Squeezing the turnip with artificial neural nets

Phytopathology

Volumn 94, Issue 9, 2004, Pages 1007-1012

  Francl, Leonard J ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRASSICA RAPA SUBSP. RAPA;

EID: 4344584274     PISSN: 0031949X     EISSN: None     Source Type: Journal    
DOI: 10.1094/PHYTO.2004.94.9.1007     Document Type: Conference Paper

References (25)

1. Bishop, C. M. 1995. Neural Networks for Pattern Recognition. University Press, Oxford, UK.
2. Cheng, B., Titterington, D. M. 1994. Neural networks: A review from a statistical perspective. Stat. Sci. 9:2-54.
3. Chtioui, Y., Francl, L. J., and Panigrahi, S. 1999. Moisture prediction from simple micrometeorological data. Phytopathology 89:668-672.
4. Chtioui, Y., Panigrahi, S., and Francl, L. 1998. A generalized regression neural network and its application for leaf wetness prediction to forecast plant disease. Chemom. Intel. Lab. Syst. 48:55-67.
5. Chtioui, Y., Panigrahi, S., and Francl, L. 1999. Neuro-Regress: Selected Parametric and Nonparametric Regression Tools for Chemometrics. Technical Description and Software Users Guide. North Dakota State University, Fargo.
6. De Wolf, E. D. 2000. Development and application of infection period models for wheat tan spot and Stagonospora blotch. Ph.D. thesis. North Dakota State University, Fargo.
7. De Wolf, E. D., and Francl, L. J. 1997. Neural networks that distinguish infection periods of tan spot of wheat in an outdoor environment. Phytopathology 87:83-87.
8. De Wolf, E. D., and Francl, L. J. 2000. Neural network classification of tan spot and Stagonospora blotch infection periods in a wheat field environment. Phytopathology 90:108-113.
9. De Wolf, E. D., Madden, L. V., and Lipps, P. E. 2003. Risk assessment models for wheat Fusarium head blight epidemics based on within-season weather data. Phytopathology 93:428-435.
10. Fausett, L. 1994. Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice Hall, Englewood Cliffs, NJ.
11. Francl, L. J. 1995. Challenge of bioassay plants in a monitored outdoor environment. Can. J. Plant Pathol. 17:138-143.
12. Francl, L. J., and Panigrahi, S. 1997. Artificial neural network models of wheat leaf wetness. Agric. For. Meteorol. 88:57-65.
13. Gleason, M. L., Taylor, S. E., Loughlin, T. M., and Koehler, K. J. 1994. Development and validation of an empirical model to estimate the duration of dew periods. Plant Dis. 78:1011-1016.
14. Huber, L., and Gillespie, T. J. 1992. Modeling leaf wetness in relation to plant disease epidemiology. Annu. Rev. Phytopathol. 30:553-577.
15. Larson, C. 2001. Evaluation of the NDSU wheat disease forecasting system. MS thesis. North Dakota State University, Fargo.
16. McCulloch, W. S., and Pitts, W. 1943. A logical calculus of ideas immanent in neural activity. Bull. Math. Phys. 5:115-133.
17. Nadaraya, E. A. 1964. On estimating regression. Theory Prob. Appl. 9:141-142.
18. Ripley, B. D. 1996. Pattern Recognition and Neural Networks. University Press, Cambridge, UK.
19. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. 1986. Learning representation by back-propagating errors. Nature 323:533-536.
20. Sarle, W. S. 2002. Neural network FAQ: Introduction. Documented online as FAQ 1473 by the SAS Institute, Cary, NC.
21. Smith, M. 1996. Neural Networks for Statistical Modeling. International Thomson Computer Press, Boston, MA.
22. Specht, D. F. 1991. A general regression neural network. IEEE Trans. Neural Networks 2:568-576.
23. White, H. 1989. Learning in artificial neural networks: A statistical perspective. Neural Comput. 1:425-464.
24. Widrow, B., and Hoff, M. E., Jr. 1960. Adaptive switching circuits. IRE WESCON Convention Record, part 4, Pages 96-104 [Reprinted in 1988: Neurocomputing: Foundations of Research. J. A. Anderson and E. Rosenfeld, eds. MIT Press, Cambridge, MA].
25. Widrow, B., and Hoff, M. E., Jr. 1960. Adaptive switching circuits. IRE WESCON Convention Record, part 4, Pages 96-104 [Reprinted in 1988: Neurocomputing: Foundations of Research. J. A. Anderson and E. Rosenfeld, eds. MIT Press, Cambridge, MA].