TY - JOUR ID - 758 TI - The optimization of root nutrient content for increased sugar beet productivity using an artificial neural network JO - International Journal of Plant Production JA - IJPP LA - en SN - 1735-6814 AU - Gholipoor, M. AU - Emamgholizadeh, S. AU - Hassanpour, H. AU - Shahsavani, D. AU - Shahoseini, H. AU - Baghi, M. AU - Karimi, A. AD - Department of Crop Sciences, Shahrood University of Technology, Shahrood, Iran. AD - Department of Water and Soil Sciences, Shahrood University of Technology, Shahrood, Iran. AD - Department of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran. AD - Department of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran. Y1 - 2012 PY - 2012 VL - 6 IS - 4 SP - 429 EP - 442 KW - Keywords: Artificial neural network KW - Nutrient content KW - Optimization KW - Sugar beet DO - 10.22069/ijpp.2012.758 N2 - 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%. UR - https://ijpp.gau.ac.ir/article_758.html L1 - https://ijpp.gau.ac.ir/article_758_764031dd9f38f73bbc0f67c3e5ab7f13.pdf ER -