Forecasting Helianthus annuus Seed Quality Based on Soil Chemical Properties Using Radial Basis Function Neural Networks

Forecasting crop chemical characteristics based on soil properties is not only a possible way to spare supplementary sampling and testing, but also a potential method of instructing cultivation planning based on regional soil surveys. In this paper, taking the data of regional agricultural geological survey on Helianthus annuus sources in the western part of the Jilin province as an attempt, radial basis function neural networks were used to forecast the quality indexes of Helianthus annuus seeds based on the non-linear relationship between soil and crop. The results indicate the following: (1) The mean relative errors of vitamin E, protein, fat, and TAA concentration forecasting neural networks are 2.63%, 2.19%, 2.19%, and 2.80%, respectively. The root mean square errors are 1.7 mg/100 g, 0.59%, 1.09%, and 0.77%. The forecasting radial basis function neural networks are of high prediction accuracy, which introduces an empirical case of forecasting the quality of crop based on a systematical soil environmental quality investigation along with a sampling survey of the crops. To set a proper model, interrelation between the selected indexes of input layer and output layer needs to be confirmed first, and a low setting of spread can improve the accuracy; (2) Soil in the studied area is under severe salinization, and concentrations of soil chemical properties mostly show an evident regional difference between the three experimental fields. However, the vitamin E, protein, and TAA concentrations of Helianthus annuus seeds all stabilize in a certain range despite the different soil environments. The mean fat concentration of Helianthus annuus seeds collected from Nongan and Daan exceeds those from Tongyu by approximately 5%, which shows a relatively evident regional difference.