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

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The main goal of the DataBio project is to show the benefits of Big Data technologies in the raw material production from agriculture, forestry and fishery/aquaculture for the bio-economy industry to produce food, energy and bio-materials responsibly and sustainably. DataBio proposes to deploy a state of the art, big data platform on top of the existing partners’ infrastructure and solutions – the Big DATABIO Platform. Achieved impacts are measured against anticipations. In this webinar, we present the impact of the DataBio project and of its big data platform after three years of implementation, and we illustrate some of the novel breakthroughs on practical cases of artificial intelligence applications that are meaningfully boosting crop monitoring businesses with a global potential.

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

  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 BIG DATA BREAKTHROUGHS FOR GLOBAL BIO-ECONOMY BUSINESSES DR. Ephrem Habyarimana Chief Scientist & Research Scientist CREA Research Center for Cereal and Industrial Crops Italy BDVe Webinar December 16, 2019
  2. 2. FINALIST: EUROPEAN DATA SCIENCE & AI AWARDS 2019 DUBLIN, SEPTEMBER 5-TH, 2019 2
  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 Estimate: 7.7 billion people as of April 2019. World population is forecast to grow to 9 billion by 2050. 1 in 6 is already hungry and food production must increase by 70-100% if it is to feed this growing population (Ammann, K., 2012, Chapter 27) No single solutions will solve this problem but Big Data technologies can help to increase agricultural productivity and save people from hunger and other crises in a sustainable manner. WORLD POPULATION AND ASSOCIATED ISSUES
  4. 4. DATABIO’S TARGET: CONTRIBUTION TO INCREASING EUROPEAN AND WORLD’S PRODUCTIVITY DA TA BIO targets to demonstrate that an increase of bioeconomy productivity of 20% is possible. STRATEGY: Engaging European ICT and Earth Observation Industries in innovative Big Data Technologies to boost the main European bioeconomy sectors of Agriculture, Fisheries, and Forestry. 4
  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 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
  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 Project at a glance 2017 2018 2019 27
  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 A Sample of Key Breakthroughs in the DataBio’s Agricultural 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. 8 PILOT 1 [A1.1] PRECISION AGRICULTURE IN OLIVES, FRUITS, GRAPES 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, • Sustainable farming practices through the provision of irrigation, fertilization and pest/disease management advices, • The farmer directly benefits from 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
  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. 9 PILOT RESULTS avgcostofspraying (euros/ha) avgcostofirrigation (euros/ha) Nitrogen use(kg/ha) 0 500 1000 1500 Olives Grapes Peaches 2017 2018 2019 0 1000 2000 3000 4000 Olives Grapes Peaches 2017 2018 2019 0 100 200 300 Olives Peaches 2017 2018 2019 Pilot 1 [A1.1] Precision Agriculture in Olives, Fruits, Grapes 12.40% 40% 30% 2.84% 33.76% N/A 3.58% 42.87% 71.89% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% Crop protection Irrigation Fertilization Input Cost Reduction Peaches Grapes Olives Target: 36% N/A 9% 30% 30% 30% 5% 3.5% 15% Rainfall (mm)
  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. 10 28/11/2017 - The Greek Minister of Digital Policy, Telecommunications and Media Dr. Nikos Pappas 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 http://www.ypaithros.gr/en/yannis-olive-grove- reduction-by-30-in-production-costs-and- parallel-increase-of-sales/ SUCCESS STORIES Chalkidiki Pilot Veroia Pilot Pilot 1 [A1.1] Precision Agriculture in Olives, Fruits, Grapes Stimagka Pilot http://www.gaiasense.gr/en/a-greek-innovation- gaiasense-evolves
  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. 11 Pilot 6 [B1.2] Cereal, Biomass and Cotton Crops_2
  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. 12 Pilot 6 [B1.2] Cereal, Biomass and Cotton Crops_2 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
  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. 13 PILOT RESULTS avg cost of irrigation (euros/ha) 2670 2379 1881 0 500 1000 1500 2000 2500 3000 Cotton 2017 2018 2019 Pilot 6 [B1.2] Cereal, Biomass and Cotton Crops_2 0% 10% 20% 30% 40% 50% 60% Cotton Irrigation Cost Reduction Target Measured
  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. 14 Pilot A2.1. Big data management in greenhouse ecosystems
  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. 15 GENOMIC PREDICTION & SELECTION DATA SCIENCE The High-level Challenge in Genomic Selection: Incorporating MAS for yield into practical breeding programmes. For ~30 years of QTL mapping, this was not yet possible.
