Artificial Neural Network Methodology for Modelling and Forecasting Maize Crop Yield

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.


Issue Date:
2008
Publication Type:
Journal Article
Record Identifier:
http://ageconsearch.umn.edu/record/47354
PURL Identifier:
http://purl.umn.edu/47354
Published in:
Agricultural Economics Research Review, Volume 21, Number 1
Page range:
5-10
Total Pages:
6




 Record created 2017-04-01, last modified 2018-01-22

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)