A neural network model for prediction of deoxynivalenol content in wheat grain based on weather data and preceding crop
2007
Klem, K.,Agrotest Fyto, Kromeriz (Czech Republic) | Vanova, M.,Agrotest Fyto, Kromeriz (Czech Republic) | Hajslova, J.,Vysoka Skola Chemicko-technologicka, Prague (Czech Republic) | Lancova, K.,Vysoka Skola Chemicko-technologicka, Prague (Czech Republic) | Sehnalova, M.,Vysoka Skola Chemicko-technologicka, Prague (Czech Republic)
Data about deoxynivalenol (DON) content in wheat grain, weather conditions during the growing season and cultivation practices from two field experiments conducted in 2002-2005 were used for the development of neural network model designed for DON content prediction. The winning neural network is based on five input variables: a categorial variable - preceding crop, and continuous variables - average April temperature, sum of April precipitation, average temperature 5 days prior to anthesis, sum of precipitation 5 days prior to anthesis. The most important input variables are the preceding crop and sum of precipitation 5 days prior to anthesis. The weather conditions in April which are important for inoculum formation on crop debris are also of important contribution to the model. The correlation between observed and predicted data using the neural network model reached the coefficient R2=0.87.
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