COMPARISON OF NEURAL NETWORK AND MULTIVARIATE DISCRIMINANT ANALYSIS IN
SELECTING NEW COWPEA VARIETY
Adewole, Adetunji Philip *
Department of Computer Science, University of Agriculture, Abeokuta
Sofoluwe, A. B.
Department of Computer Science, University of Lagos, Akoka
Agwuegbo , Samuel Obi-Nnamdi
Department of Statistics, University of Agriculture, Abeokuta
In this study, neural networks (NN) algorithm and multivariate discriminant (MDA) based model were developed to classify ten (10)
varieties of cowpea which were widely planted in Kano. . In order to demonstrate the validity of our model, we use the case study to
build a neural network model using Multilayer Feedforward Neural Network, and compare its classification performance against the
Multivariate discriminant analysis. Two groups of data (Spray and Nospray) were used. Twenty kernels were used as training data set
and test data to classify cowpea seed varieties. The neural network classified the new cowpea seed varieties based on the information
it is trained with. At the end both methods were compared for their strength and weakness. It is noted that NN performed better than
MDA, so that NN could be considered as a support tool in the process of selection of new cowpea varieties.
KEYWORDS: Cowpea, Multivariate Discriminant Analysis (MDA), Neural Network (NN), Perceptron.
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 4, July 2010
The history of neural networks begins with the earliest model of the biological neuron given by . This model describes a neuron as
a linear threshold computing unit with multiple inputs and a single output of either 0, if the nerve cell remains inactive, or 1, if the cell
fires. A neuron fires if the sum of the inputs exceeds a specified threshold. In functional form, this gives f(x) = 1 for x greater than