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Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment
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| Zeitschriftentitel: | Phytopathology® |
|---|---|
| Personen und Körperschaften: | , |
| In: | Phytopathology®, 87, 1997, 1, S. 83-87 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Scientific Societies
|
| Schlagwörter: |
| author_facet |
De Wolf, E. D. Franel, L. J. De Wolf, E. D. Franel, L. J. |
|---|---|
| author |
De Wolf, E. D. Franel, L. J. |
| spellingShingle |
De Wolf, E. D. Franel, L. J. Phytopathology® Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment Plant Science Agronomy and Crop Science |
| author_sort |
de wolf, e. d. |
| spelling |
De Wolf, E. D. Franel, L. J. 0031-949X 1943-7684 Scientific Societies Plant Science Agronomy and Crop Science http://dx.doi.org/10.1094/phyto.1997.87.1.83 <jats:p> Tan spot of wheat, caused by Pyrenophora tritici-repentis, provided a model system for testing disease forecasts based on an artificial neural network. Infection periods for P. tritici-repentis on susceptible wheat cultivars were identified from a bioassay system that correlated tan spot incidence with crop growth stage and 24-h summaries of environmental data, including temperature, relative humidity, wind speed, wind direction, solar radiation, precipitation, and flat-plate resistance-type wetness sensors. The resulting data set consisted of 97 discrete periods, of which 32 were reserved for validation analysis. Neural networks with zero to nine processing elements were evaluated 20 times each to identify the model that most accurately predicted an infection event. The 200 models averaged 74 to 77% accuracy, depending on the number of processing elements and random initialization of coefficients. The most accurate model had five processing elements and correctly predicted 87% of the infection periods in the validation set. In comparison, stepwise logistic regression correctly predicted 69% of the validation cases, and multivariate discriminant analysis distinguished 50% of the validation cases. When wetness-sensor inputs were withheld from the models, both the neural network and logistic regression models declined 6% in prediction accuracy. Thus, neural networks were more accurate than statistical procedures, both with and without wetness-sensor inputs. These results demonstrate the applicability of neural networks to plant disease forecasting. </jats:p> Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment Phytopathology® |
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10.1094/phyto.1997.87.1.83 |
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Scientific Societies, 1997 |
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| title |
Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| title_unstemmed |
Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| title_full |
Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| title_fullStr |
Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| title_full_unstemmed |
Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| title_short |
Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| title_sort |
neural networks that distinguish infection periods of wheat tan spot in an outdoor environment |
| topic |
Plant Science Agronomy and Crop Science |
| url |
http://dx.doi.org/10.1094/phyto.1997.87.1.83 |
| publishDate |
1997 |
| physical |
83-87 |
| description |
<jats:p> Tan spot of wheat, caused by Pyrenophora tritici-repentis, provided a model system for testing disease forecasts based on an artificial neural network. Infection periods for P. tritici-repentis on susceptible wheat cultivars were identified from a bioassay system that correlated tan spot incidence with crop growth stage and 24-h summaries of environmental data, including temperature, relative humidity, wind speed, wind direction, solar radiation, precipitation, and flat-plate resistance-type wetness sensors. The resulting data set consisted of 97 discrete periods, of which 32 were reserved for validation analysis. Neural networks with zero to nine processing elements were evaluated 20 times each to identify the model that most accurately predicted an infection event. The 200 models averaged 74 to 77% accuracy, depending on the number of processing elements and random initialization of coefficients. The most accurate model had five processing elements and correctly predicted 87% of the infection periods in the validation set. In comparison, stepwise logistic regression correctly predicted 69% of the validation cases, and multivariate discriminant analysis distinguished 50% of the validation cases. When wetness-sensor inputs were withheld from the models, both the neural network and logistic regression models declined 6% in prediction accuracy. Thus, neural networks were more accurate than statistical procedures, both with and without wetness-sensor inputs. These results demonstrate the applicability of neural networks to plant disease forecasting. </jats:p> |
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| author | De Wolf, E. D., Franel, L. J. |
| author_facet | De Wolf, E. D., Franel, L. J., De Wolf, E. D., Franel, L. J. |
| author_sort | de wolf, e. d. |
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| description | <jats:p> Tan spot of wheat, caused by Pyrenophora tritici-repentis, provided a model system for testing disease forecasts based on an artificial neural network. Infection periods for P. tritici-repentis on susceptible wheat cultivars were identified from a bioassay system that correlated tan spot incidence with crop growth stage and 24-h summaries of environmental data, including temperature, relative humidity, wind speed, wind direction, solar radiation, precipitation, and flat-plate resistance-type wetness sensors. The resulting data set consisted of 97 discrete periods, of which 32 were reserved for validation analysis. Neural networks with zero to nine processing elements were evaluated 20 times each to identify the model that most accurately predicted an infection event. The 200 models averaged 74 to 77% accuracy, depending on the number of processing elements and random initialization of coefficients. The most accurate model had five processing elements and correctly predicted 87% of the infection periods in the validation set. In comparison, stepwise logistic regression correctly predicted 69% of the validation cases, and multivariate discriminant analysis distinguished 50% of the validation cases. When wetness-sensor inputs were withheld from the models, both the neural network and logistic regression models declined 6% in prediction accuracy. Thus, neural networks were more accurate than statistical procedures, both with and without wetness-sensor inputs. These results demonstrate the applicability of neural networks to plant disease forecasting. </jats:p> |
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| spelling | De Wolf, E. D. Franel, L. J. 0031-949X 1943-7684 Scientific Societies Plant Science Agronomy and Crop Science http://dx.doi.org/10.1094/phyto.1997.87.1.83 <jats:p> Tan spot of wheat, caused by Pyrenophora tritici-repentis, provided a model system for testing disease forecasts based on an artificial neural network. Infection periods for P. tritici-repentis on susceptible wheat cultivars were identified from a bioassay system that correlated tan spot incidence with crop growth stage and 24-h summaries of environmental data, including temperature, relative humidity, wind speed, wind direction, solar radiation, precipitation, and flat-plate resistance-type wetness sensors. The resulting data set consisted of 97 discrete periods, of which 32 were reserved for validation analysis. Neural networks with zero to nine processing elements were evaluated 20 times each to identify the model that most accurately predicted an infection event. The 200 models averaged 74 to 77% accuracy, depending on the number of processing elements and random initialization of coefficients. The most accurate model had five processing elements and correctly predicted 87% of the infection periods in the validation set. In comparison, stepwise logistic regression correctly predicted 69% of the validation cases, and multivariate discriminant analysis distinguished 50% of the validation cases. When wetness-sensor inputs were withheld from the models, both the neural network and logistic regression models declined 6% in prediction accuracy. Thus, neural networks were more accurate than statistical procedures, both with and without wetness-sensor inputs. These results demonstrate the applicability of neural networks to plant disease forecasting. </jats:p> Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment Phytopathology® |
| spellingShingle | De Wolf, E. D., Franel, L. J., Phytopathology®, Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment, Plant Science, Agronomy and Crop Science |
| title | Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| title_full | Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| title_fullStr | Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| title_full_unstemmed | Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| title_short | Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| title_sort | neural networks that distinguish infection periods of wheat tan spot in an outdoor environment |
| title_unstemmed | Neural Networks That Distinguish Infection Periods of Wheat Tan Spot in an Outdoor Environment |
| topic | Plant Science, Agronomy and Crop Science |
| url | http://dx.doi.org/10.1094/phyto.1997.87.1.83 |