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iGreen: Co-creative Mobile Services in Agriculture - A Knowledge Management Perspective

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Presentation held by Andreas Dengel at the Agricultural Ontology Service (AOS) Workshop 2012 in Kutching, Sarawak, Malaysia from September 3 - 4, 2012 …

Presentation held by Andreas Dengel at the Agricultural Ontology Service (AOS) Workshop 2012 in Kutching, Sarawak, Malaysia from September 3 - 4, 2012


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  • 1. Andreas  Dengel  © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 1
  • 2. Agenda   Some  words  about  the  world‘s  food  situations     The  iGreen  project   Agricultural  knowledge  management  –  two   perspectives   Consolidating  knowledge  and  decision  support   services   Crop  forecasting   Logistic  planning   Soil  quality  map  adjustment   Summary  and  conclusions   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 2
  • 3. Agriculture  must  provide  food  for  an  extra  2  billion  people  but  80%  of     extra  agricultural  food  can  be  provided  only  through  yield  increase!     9  billion  people   Extra  agricultural food  production     by  2050!   100%   ≤20%   ≥80%   By  increasing  arable   By  increasing  yield   land   Amount  of  arable  land   Too  many  factors   in  developed  countries   High  complexity!   World  demand  for  cereal  (bn  tonnes2   (mn  hectares)   ?   ) +43%   -­‐0.2%   625   3,0   575   2,1   2010   2050   2010   2050  Source:  (1)  UN,  World  population  to  exceed  9  billion  by  2050.  http://www.un.org/esa/population/publications/wpp2008/pressrelease.pdf,  2008    (2)  N.  Alexandratos,  J.  Bruinsma,  G.  Boedeker,  J.  Schmidhuber,  S.  Broca,  P.  Shettym,  and  MG  Ottaviani.  World  agriculture:  towards  2030/2050.      Interim  Report.  Prospects  For  Food,  Nutrition,  Agriculture  and  Major  Commodity  Groups,  2006.   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 3
  • 4. IT  can  help  to  improve  and  economize  methods,  procedures  and  agricultural  technologies  to  produce  more  efficiently  and  save  resources   ¨  Precision  farming:    Linked  and  controlled  by  GPS,  seed,  fertilizer  and    pesticides  are  yielded  exactly  where  they  are  needed   ¨  Digital  soil  quality  maps:   Continuous  measure  lead  to  updated  guidelines  helping   to  plan  the  supply  of  nutrients  or  predict  harvest   ¨  Analysis  of  high-­‐resolution  images  from  space:   Controlled  specialized  satellites  take  pictures  of   agricultural  region  all  over  the  world   ¨  Field  robots  will  take  over  some  agricultural  tasks:   Equipped  vehicles  (not  only  for  harvesting)  are  staffed   with  sensors  to  measure  the  state  of  crop  and  soil   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 4
  • 5.   What  are  the  ideas  of  the  iGreen  project?     © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 5
  • 6. iGreen  builds  on  an  alliance  of  23  partners  from  science,  business  and  public  institutions   Connec4vity   private   public   (internaonal)     (naonal)   iis   (regional)   EPP (na4onwide)   e.v. Interests  of  the  agricultural  sector   Interests  of  the  social  community   Applica4on   Consulng  services   Das Bildelement mit der Beziehungs-ID rId7 Management   wurde in der Datei nicht Farmers  /  agricultural  service     gefunden. supply  agency     © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 6
  • 7. iGreen  strikes  a  new  path  in  public-­‐private  knowledge  management  having  a  focus  in  agriculture   ¨  The  goal  of  iGreen  is  to  develop  location-­‐bases  services  and  knowledge  sharing   networks  for  combining  distributed,  heterogeneous  public  as  well  as  private   information  sources   ¨  Built  on  that,  we  aim  at  the  development  of  mobile  decision  assistants  using  Web   services  for  a  decentralized  support  of   energy-­‐efficient,  economic,   environmental-­‐adapted  and  collaboratively-­‐organized    production  and  planning  processes   ¨  There  are  many  high-­‐potential  application  fields  for  the  iGreen   platform,  such  as  agriculture,  forestry,  water  supply  and   distribution,  urban  development  and  landscaping,  or  nature   conservation   ¨  in  iGreen  we  exemplary  focus  on  crop  production   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 7
  • 8. In  crop  production  there  is  whole  bunch  of  relevant  questions  having  a  space-­‐time  relationship  that  have  to  be  answered  for  making  decisions                    Which  kind  of  plant  species              When  should  we  apply  what            should  we  cultivate     kind  of  fertilizer    and  to  which  dose?            at  what  location?     How  much  pesticides   When  is  the  best                should  we  apply  at   time  for  the  harvest?   what  time?          What  market  prices          may  we  expect?   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 8