  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. 16 PRO G RESS IN BREED IN G RESPO N SE TO SELECTIO N Genomic selection is a gold standard approach for estimating breeding values
  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. 17 IT CAN BE INFERRED: ONLY 4 WAYS TO INCREASE RATE OF RESPONSE: TARGET BREEDING METHOD 1. Increase variation Wide crosses / mutation 2. Increase precision Test more plots per line 3. Select harder Test more lines 4. Reduce time Out of season nurseries GS In GS: Genomic prediction-driven intercrosses => shorter generation intervals ΔR = ihσg/t
  18. 18. http://itema.cereteth.gr/ IOT, CROSSING BLOCKS, AND PHENOTYPING FACILITIES IN ITALY & GREECE (1/2) Big data: 1) Phenotypes + biochemistry 2) IoT data => Glasshouse environmental characteristics
  19. 19. 19 IOT, CROSSING BLOCKS, AND PHENOTYPING FACILITIES IN ITALY & GREECE (2/2) BIG DATA: 1) Phenotypes + Multi/Hyperspectral data 2) IoT data
  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. 20 HIGH-THROUGHPUT GENOTYPING: DDRADSEQ, GBS SNPS, WHOLE-GENOME RE-SEQUENCING Nucleic Acids Info: 1. Genomic data 2. Transcriptomic data
  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. 21 Single/multi-trait & single/multi-environment predictive analytics CV1: prediction of lines that have not been evaluated in any glasshouse/field trials CV2: prediction of lines that have been evaluated in some but NOT all target environments (fields, glasshouses). SEVERAL SCENARIOS OF BREEDING INTEREST ARE MODELLED CV3: Montecarlo Cross-validation: Repeated holdout (e.g., 70% TRN : 30% TST)
  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. 22 GENOMIC SELECTION FOR HEALTH-PROMOTING PRODUCTS TO MANUFACTURE SPECIALTY FOODS (1/2) Habyarimana et al., 2019
  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. 23 GEN O M IC SELECTIO N FO R HEALTH -PRO M O TIN G PRO D U CTS TO MAN U FACTU RE SPECIALTY FO O DS (2/2) Habyarimana et al., 2019
  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. 24 ACCURACY ACHIEVED IN VEGETABLES (CULTIVATED POTATO) Habyarimana et al., 2017 Agronomic and trading traits in cultivated potato
  25. 25. IN OUR OWN EVALUATION: GENOMIC SELECTION VS. PHENOTYPIC SELECTION 25 Empiricial results (KPIs): GS can sustain plant breeding programmes, allowing increased genetic gain per unit time and cost:
  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. 26 Pilot B1.3. Cereal and Biomass Crops
  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. 27 Research challenges 27 Main challenge: Being able to use high-resolution satellite imageries to predict sorghum biomass yields early within season, and with high precision to avoid Stakeholders’ aversion. Current yields forecasting approaches (background info): Field surveys, Censuses, Coarser spatial (250-1000m) resolution satellites (e.g., MODIS, SPOT-VEGETATION); all of which are undependable and/or costly. Our project was therefore designed to address these shortcomings. Research context: Biomass sorghum crop monitoring using fAPAR data derived from Sentinel-2 satellite constellations. Funds: This work was carried out in the framework of DataBio project (2017-2019; www.databio.eu), EU H2020 research and innovation programme.
