damian peckett - artificially intelligent crop irrigation

Download Damian Peckett - Artificially Intelligent Crop Irrigation

Post on 21-Nov-2014

404 views

Category:

Education

1 download

Embed Size (px)

DESCRIPTION

Presented 27/09/2013 at the University of Southern Queensland, Undergraduate Engineering Conference. Due to constraints set by other mandatory activities I was only able to secure 28 hours to prepare. After an epic all-nighter these slides and my accompanying talk received an "A" grading.

TRANSCRIPT

  • 1. ARTIFICIALLY INTELLIGENT CROP IRRIGATION Damian Peckett Supervisor: Dr Alison McCarthy Assistant Supervisor: Dr Nigel Hancock
  • 2. ARTIFICIAL INTELLIGENCE?
  • 3. Its Broad!
  • 4. Bayesian Statistics Machine Perception Natural Language Processing Machine Learning And More!
  • 5. MACHINE LEARNING A FIVE MINUTE INTRODUCTION
  • 6. MATH IS Beautiful! It Really Is!
  • 7. But The Resolution Of This Plot Isnt!
  • 8. Reinforcement OR CLASSIFICATION?
  • 9. SOMETIMES THERE ISNt JUST A RIGHT ANSWER!
  • 10. GROWING A PLANT? REINFORCEMENT!
  • 11. MARKOV DECISION PROCESS Construct a Model Develop a Value Estimator Solve Belmanns Equations ??? PROFIT (Meme Reference)
  • 12. A MODEL?
  • 13. ARABIDOPSIS THALIANA
  • 14. 5 WEEKS 5 CHROMOSOMES 50 YEARS
  • 15. FRESNO CALIFORNIA
  • 16. Similar CONDITIONS Human irrigation GREAT DATA
  • 17. Big DATA and 2000 lines of C
  • 18. MODEL PARAMETERS Biological Properties (Numerous Research Papers) Solar Insolation (METSTAT NSRDB Model) Air Temperature (NOAA Observations / NASA GFS Model) Leaf Temperature (Custom Physics Model) Soil Temperature (CSIRO Shallow Soil Model (B. Horton 2012)) Relative Humidity (NOAA Observations / NASA GFS Model) Wind Speed (NOAA Observations / NASA Model) Precipitation (NOAA Observations)
  • 19. WEATHER Time Solar Flux Air Temp Relative Humidity Wind Speed
  • 20. Transpiration Time Milliliters Per Hour
  • 21. EVAPORATION Time Milliliters Per Hour
  • 22. PROPAGATION Time Grams Per Hour
  • 23. GROWTH RESPONSE Time Soil Moisture Leaf Area
  • 24. REINFORCEMENT LEARNING
  • 25. A SIMPLIFIED MDP
  • 26. VALUE FUNCTION () = () + max ( ) 0.863 0.879 0.900 0.919 0.936 0.956 0.976 1.000 0.843 0.863 0.879 0.900 0.919 0.936 0.956 0.976 0.827 0.843 0.827 0.879 0.890 0.879 0.936 0.956 0.807 0.827 0.807 0.827 0.839 0.899 0.919 0.936 0.790 0.807 0.827 0.843 0.863 0.879 0.899 0.919 0.774 0.790 0.807 0.827 0.843 0.8790.823 0.899 0.758 0.774 0.790 0.807 0.827 0.843 0.863 0.879 0.742 0.758 0.774 0.790 0.807 0.827 0.843 0.863
  • 27. OPTIMUM POLICY 0.863 0.879 0.900 0.919 0.936 0.956 0.976 1.000 0.843 0.863 0.879 0.900 0.919 0.936 0.956 0.976 0.827 0.843 0.827 0.879 0.890 0.879 0.936 0.956 0.807 0.827 0.807 0.827 0.839 0.899 0.919 0.936 0.790 0.807 0.827 0.843 0.863 0.879 0.899 0.919 0.774 0.790 0.807 0.827 0.843 0.8790.823 0.899 0.758 0.774 0.790 0.807 0.827 0.843 0.863 0.879 0.742 0.758 0.774 0.790 0.807 0.827 0.843 0.863
  • 28. AT EVERY ACTION THE VALUE FUNCTION IS MAXIMISED
