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Toward Global Agricultural Cloud

Toward Global Agricultural Cloud. Masayuki HIRAFUJI * ** Yasuyuki HAMADA * Tomokazu YOSHIDA * Atsushi ITOH * Takuji KIURA * * NARO National Agriculture and Food Research Organization ** University of Tsukuba. “Big Data” Has Been Dream in Agriculture.

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Toward Global Agricultural Cloud

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  1. Toward Global Agricultural Cloud Masayuki HIRAFUJI* ** Yasuyuki HAMADA* Tomokazu YOSHIDA* Atsushi ITOH * Takuji KIURA * * NARO National Agriculture and Food Research Organization ** University of Tsukuba

  2. “Big Data” Has Been Dream in Agriculture • Plant growth is complex system. • Environment is complex system. • Maximization of income • Minimization of pollution • Maximization of plant growth • Modeling by learning • Analysis between genome and phonotype

  3. Nonlinear Regression Models Using Artificial Neural Networks (studied since 20 years ago) Recommendation of fertilizer Predicted Yield … Accumulated air temperature. Accumulated soil moisture Accumulated soil temperature Last year’s application of fertilizer Last year’s yield

  4. Field Servers for Continuous Data Collection

  5. Low-cost USB DNA Sequencer

  6. Nanopore Technology

  7. More phenotypic data is neededfor breeding.

  8. Phenomicsvs. Genomics Gene + ome = GenomeGenome + ics = GenomicsPhenotype + ome = PhenomePhenome + ics = Phenomics

  9. Genome Data >> PhenomeDataby High-throughput Phenotyping Nanopore Sequencer Sensors in Fields Genotypic Data << Phenome Data Environment Data Genome Data >> Phenome Data Environment Data

  10. Massive Deployment by Open Field Server (Open-FS) Wi-Fi LED garden light with IR sensor Solar panel Photo sensors Inside temperature sensor Soil temperature sensor Soil moisture sensor

  11. Field Twitter (Open-FS) Has Been Improved. 樹体水分センサ

  12. Towards A Field PhenomicsCenter Wi-Fi Router 1km • Phenotype data • Calibration data for remote sensing • Memuro Campus of HARC, NARO

  13. Tweeting data

  14. Tweeting data

  15. Tweeting data

  16. Collecting Microscopic Databy A Smartphone with A Macro Lens A macro lens for iPhone Stomata on beet leaves can be measured.

  17. Products with Twitter

  18. Plant Sensor

  19. Data of Agricultural Machinery

  20. Data stream on agricultural machinery Petition (GPS) Speed Power Fuel consumption Steering Vibration Yield Fertilizer Chemical

  21. XML by iGreenProject for Agricultural MachineEU (Germany) leading. USA has a same project (AgGateway) ・ Reprinted from the Proceeding of AgEng 2011 , pp.294, 2011

  22. Farm management data

  23. Contents of FIX-pmsCommon Data Format for Farm Management Data

  24. How Can We Combine Data?API of Cloud Services Can Be A Method.

  25. Let’s Make Big Data for Agriculture! Applicayions Developing New Businesses Precision Farming Decision Support System GAP Models API （Application Interface） New Apps and Businesses CLOP: CLoudOpen Platform in agriculture API API API API Consortium Others Field Data Sensor data of Agr-Machines Faming Data 移動監視 UAV Satellites etc. SNS Smartphones Sensor Networks Such As Field Server ISO11783

  26. All Data Provided As API API of Cloud Services Satellites UAV Smartphone • Variable rate fertilization • Harvester equipped with yield sensor Sensor data

  27. Mashape: Cloud API Hub https://www.mashape.com/

  28. Mash-Up UsingAPIfor Agricultural Data (FIX-pms) API on CLOP FIX FARMS APRAS

  29. Big Data Will Be Created by Using API of Apps FIX FARMS APRAS

  30. The Best Condition Can Be Found on Nonlinear Models Predicted yield • Big data • Yield • Fertilizer • Soil temperature • Soil moisture • : • : … Last year’s yield Last year’s application of fertilizer This year’s application of fertilizer Accumulated soil moisture

  31. Conclusion • CLOP is conceptual framework for API mash-up. • CLOP must be flexible, and will include all. • ANN can utilize big data. • ICT companies should provide open API. • Let’s make big data together. • Let’s make API of agricultural apps. • Let’s open “How to use API”. • Let’s make big data together.

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