Forecasting of Losses Due to Pod Borer, Pod Fly and Yield of Pigeonpea (Cajanus cajan) for Central Zone (CZ) of India by Using Artificial Neural Network

Abstract

Pigeonpea (Cajanus cajan L.) is an important food legume that can be grown under rainfed conditions with least inputs. Pigeonpea is rich in starch, protein, calcium, manganese, crude fiber, fat, trace elements and minerals. High domestic consumption and significant losses due to major insect-pests are become the important issue to have timely forecast of productivity and pod damage caused by major insect-pests in pigeonpea. In this study, we presented here the developed Artificial Neural Network (ANN) model for forecasting productivity (Kg/ha.) and percent pod damage by two major insect-pests that are Helicoverpa armigera and Melanagromyza obtusa of medium maturing pigeonpea in Central Zone (CZ) of India. The performance of the model was assessed by values of the mean squared error and was found to be suitable for the problem under study.