Artificial neural network methodology for modelling and forecasting maize crop yield

Singh, R.K. and Prajneshu, - (2008) Artificial neural network methodology for modelling and forecasting maize crop yield. Agricultural Economics Research Review, 21 (1). pp. 5-10.

[img] PDF
Restricted to ICRISAT researchers only


A particular type of “Artificial neural network (ANN)”, viz. Multilayered feedforward artificial neural network (MLFANN) has been described. To train such a network, two types of learning algorithms, namely Gradient descent algorithm (GDA) and Conjugate gradient descent algorithm (CGDA), have been discussed. The methodology has been illustrated by considering maize crop yield data as response variable and total human labour, farm power, fertilizer consumption, and pesticide consumption as predictors. The data have been taken from a recently concluded National Agricultural Technology Project of Division of Agricultural Economics, I.A.R.I., New Delhi. To train the neural network, relevant computer programs have been written in MATLAB software package using Neural network toolbox. It has been found that a three-layered MLFANN with (11,16) units in the two hidden layers performs best in terms of having minimum mean square errors (MSE) for training, validation, and test sets. Superiority of this MLFANN over multiple linear regression (MLR) analysis has also been demonstrated for the maize data considered in the study. It is hoped that, in future, research workers would start applying not only MLFANN but also some of the other more advanced ANN models, like ‘Radial basis function neural network’, and ‘Generalized regression neural network’ in their studies.

Item Type: Article
Author Affiliation: Biometrics Division, Indian Agricultural Statistics Research Institute (ICAR), New Delhi - 110 012
Subjects: Atmosperic Science > Climatology
Social Sciences > Agricultural Economics
Divisions: Maize
Depositing User: David T
Date Deposited: 18 Nov 2010 16:21
Last Modified: 29 Dec 2010 20:27

Actions (login required)

View Item View Item