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

Conventional procedures are inadequate for optimizing the concentrations ofnutrients to increase the sugar yield. In this study, an artificial neural network(ANN) was used to optimize the Ca, Mg, N, K and Na content of the storage rootto increase sugar yield (Y) by increasing both sugar content (SC) and root yield(T). Data from three field experiments were used to produce a wide range ofvariation in nutrient content, SC and T. In the training phase of the ANN, R2 was0.91 and 0.94 for SC and T, respectively. The high R2 values obtaineddemonstrating the ability of the ANN to predict SC and T. The obtained optimumvalues were 0.37%, 0.35%, 0.97%, 4.67 (meq/100 g) and 0.33% for Ca, Mg, N, Kand Na, respectively. Optimization increased the potential Y by 17%.