  28. 28. BIG BIG DATA COLLECTION IN BIOMASS SORGHUM: REAL-TIME STREAMS + NEAR REAL-TIME SATELLITE CONSTELLATION IMAGERIES 28
  29. 29. Results (1/3): We accurately predicted yields early in the cropping season • Meaningful Achievements: we accurately predicted yields up to 6 months ahead of harvest
  30. 30. 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. 30 Results 2/3: Our technology’s hallmark : Reliability, high accuracy Model SMAPE (%) MAPE (%) MAE (t ha-1 ) R2 LM 0.74 0.99 10.47 0.47 bartMachine 0.18 0.16 2.32 0.51 Bayesglm 0.74 0.98 10.34 0.48 xgbTree 0.44 0.36 4.07 0.62 Bayesian machine learning was the most powerful machine learning algorithm (Habyarimana et al., 2019b) SMAPE, MAPE, MAE, R2, symmetrical mean absolute percentage error, mean absolute percentage error, mean absolute error, and coefficient of determination, respectively. LM, bart-Machine, bayesglm, xgbTree, respectively, simple linear model, Bayesian additive regres-sion trees (bartMachine method), Bayesian generalized linear model (bayesglm method), and eXtreme Gradient boosting (xgbTree method). 30
  31. 31. 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. 31 Results 3/3: Towards remote fingerprinting of sorghum phenology and types Cropping season: April – November 2017 fAPARindex:scale0-1 31
  32. 32. 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. 32 PILOT B1.4 CEREALS AND BIOMASS CROPS_4
  33. 33. 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. 33 PILOT B1.4 CEREALS AND BIOMASS CROPS_4, CZECH REPUBLIC • Delineation of yield potential zones based on the time- series analysis of EO data (Landsat 8, Sentinel-2) for whole acreage of pilot farm (8,300 ha), updated every year • Since spring 2019, the variable rate technology for application of nitrogen fertilizers was implemented over 3,000 ha by addressing yield potential heterogeneity within the fields. • Base N fertilizing – before sowing of spring barley and maize • 1st N application in top-dressign of winter cereals (winter wheat, winter barley) • testing of combination of yield potential zoning with current crop status monitoring by Sentinel-2 for VRA of crop growth regulators in spring barely
  34. 34. 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. 34 Pilot 10 [C1.1] Insurance
  35. 35. 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. 35 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 Pilot 10 [C1.1] 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
  36. 36. 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. 36 Pilot C2.1. CAP Support
  37. 37. 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. 37 New innovativeapproach developed by ROMANIA – CAP SUPPORT MONITORING C developments improvements v 4 y Version v.05 Thisdocument is part of aproject that hasreceivedfunding from the European ’ Horizon 2020 researchand innovationprogramme under agreementNo 732064. It is the property ofthe DataBioconsortium and shall not be distributed or reproduced without the formal approval of the DataBioManagementCommittee. Find usatwww.databio.eu. 3 7
  38. 38. 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. 38 Forthe 2018 and 2019 agricultural years, Terrasigna also monitored the CAP declarations for the entire agriculturalarea of Romania. Thetotal surveyed areaexceeds 9milion ha, corresponding tomore than 6milion plots of various sizesandshapes. ThenecessaryEarth Observation (EO)data required multiple Sentinel-2 scenes projected in 2 UTMzones. 21%of the total number of plots within the test areashave surfacesbelow 1ha. Thisdocument is part of aproject that hasreceivedfunding from the European ’ Horizon 2020 researchand innovationprogramme under agreementNo 732064. It is the property ofthe DataBioconsortium and shall not be distributed or reproduced without the formal approval of the DataBioManagementCommittee. Find usatwww.databio.eu. 3 8
  39. 39. 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. 39 Validation using reference data 97.28% 32crops, Based on 16.000+ plots, 60.000+ ha Thisdocument is part of aproject that hasreceivedfunding from the European ’ Horizon 2020 researchand innovationprogramme under agreementNo 732064. It is the property ofthe DataBioconsortium and shall not be distributed or reproduced without the formal approval of the DataBioManagementCommittee. Find usatwww.databio.eu. 3 9
  40. 40. 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. 40 Independent validationresults (validated against VHRimagery, Sentinel-2 and other data sources) 98.3% correct estimations for 8crop categories Increased performance for larger plots T j v E ’ H z v 7 4. I y b b v M C . F . b . . 4 0
  41. 41. 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. 41 • H y , v • S by v • H z k • F v • CONCLUSIONS & PERSPECTIVES Thisdocument is part of aproject that hasreceivedfunding from the European ’ Horizon 2020 researchand innovationprogramme under agreementNo 732064. It is the property ofthe DataBioconsortium and shall not be distributed or reproduced without the formal approval of the DataBioManagementCommittee. Find usatwww.databio.eu. 4 1
  42. 42. POTENTIAL: Services at a Global Level 42 Advisory Services without border Predictive, Descriptive, Recommendation Analytics Visualization [Historical + Current] data; [Crop + Disease] models Georeferenced (Commercial) Fields
  43. 43. 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. 43 Thank you for your attention! ephrem.habyarimana@crea.gov.it

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