  • 9. Public  country-­‐wide  institutions  in  Germany  act  as  competence  centers  and  provide  best-­‐practice  advice  about  crop  production  for  farmers   Competence   Center   Support  for  Data  Management   Compendium  about   and  Model  Generation     Best-­‐Practice  Crop  Protection   Advise  in  Grade  and  Seed   Regional  Data   Selection   About  Fields  and  Lots   Consulting  about  Fertilizer   Data  Base  about  Crop  Experiment   Combination  and  Use   Results   Tools  for  Prognoses  and   News  about  Market  Development   Statistical  Data  Analysis   Information  about  how  to  combat   pest  and  plant  diseases     (best  time,  conditions  and  strategies)   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 9
  • 10. Some  examples  about  the  services  from  the  competence  centers   Competence   Center   Pest Immigration Data Interpolation Methods (MR, Kriging, spline, ....) 2009 2011 Exposition Gradient Regional Risc Map Factor Weather Station Information (Point⇒Zone) + + = ⇒ ⇒ © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 10
  • 11. Yield  depends  on  many  factors  –  Experts  and  Decision  Support  Systems  help  farmers  to  optimize  yields   Ecological   Social  factors   factors   Yield   •  Soil  quality   •  Transfer  of  skills   and  roles   •  Weather   condition   •  Security  of  land   (rainfall)   tenure   •  Sun  exposition   •  Access  to  land   ?   Economical   Political   factors   factors   +  Ecological  factors   •  Fuel,  fertilizers,   •  Agricultural   +  Economical  factors   pesticides  prices   subventions   +  Political  factors   •  Product  selling   •  Quotas   +  Social  factors   prices  (Market)   +  Model(s)   •  Other   ________________   •  …   restrictions        Optimized  Yield   So  how  to  get  all  of  these  factors  combined?   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 11
  • 12. iGreen  profits  from  recent  technological  advances  that,  in  the  intended  combination,  will  strongly  influence  agricultural  progresses   Soil  Quality  Estimation   ¨  Public  information  as  well  as  product  and  expert  knowledge   are  increasingly  digitized  and  available  via  the  internet,  such   as  different  kinds  of  maps  or  weather  data   Methods   Thematical  Maps   ¨  Accessing  the  internet  is  possible  from   almost  all  locations  via  mobile  devices   ¨  GPS  devices  for  positioning  are  easy  to   buy  or  are  even  part  of  a  mobile  phone   ¨  Agricultural  engineering  provides  more  and  more  software   interfaces  for  the  automatic  situation-­‐adaptive  control  (on-­‐board   terminals,  sensor  technology,  …   So  we  are  going  to  establish  a  Service  and  Knowledge  Network   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 12
  • 13. The  iGreen  Planning  System  combines  knowledge  subjects  and  information  objects  for  a  participative  public-­‐private  knowledge  management   Agro  Databases   Geo  Catalogue   Data  Provision   Competence   Center   Planning   System   Global  Optimization   Public   Local  Optimization   Private   Free  Service     for  Farmers   Feedback Feedback   Prognoses   Farmers   Location-­‐based   Decision  Assistance   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 13
  • 14. Flexible  Data  Management  is  a  challenge   “ Before you start talking about sensor data, please provide me first an option to file, manage and employ the data “ Farmer  and  Contractor  Marx   ¨  The  only  condition  is  individual  data  ownership      -­‐  local  filing  combined  with  controlled  interchange  within  the  iGreen  network   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 14
  • 15.   Public-­‐private   knowledge  management    © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 15
  • 16. Knowledge  Management  is  a  process  for  improving  organizational  capabilities  by   better  use  of  individual  and  collective  knowledge  resources   1.   Knowledge  Goals:  Define  all  capabilities  an   Definition   organization  should  build  on   2.   Knowledge  Identification:  Identify  internal  and   Identify   Acquire   knowledge   external  knowledge  of  the  organization   knowledge   3.   Knowledge  Acquisition:  Critical  capabilities  must  be   Define knowledge     2   3   bought  or  otherwise  obtained   4.   Knowledge  Development:  Produce  new  internal     Goals   1   wledge! and  external  knowledge  (individual  &  collective  level)   Develop   5.   Knowledge  Dissemination:  Define  who  should     Kno Feedback   knowledge   know  what  and  at  what  level  of  detail,  and  how  the   organization  can  support  this  distribution  process   knowledge Control 8       Mana gement! 4   6.   Knowledge  Utilization:  Productive  deployment  of   organizational  knowledge  in  the  business/production   Preserve   Cycle! 7   process  of  the  organization   knowledge   7.   Knowledge  Preservation:  Identify  valuable   5   knowledge,  store  it,  and  regularly  integrate  it  into   Use 6     Distribute   the  organizational  knowledge  base     knowledge   knowledge   8. Knowledge  Controlling:  Compare  initial  knowledge   goals  with  results  of  organizational  knowledge  magt.   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 16Source:  G.J.B.  Probst,  S.  Raub,  and  K.  Romhardt.  Wissen  managen.  Gabler,  1997.  ISBN  3409193170.    
