The optimization of root nutrient content for increased sugar beet productivity using an artificial neural network

Abstract

Conventional procedures are inadequate for optimizing the concentrations of<br />nutrients to increase the sugar yield. In this study, an artificial neural network<br />(ANN) was used to optimize the Ca, Mg, N, K and Na content of the storage root<br />to increase sugar yield (Y) by increasing both sugar content (SC) and root yield<br />(T). Data from three field experiments were used to produce a wide range of<br />variation in nutrient content, SC and T. In the training phase of the ANN, R2 was<br />0.91 and 0.94 for SC and T, respectively. The high R2 values obtained<br />demonstrating the ability of the ANN to predict SC and T. The obtained optimum<br />values were 0.37%, 0.35%, 0.97%, 4.67 (meq/100 g) and 0.33% for Ca, Mg, N, K<br />and Na, respectively. Optimization increased the potential Y by 17%.