Decision tree induction model for forecasting the pest Gesonia gemma on soybean based on abiotic factors
Keywords:Semilooper, soybean, decision tree model, Shannon gain ratio, abiotic factors, forecasting model
Severe incidence of semilooper species complex comprising of Gesonia gemma Swinhoe, Chrysodeixis acuta (Wlk.),Â Diachrysia orichalcea (Fabricius) and Mocis undata Fabricius was noticed in the state of Maharashtra betweenÂ 2009 and 2012. Inadequate knowledge on the factors influencing the dynamics of this pest complex is one of theÂ main reasons for control failures. Data were collected from Akola and Amaravathi districts of Maharashtra on theÂ incidence of semiloopers along with weather data to develop the pest forecasting models for planning managementÂ strategies. The data mining technique using decision tree induction model has been proposed for forecasting theÂ incidence of semiloopers. Weather data like maximum temperature, minimum temperature, relative humidity, rainfallÂ and number of rainy days in a week were considered to build the model. The results of Shannon gain ratioÂ revealed that among all the abiotic factors minimum temperature played a major role on pest incidence.
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