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

Pigeonpea (Cajanus cajan L.) is a valuable food legume that may be cultivated with minimal inputs under rainfed conditions. Starch, protein, calcium, manganese, crude fibre, fat, trace elements, and minerals abound in pigeonpea. The need of having a timely forecast of productivity and pod damage caused by key insect-pests in pigeonpea has become a serious issue due to high domestic consumption and large losses due to major insect-pests. The built Artificial Neural Network (ANN) model for forecasting productivity (Kg/ha.) and percent pod damage by two important insect-pests, Helicoverpa armigera and Melanagromyza obtusa, of medium ripening pigeonpea in the Central Zone (CZ) of India, is reported in this paper. The model’s performance was evaluated using mean squared error values, and it was determined to be suitable for the task at hand.

Author(S) Details

Prity Kumari
Section of Agricultural Statistics, Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221005, India.

G. C. Mishra
Section of Agricultural Statistics, Department of Farm Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221005, India.

C. P. Srivastava
Department of Entomology and Agricultural Zoology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi-221005, India.

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