  • 17. In  terms  of  knowledge  management,  both  experts  and  farmers  have  their  own   knowledge  management  cycle   Decision  Support  System  (DSS)   Disseminate     Identify   Acquire   Use   knowledge   knowledge   knowledge   5   6   knowledge   2   3   Define   Preserve   knowledge   7   knowledge   Goals   1   Increase  Yields   Develop   Feedback   knowledge   Control   8   knowledge   Control   8   4   4   knowledge   Feedback   Develop   Define  knowledge   1   knowledge   Preserve   7   Goals   knowledge   Develop  DSS   3   5   Acquire   2   Use 6     Distribute   knowledge   Identify   knowledge   knowledge     knowledge   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 17
  • 18. In  terms  of  knowledge  management,  both  experts  and  farmers  have  their  own   knowledge  management  cycle   Decision  Support  System  (DSS)   Disseminate     Identify   Acquire   Use   knowledge   knowledge   knowledge   5   6   knowledge   2   3   Define   knowledge   7   Preserve   Goals   1   knowledge   Increase  Yields   Develop   Feedback   knowledge   Control   8   knowledge   Control 8     4   4   knowledge   Feedback   Develop   Define  knowledge   1   knowledge   Preserve   7   Goals   knowledge   Develop  DSS   3   5   Acquire   2   Use 6     Distribute   knowledge   Identify   knowledge   knowledge     knowledge   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 18
  • 19. In  terms  of  knowledge  management,  both  experts  and  farmers  have  their  own   knowledge  management  cycle   Decision  Support  System  (DSS)   Disseminate     Acquire   knowledge   knowledge   5   3   Define   knowledge   Goals   1   Increase  Yields   Develop   knowledge   4   4   Develop   Define  knowledge   1   knowledge   Goals   Develop  DSS   3   Acquire   Use 6     knowledge   knowledge     © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 19
  • 20. In  terms  of  knowledge  management,  both  experts  and  farmers  have  their  own   knowledge  management  cycle   Decision  Support  System  (DSS)   No  better   decision  support  !   Disseminate     Acquire   knowledge   knowledge   5   3   Define   knowledge   Goals   1   Increase  Yields   Develop   Not  Possible!   knowledge   4   4   Develop   Define  knowledge   1   knowledge  No  better   Goals   advice   Soil Type Develop  DSS   3   S Acquire   Use 6   SI IS SL sL   knowledge   L LT T Mo knowledge   No  better     map  material!   Soil  quality  map  from    the  1950’s    (Low  definition  and  outdated)   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 20
  • 21. Available  agricultural  technology  allows  farmers  to  derive  accurate  and  up  to   date  soil  quality  maps   GPS  Technology  combined  with  agricultural  sensors  to  measure  the  yield   Moisture  Sensor   Mass  Flow  Sensor   GPS  Receiver   Task  Computer   User  Interface   Soil  quality  map  from  the   Soil  quality  map  from   1950’s  :   precision  ag.:   •  Outdated   •  Up  to  date  Source:  R.  Grisso,  M.  Alley,  and  P.  McCellan.  Precision  farming  tools:   yield  monitor.  Precision  Farming,  pages  442–502,  2003.     •  Low  resolution   •  High  resolution   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 21
  • 22. A  collaborative  knowledge  management  approach  can  lead  to  better  agricultural   decision  support   Better  Decision   Support   Win-­‐Win   Acquire   Disseminate     knowledge   5   Situation   knowledge   Define   3   knowledge   Goals   1   Increase  Yields   Develop   Possible   knowledge   4   4   Develop   Define  knowledge   1   knowledge  Better  DSS   Goals   Yield in  t/ha Develop  DSS   3   0  -­‐2 2,01  – 4 Use 6   4.01  – 6 Acquire   6.01  – 8 >8   knowledge   knowledge   Yield  maps     Better   (High  definition  &  up-­‐to-­‐date)   map  material!   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 22
  • 23. Farmers  can  collaboratively  contribute  to  the  acquisition  of  better  geo-­‐data  and   get  in  return  better  decision  support  Partial  collaboration:   Full  collaboration:  Data  is  only  shared  with  the  experts   Data  is  shared  within  the  community   Geo-­‐Data   Information  Supply   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 23
