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dc.contributor.authorChakraborty, S.
dc.contributor.authorGhosh, R.
dc.contributor.authorGhosh, M.
dc.contributor.authorFernandes, C.D.
dc.contributor.authorCharchar, M.J.
dc.contributor.authorKelemu, Segenet
dc.date.accessioned2012-07-09T05:57:59Z
dc.date.available2012-07-09T05:57:59Z
dc.date.issued2004-08-24
dc.identifier.citationChakraborty, S., Ghosh, R., Ghosh, M., Fernandes, C.D., Charchar, M.J. and Kelemu, S. 2004. Weather-based prediction of anthracnose severity using artificial neural network models. Plant Pathology 53(4):375-386.en_US
dc.identifier.urihttps://hdl.handle.net/10568/21179
dc.description.abstractData were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. Of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21·9% for the Australian and 22·1% for the South American model. Of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.en_US
dc.language.isoenen_US
dc.sourcePlant Pathologyen_US
dc.subjectWEATHERen_US
dc.subjectANIMAL DISEASESen_US
dc.titleWeather-based prediction of anthracnose severity using artificial neural network modelsen_US
dc.typeJournal Articleen_US
cg.subject.ilriANIMAL DISEASESen_US
cg.subject.ilriANIMAL HEALTHen_US
cg.subject.ilriKNOWLEDGE AND INFORMATIONen_US
cg.identifier.statusLimited Accessen_US
cg.targetaudienceACADEMICSen_US
cg.identifier.doihttps://dx.doi.org/10.1111/j.1365-3059.2004.01044.xen_US


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