  • 29. BUT SOMETIMES YOU CANT USE TILES!
  • 30. VALUE ESTIMATION, USING REGRESSION
  • 31. CONTINUOUS MDP
  • 32. BUT THE CONTOUR MAP IS COMPLEX! Try fitting a line of best fit to that!
  • 33. Introducing THE HERO!
  • 34. Gaussian KERNEL RBF = exp( 2 ) X Is The Current State, Xk Is The RBF Centroid, Is The Inverse Variance.
  • 35. Complex Shapes REQUIRE COMPLEX FEATURE SPACES!
  • 36. FEATURE DIMENSIONS Taylor Series Expansion Of The Exponential Function: exp = 1 + + 2 2! + 3 3! + In the Gaussian RBF x is equal to the square of the L2 distance from the radial basis centroid. Therefore: INFINITE! FEATURE DIMENSIONS!
  • 37. WITH ENOUGH RBFs YOU CAN ESTIMATE ANY FUNCTION!
  • 38. MY ALGORITHM Value Estimation MDP Linear Combination Of Basis Functions Eight Gaussian Radial Basis Functions Fourteen Input Features
  • 39. INPUT FEATURES Current Air Temperature Current Relative Humidity Current Solar Insolation Current Soil Water Availability Ratio Current Leaf Area Current Wind Speed 24 Hour Predicted Average Air Temperature 24 Hour Predicted Average Relative Humidity 24 Hour Predicted Average Wind Speed 24-48 Hour Predicted Average Air Temperature 24-48 Hour Predicted Average Relative Humidity 24-48 Hour Predicted Average Wind Speed Total Solar Energy Last 24 Hours Total Applied Irrigation Phew that was a long list!
  • 40. RBF Centers?
  • 41. Run The Model With A BANG-BANG CONTROLLER
  • 42. K-mEANS CLUSTERING t-SNE 2D Representation of 14 Dimensional Model Output Data
  • 43. CENTER THE RBFS ON THE CLUSTERS!
  • 44. FIXED VARIANCE FOUND USING GRID SEARCH
  • 45. REWARD FUNCTION What Needs To Be Optimized? For All The Following Results We Are Using: R(s) = (LEAF AREA) / (TOTAL APPLIED IRRIGATION) Attempting To Maximize For Water Usage Efficiency.
  • 46. SOLVE USING VALUE ITERATION!
  • 47. TRAINING RESULTS Xk = [ 297 41 356 0.162 16983 1.9 299 38 1.7 300 36 2 30 0.976 291 53 281 0.153 9173 2.2 291 53 2.3 291 52 2.3 25 0.533 293 48 301 0.168 2169 1.7 293 49 1.8 292 52 1.9 27 0.271 303 34 354 0.157 22727 1.9 303 32 2.3 300 28 2.4 31 1.5 292 49 359 0.152 12487 2.4 292 49 2.5 295 45 2.3 27 0.763 291 47 314 0.152 6300 2.4 291 47 2.2 291 49 2.1 29 0.45 291 49 392 0.174 609 2.5 290 52 2.5 291 52 2.5 26 0.107 290 55 301 0.183 3974 2.3 290 55 2.4 290 52 2.5 24 0.352 ] = [ 78519 63857 74194 76854 53085 70596 76115 76447 76815 ] Holdout Cross Validation Error: 3.6 Percent The training worked!
  • 48. Lets TEST IT!
  • 49. ON THE MODEL Time (hours) Leaf Area (mm2) Artificially Intelligent Algorithm Undergoing Simulated Testing
  • 50. MODEL Results Normal Irrigation Schedule (Sub Irrigation): Dry Shoot Biomass: 0.684 grams Irrigation Approach Achieved A Score Of: 7011 Bang-Bang Control, Drip Irrigation: Dry Shoot Biomass: 1.07 grams Irrigation Approach Achieved A Score Of: 25673 Artificial Intelligence , Drip Irrigation: Dry Shoot Biomass: 1.15 grams Irrigation Approach Achieved A Score Of: 26335
  • 51. THE REAL WORLD
  • 52. PROCESSING UNIT
  • 53. ARABIDOP