Author/Authors :
Gholipoor، M نويسنده Department of Crop Sciences, Shahrood University of Technology, Shahrood, Iran , , Emamgholizadeh، S نويسنده Department of Water and Soil Sciences, Shahrood University of Technology, Shahrood, Iran , , Hassanpour، H نويسنده Department of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran , , Shahsavani، D نويسنده Department of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran , , Shahoseini، H نويسنده Department of Crop Sciences, Shahrood University of Technology, Shahrood, Iran , , Baghi، M نويسنده Department of Water and Soil Sciences, Shahrood University of Technology, Shahrood, Iran , , Karimi، A نويسنده Department of Crop Sciences, Shahrood University of Technology, Shahrood, Iran ,
Abstract :
Conventional procedures are inadequate for optimizing the concentrations of
nutrients 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 root
to 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 of
variation in nutrient content, SC and T. In the training phase of the ANN, R2 was
0.91 and 0.94 for SC and T, respectively. The high R2 values obtained
demonstrating the ability of the ANN to predict SC and T. The obtained optimum
values were 0.37%, 0.35%, 0.97%, 4.67 (meq/100 g) and 0.33% for Ca, Mg, N, K
and Na, respectively. Optimization increased the potential Y by 17%.