  • 24.   Scenario  planning  allows   crop  forecast    © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 24
  • 25. Examples  of  Applications  and  Evaluations  The  collaborative  contribution  was  implemented  in  applications  for  biomass  and  logistics  planning   Biomass  Planning     Goal   • Computing  the  optimal  biomass  yield   based  on:   –  list  of  fields   –  production  plan   –  soil  quality   –  weather  conditions   Resources   • Experts  provide  models  and  weather  data   • Farmers  provide  accurate  soil  quality   maps  using  precision  agriculture  and  a   production  plan   Approach   • Soils  quality  maps  provided  by  a  farmer   are  used  to  improve  results  but  remain   confidential   • Models  are  improved  and  benefit                             the  whole  community   Partial  collaboration! © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 25
  • 26. Tasks  and  their  dependencies  of  Biomass-­‐Yield-­‐Models  (BYM)  are  transformed   into  a  scientific  workflow   Computational  steps  for  scientific  simulations  or  data-­‐analysis  steps   n  Advantages  of  scientific  workflows:   •  Data  flow  approach  based  on  visual  programming   •  Modularity  and  reusability   •  Provenance  information  to  better  interpret  results  and   debug  errors   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 26Source:  B.  Ludäscher,  I.  Altintas,  C.  Berkley,  D.  Higgins,  E.  Jaeger,  M.  Jones,  E.A.  Lee,  J.  Tao,  and  Y.  Zhao.  Scientific  workflow  management  and  the  Kepler  system:  Research  articles.  Concurr.  Comput.  :  Pract.  Exper.,  18:1039-­‐1065,  August  2006.  ISSN  1532-­‐0626.  doi:  10.1002/cpe.v18:10.  
  • 27. Based  on  various  public  sources  we  have  developed  a  Crop  Forecast  System   aiming  at  supporting  the  farmer  in  making  decisions   Pre-­‐given  Goal   Rain   Result  Map   Crop  Forecast   Region   Intermediate   Result   BYM   Result  Table   Filter   Soil  Quality   Result  Chart   Agricltural  Crop  Land  Online   Rheinland-­‐Pfalz  (FLOrlp)   Biomass   Yield   Models     Soil  Quality  Data (BYM)  *  Zentralstelle  der  Länder  für  EDV-­‐gestützte  Entscheidungshilfen      und  Programme  im  Pflanzenschutz     © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 27
  • 28. A  Mashup  of  maps  and  tabular  information  provides  a  intuitive  platform  for  biomass  planning   Excel  and  Google  Earth  export   Ecological  or  conventional  yield   3  rainfall  scenarios  (dry,  normal,  wet)   Results  for  fields   Results  for  subfields   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 28
  • 29. In  Google  Earth  subfield-­‐related  information  may  be  accessed  via  any  mobile  device   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 29
  • 30. Exporting  the  information  to  an  Excel  spreadsheet  (with  Macros)  supports  realizing  a  production  plan   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 30 © andreas.dengel@dfki.de -
  • 31. We  have  tested  the  forecast  on  a  farm  by  evaluating  four  tours*   Biomass  (t)   -­‐15%   One  field  not  be   harvested   1.215 Weed  infestations   -­‐2%   -­‐10%   -­‐19%   667 651 512 550 459 443 Tour  1   Tour  2   Tour  3   Tour  4   Forecast   Real   *  16  fields  to  be  harvested;  avg.  dist.  betw.  field  and  silo:  9.5  km;  average  exploitable  acreage:  2.44  ha   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 31
  • 32.   Logistic  planning  allows   to  remarkably  save  fuel    © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 32
