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Overview of data bio project results

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Overview of DataBio project results.
Thanasis Poulakidas (DataBio coordinator, INTRASOFT Intl

Publicado en: Datos y análisis
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Overview of data bio project results

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

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