Section of Agricultural Statistics, Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar pradesh, India
*Email: gcmishrabhu@gmail.com
Online published on 28 June, 2013.
Pigeonpea (Cajanus cajan) is one of the most important food legume, making it an ideal supplement to traditional cereals, which are generally protein-deficient. So, due to its high nutritional value and enormous losses caused by insect pests, it is very important to forecast the damage caused by major insect-pests and the yield of this crop. In this paper, Artificial Neural Network (ANN) model was developed to forecast productivity (Kg/ha) and percent pod damage by a key insect pest Helicoverpa armigera of long duration pigeonpea in North East Plain Zone (NEPZ) of India. The forecasted values of percent pod damage by this pest and productivity of Pigeonpea during 2012–13 were obtained as 26.29% and 1137.40 kg/ha, respectively. The performance of the model was assessed by values of the mean squared error, and the model was found suitable for the problem under study.
Artificial Neural Network (ANN) model was developed to forecast productivity and percent pod damage by Helicoverpa armigera for NEPZ in India. The forecasted values of percent pod damage and productivity of Pigeonpea by this pest were obtained as 26.29% and 1137.40 kg/ha, respectively.
Forecasting, artificial neural network, mean squared error, pigeonpea, Helicoverpa armigera and productivity
