Decision tree induction model for forecasting the pest Gesonia gemma on soybean based on abiotic factors - Pratheepa, M; Prasad, Y G; Jeyakumar, P; Vennila, S; Prabhakar, M; Barkhede, U P; Singh, Niranjan; Sharma, A N; Antony, J Cruz (Indian Journal of Plant Protection, 2020)
Severe incidence of semilooper species complex comprising of Gesonia gemma Swinhoe, Chrysodeixis acuta (Wlk.), Diachrysia orichalcea (Fabricius) and Mocis undata Fabricius was noticed in the state of Maharashtra between 2009 and 2012. Inadequate knowledge on the factors influencing the dynamics of this pest complex is one of the main reasons for control failures. Data were collected from Akola and Amaravathi districts of Maharashtra on the incidence of semiloopers along with weather data to develop the pest forecasting models for planning management strategies. The data mining technique using decision tree induction model has been proposed for forecasting the incidence of semiloopers. Weather data like maximum temperature, minimum temperature, relative humidity, rainfall and number of rainy days in a week were considered to build the model. The results of Shannon gain ratio revealed that among all the abiotic factors minimum temperature played a major role on pest incidence.