  • 33. Examples  of  Applications  and  Evaluations  The  collaborative  contribution  was  implemented  in  applications  for  biomass  and  logistics  planning   Biomass  Planning     Logistics  Planning   Goal   • Computing  the  optimal  biomass  yield   • Computing    the  optimal  logistics  plan,   based  on:   i.e.,  route  and  costs    for:   –  list  of  fields   –  harvester(s)     –  production  plan   –  tractor(s)  with  trailers   –  soil  quality   –  weather  conditions   Resources   • Experts  provide  models  and  weather  data   • Experts  provide  models   • Farmers  provide  accurate  soil  quality   • Farmers  provide  accurate  GPS-­‐Tracks  and   maps  using  precision  agriculture  and  a   meta-­‐information  about  their  routes  using   production  plan   precision  agriculture  and  number  of   harvesters  and  tractors  available   Approach   • Soils  quality  maps  provided  by  a  farmer   • GPS-­‐Tracks  and  meta-­‐information   are  used  to  improve  results  but  remain   provided  by  a  farmer  are  sanitized  and   confidential   shared  with  the  whole  community   • Models  are  improved  and  benefit                             • Models  are  improved  and  benefit                         the  whole  community   the  whole  community   Full  collaboration! © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 33
  • 34. Current  available  route  guidance  systems  do  not  provide  information  about  road  narrowing  nor  bridge  weights  limits   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 34
  • 35. This  way,  we  also  developed  a  first  collaborative  open-­‐source  routing  system  for  utilities  vehicles  (CRUV)   Vehicle  type  (car,  lorry,  tractor)   -­‐ Road  types:  Motorway,  highway,        country  road,  field  path  with  priorities    (preferred,  normal,  avoid,  forbidden)   -­‐ Tunnels  and  bridges  with  priorities   Vehicle  length,  weight,  height,  width   Rules  based  on  user  meta-­‐information    stored  in  Open  Street  Maps   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 35
  • 36. Compared  to  Map24,  there  is  no  difference  in  quality  in  traditional  route  planning   CRUV:  186  km   Map24:  186  km   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 36
  • 37. However,  CRUV  reveals  its  advantages  when  including  the  additional  information,  e.g.  with  bridges  and  maximal  weight  allowed   Google  Maps  does  not   support  the  kind  of  query!   GoogleMaps:  1,5  km   CRUV:  2,8  km     © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 37
  • 38. A  logistic  planer    computes  transportation  costs  based  on  the  results  from  the  CRUV  and  biomass  planner   Overview  with  Biomass  and  costs   Field  repartition  with  respect  to  the  driving  distance   Max.  driving  distance  d   between  field  and  POI   List  of  Fields  (<  d):   Point  of  interest  (ex:   -­‐ Size   Biogas  plant)   -­‐ Driving  distance   -­‐ Crop   -­‐ Yield   -­‐ Transportation  costs   Slope  (of  the  road)   Route  between  POI  based  on  driving  distance   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 38
  • 39. We  have  tested  the  forecast  on  a  farm  by  evaluating  four  tours*   Biomass  (t)   Transportation  costs  (EUR)   +4%   -­‐15%   One  field  not  be   Higher  fuel   harvested   consumption   14.077 due  to  dry  soil   1.215 Weed  infestations   +57%   -­‐3%   +8%   -­‐2%   8.003 -­‐10%   -­‐19%   6.714 6.542 6.816 667 651 6.320 512 550 5.086 459 443 Tour  1   Tour  2   Tour  3   Tour  4   Tour  1   Tour  2   Tour  3   Tour  4   Forecast   Real   Forecast   Real   *  16  fields  to  be  harvested;  avg.  dist.  betw.  field  and  silo:  9.5  km;  average  exploitable  acreage:  2.44  ha   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 39
