Machine Learning Applied to Fourteen Agricultural Datasets Kirsten Thomson Robert J. McQueen Machine Learning Group Department of Computer Science University of Waikato Research Report September, 1996 Table of Contents 1. INTRODUCTION............................................................................................................. 1 1.1 Machine Learning........................................................................................................ 1 1.2 The WEKA machine learning workbench.....................................................................2 1.3 Characteristics of the datasets ...................................................................................... 3 1.4 Classes and accuracy.................................................................................................... 3 1.5 WEKA Workbench functions and machine learning schemes used................................ 4 1.6 The results of the tests ................................................................................................. 4 2. APPLE BRUISING........................................................................................................... 6 2.1 Original research.......................................................................................................... 6 2.2 Machine learning..........................................................................................................6 2.3 Discussion of results.................................................................................................. 12 2.4 Conclusion................................................................................................................. 12 3. BULLS............................................................................................................................ 13 3.1 Original research........................................................................................................ 13 3.2 Machine learning........................................................................................................13 3.3 Discussion of results.................................................................................................. 15 3.4 Conclusion................................................................................................................. 15 4. EUCALYPTUS SOIL CONSERVATION ...................................................................... 17 4.1 Original research........................................................................................................ 17 4.2 Machine learning........................................................................................................17 4.3 Discussion of results.................................................................................................. 25 4.4 Conclusion................................................................................................................. 25 5. GRASS GRUBS AND DAMAGE RANKING................................................................ 30 5.1 Original research........................................................................................................ 30 5.2 Machine learning........................................................................................................30 5.3 Discussion of results.................................................................................................. 33 5.4 Conclusion................................................................................................................. 34 6. GRASS GRUBS AND RAINFALL................................................................................. 38 6.1 Original research........................................................................................................ 38 6.2 Machine learning........................................................................................................39 6.3 Discussion of results.................................................................................................. 41 6.4 Conclusion................................................................................................................. 42 7.GROWER QUESTIONNAIRE ........................................................................................ 46 7.1 Original research........................................................................................................ 46 7.2 Machine learning........................................................................................................46 7.3 Discussion of results.................................................................................................. 49 7.4 Conclusion................................................................................................................. 49 8. PASTURE PRODUCTION............................................................................................. 51 8.1 Original research........................................................................................................ 51 8.2 Machine learning........................................................................................................51 8.3 Discussion of results.................................................................................................. 55 8.4 Conclusion................................................................................................................. 55 9. PEA SEED COLOUR ..................................................................................................... 58 9.1 Original research........................................................................................................ 58 9.2 Machine learning........................................................................................................58 9.3 Conclusion................................................................................................................. 61 i 10. SLUGS.......................................................................................................................... 63 10.1 Original research...................................................................................................... 63 10.2 Machine learning...................................................................................................... 63 10.3 Discussion of results................................................................................................ 68 10.4 Conclusion............................................................................................................... 68 11. SQUASH HARVEST.................................................................................................... 71 11.1 Original research...................................................................................................... 71 11.2 Machine learning...................................................................................................... 71 11.3 Conclusion............................................................................................................... 78 12. VALLEY CATEGORIES.............................................................................................. 83 12.1 Original research...................................................................................................... 83 12.2 Machine learning...................................................................................................... 83 12.3 Discussion of results................................................................................................ 87 12.4 Conclusion............................................................................................................... 87 13. VENISON BRUISING.................................................................................................. 89 13.1 Original research...................................................................................................... 89 13.2 Machine learning...................................................................................................... 89 13.3 Discussion of results................................................................................................ 95 13.4 Conclusion............................................................................................................... 95 14. WASP NESTS .............................................................................................................. 97 14.1 Original research...................................................................................................... 97 14.2 Machine learning...................................................................................................... 97 14.3 Discussion of results.............................................................................................. 102 14.4 Conclusion............................................................................................................. 102 15. WHITE CLOVER PERSISTENCE TRIALS............................................................... 103 15.1 Original research.................................................................................................... 103 15.2 Machine learning.................................................................................................... 103 15.3 Discussion of results.............................................................................................. 111 15.4 Conclusion............................................................................................................