Comparison of three measurement models of soil nitrate-nitrogen based on ion-selective electrodes - Du, Shangfeng; Pan, Qi; Xu, Yun; Cao, Shushu (International Journal of Agricultural and Biological Engineering, 2020)
Ion-selective electrode (ISE) is a quick and low-cost method of soil nitrate nitrogen (N) detection. The measurement models of soil nitrate-N based on ISEs includes the linear regression model, multiple linear regression model and BP neural network model, and so on. Three models were analyzed in theory, measurement experiments of validation samples and soil nitrate-N concentrations were carried out in this study, and the measurement accuracies of the three models were compared. The results showed that, in the measurement experiments of validation samples and soil nitrate-N concentrations, BP neural network model had the highest accuracy (the average relative errors between results of the BP neural network model and the reference values were 5.07% and 8.81%, respectively) among the three models, multiple linear regression model had the second highest accuracy (the average relative errors between results of the multiple linear regression model and the reference values were 7.70% and 10.51%, respectively), linear regression model couldn’t exclude the interference of chloride ions so that it had the lowest accuracy (the average relative errors between results of the linear regression model and the reference values were 11.16% and 12.28%, respectively) among the three models. The BP neural network model can effectively restrain the interference of chloride ions, and it has a high accuracy for the measurement of soil nitrate-N concentration, so that the BP neural network model can be used to measure soil nitrate-N concentration accurately. Keywords: ion-selective electrode, soil nitrate-nitrogen, measurement model, accuracy DOI: 10.25165/j.ijabe.20201301.3599 Citation: Du S F, Pan Q, Xu Y, Cao S S. Comparison of three measurement models of soil nitrate-nitrogen based on ion-selective electrodes. Int J Agric & Biol Eng, 2020; 13(1): 211–216.