  • 40. The  goals  of  iGreen  are  very  relevant  for  many  social  systems   ¨  iGreen  follows  an  approach  leading  to  more  accurate  and  geographically  differentiated   agricultural  forecast  by   -­‐  leveraging  existing  farmers  GPS-­‐based  precision  agriculture  technology   -­‐  collaboratively  acquiring  accurate,  high-­‐resolution,  and  up-­‐to-­‐dare  geo-­‐data   -­‐  allowing  public  agricultural  institutions,  in  return,  to  use  this  geo-­‐data  to  provide  farmers   with  better  decision  support  tools   ¨  Biomass  Planner   -­‐  A  decision  support  system  relying  on  this  approach  to  provide  better  biomass  planning   -­‐  Both  the  biomass  model  and  soil  quality  maps  are  improved  but  no  data  is  shared  with   the  community.  Only  the  improved  model  benefits  the  community   ¨  Logistic  Planner   -­‐  A  decision  support  system  relying  on  this  approach  to   provide  better  logistics  planning   -­‐  Both  the  model  and  the  geo-­‐data  are  shared  with  the   community   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 40
  • 41. e the! to clos re going agement! So, we a ge Man d Knowle   Cycle! Moreover,   existing  soil  quality  maps  may  be  continuously  improved     © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 41
  • 42. Remember  that  forecast  is  based  on  the  data  coming  from  existing  soil  quality  maps!   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 42
  • 43. Digital  soil  quality  maps  provided  by  state  government  allow  farmers  to  get  an  overview  about  the  quality  of  their  fields   40   Additional   information  may  be   accessed  via  the  lot   No.   33   44   61   53   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 43
  • 44. Agricultural  engineering  provides  relevant  location-­‐based  data   Humidity   Sensor   Recording  of  location,  crop,  humidity,   and  fuel  consumption:   Melt  Flow   Sensor   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 44
  • 45. Location-­‐based  recording  of  crop  data  leads  to  an  up-­‐date  of  soil  quality  maps   Recorded  on  29-­‐09-­‐2008  Average  Value  12,23  t/ha   Try  Solids  100,40  t  Humidity  73,55  %   CROP  (t/ha)   from   to   HISTOGRAM   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 45
  • 46. Decision  support  in  crop  production  provides  important  contributions  for  increasing  efficiency,  saving  resources,  and  reducing  environmental  impact  Exemplary  result  (10  farmers  in  the  north  of  the  German  state  Rhineland-­‐Palatinate):   ¨  Winter  wheat  is  the  most  important  crop  in  Germany  having  a  total   acreage  of  3  Mio  hectares.  Experts  estimate  that  each  hectare  of   winter  wheat  requires  about  200  kg  of  nitrate  per  year.  Nitrate   production  causes  about  50%  of  energy  consumption  in  crop   production   ¨  Using  location-­‐adaptive  fertilizer  dispersion  for  wheat  production   based  on  improved  soil  quality  maps  ,  it  was  possible  to  save  ca.  5%   of  nitrates  without  reducing  the  amount  of  crop   ¨  The  German  agriculture  may  …     ...  lead  to  possible  energy  saving  potential  of  30.000  tons  of  nitrate  per  year   ...  avoid  climate-­‐relevant  emissions  of  257,000  tons  of  CO2  annually   ...  reduces  the  amount  of  nitrate-­‐containing  nutriments  in  bodies  of  water   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 46
  • 47. The  goals  of  iGreen  are  very  relevant  for  many  social  systems   ¨  Food  production  is  not  a  task  just  of  agriculture,  but  a  central  aim  of  the  global  society   ¨  In  case  of  crisis,  food  security  has  to  be  guaranteed  by  both,  federal  and  private   organizations  (this  is  part  of  the  food  precaution  law  and  food  ensuring  law  in  Germany)   ¨  iGreen  use  cases  and  demonstrators  prove  the  new  options  based  on  real  application   scenarios   ¨  iGreen  SDK  provides  general  and  fundamental  components  as  open  source   ¨  iGreen  documents  (processes,  scenarios,  interfaces)   provide  excellent  guidelines,  e.g.  who  communicates  with   whom  ,why,  and  with  what  tools     Long-­‐term  Public-­‐Private  Partnerships  are  of   major  importance   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 47
  • 48. Thank  you,  questions  are  welcome!   © German Research Center for Artificial Intelligence - andreas.dengel@dfki.de 2012 - Page 48