COMPARISON OF NEURAL NETWORK AND MULTIVARIATE DISCRIMINANT ANALYSIS IN SELECTING NEW COWPEA VARIETY
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Adewole, Adetunji Philip
Sofoluwe, A. B.
Agwuegbo, Samuel Obi-Nnamdi
International Journal of Computer Science and Information Security (IJCSIS)
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.
Cowpea , Multivariate Discriminant Analysis (MDA) , Neural Network (NN) , Perceptron
Adewole, Adetunji Philip, Sofoluwe, A. B., Agwuegbo, Samuel Obi-Nnamdi (2010). COMPARISON OF NEURAL NETWORK AND MULTIVARIATE DISCRIMINANT ANALYSIS IN SELECTING NEW COWPEA VARIETY. International Journal of Computer Science and Information Security (IJCSIS). 8(4): 350-358