Title :
Forecasting the population of the corn earworm Helicoverpa zea
Author :
Elgaddel, Nazik ; Lin, Frank C. ; Nobakht, Manocher ; Okunbor, Daniel
Author_Institution :
Dept. of Math. & Comput. Sci., Maryland Univ., Princess Anne, MD, USA
Abstract :
The corn earworm (CEW) is significant pest for major crops such as soybean and corn. An accurate forecast of its population will be of great benefit to farmers, who must allocate a correct percentage of their land to trap crops. We apply statistical and neural network methods to estimate future populations based upon historical data
Keywords :
agriculture; autoregressive moving average processes; forecasting theory; neural nets; time series; Helicoverpa zea; corn earworm; neural network methods; pest control; pest population forecasting; statistical methods; time series; trap crops; Africa; Agriculture; Computer science; Crops; Europe; Neural networks; Pest control; Smoothing methods; South America; Temperature;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.938530
