C1.2 BDVA REFERENCE MODEL.....................................................................................132 FIGURE 50: AGRICULTURE PILOT C2.1 MOTIVATION VIEW ..............................................................................................140 FIGURE 51: AGRICULTURE PILOT C2.1 STRATEGY VIEW...................................................................................................141 FIGURE 52: AGRICULTURE PILOT C2.1 INITIAL ROADMAP ................................................................................................142 FIGURE 53: AGRICULTURE PILOT C2.1 BVDA REFERENCE MODEL.....................................................................................143 FIGURE 54: AGRICULTURE PILOT C2.2 MOTIVATION VIEW ..............................................................................................150 FIGURE 55: AGRICULTURE PILOT C2.2 STRATEGY VIEW ...................................................................................................151 FIGURE 56: AGRICULTURE PILOT C2.2 INITIAL ROADMAP ................................................................................................152 FIGURE 57: AGRICULTURE PILOT C2.2 BDVA REFERENCE MODEL.....................................................................................153 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 17: SUMMARY OF PILOT B1.2 (ISO JTC1 WG9 USE CASE TEMPLATE) .......................................................................76 TABLE 18: AGRICULTURE PILOT B1.2 STAKEHOLDERS AND USER STORIES..............................................................................78 TABLE 19: AGRICULTURE PILOT B1.3 OVERVIEW OF PILOT ACTIVITIES..................................................................................85 TABLE 20: SUMMARY OF PILOT B1.3 (ISO JTC1 WG9 USE CASE TEMPLATE) .......................................................................87 TABLE 21: AGRICULTURE PILOT B1.3 STAKEHOLDERS AND USER STORIES..............................................................................90 TABLE 22: SUMMARY OF PILOT B1.4 (ISO JTC1 WG9 USE CASE TEMPLATE) .......................................................................98 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
  10. 10. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 10 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 TABLE 29: SUMMARY OF PILOT C1.2 (ISO JTC1 WG9 USE CASE TEMPLATE)......................................................................124 TABLE 30: AGRICULTURE PILOT C1.2 STAKEHOLDERS AND USER STORIES ............................................................................127 TABLE 31: SUMMARY OF PILOT C2.1 (ISO JTC1 WG9 USE CASE TEMPLATE)......................................................................134 TABLE 32: AGRICULTURE PILOT C2.1 OVERVIEW OF PILOT ACTIVITIES ................................................................................138 TABLE 33: AGRICULTURE PILOT C2.1 STAKEHOLDERS AND USER STORIES ............................................................................139 TABLE 34: AGRICULTURE PILOT C2.2 OVERVIEW OF PILOT ACTIVITIES ................................................................................145 TABLE 35: SUMMARY OF PILOT C2.2 (ISO JTC1 WG9 USE CASE TEMPLATE)......................................................................146 TABLE 36: AGRICULTURE PILOT C2.2 STAKEHOLDERS AND USER STORIES ............................................................................148
  11. 11. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 11 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
  12. 12. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 12 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
  13. 13. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 13 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
  14. 14. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 14 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
  15. 15. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 15 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
  16. 16. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 16 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.
  17. 17. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 17 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
  18. 18. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 18 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)
  19. 19. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 19 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.
  20. 20. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 20 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.
  21. 21. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 21 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
  22. 22. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 22 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.
  23. 23. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 23 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.
  24. 24. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 24 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.
  25. 25. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public 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.
  26. 26. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 26 Figure 2: Relationships of the Motivation elements Figure 3: Relationships of the Strategy elements
  27. 27. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 27 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
  28. 28. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 28 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
  29. 29. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public 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
  30. 30. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 30 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,
  31. 31. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 31 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)).
  32. 32. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 32 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.
  33. 33. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 33 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.
  34. 34. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 34 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).
  35. 35. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 35 • %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.
  36. 36. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 36 Figure 7: Agriculture pilot A1.1 BDVA reference model
  37. 37. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 37 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.
  38. 38. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public Page 38 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.
  39. 39. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public 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.
  40. 40. D1.1 – Agriculture Pilot Definition H2020 Contract No. 732064 Final – v1.0, 30/6/2017 Dissemination level: PU -Public 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:

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