Prediction of productivity of mustard plant using variable reduction and artificial neural network model

Abstract

The productivity of mustard plant is dependent on huge number of time dependent parameters. But many of them are not significant or they are highly correlated with others parameters. Some parameters playing significant role in growth of the plant and give the information which is mandatory but not correlated with the others. So, same result can be produced by fewer parameters instead of considering all parameters. In this paper, an effect has been made to reduce the significant environmental parameters which have dominated the growth of mustard plant using principal component and factor analysis. These two methods have been used as variable reduction model. The artificial neural network has been applied on highly significant parameters to predict the shoot length of mustard plant. The linear equation has been used to find the shoot length at maturity. Finally, the productivity of the plant has been predicted based on shoot length of the plant at maturity. © 2011 Springer-Verlag.