Abstract
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.
Highlights
In recent years, the Chinese population has been increasing dramatically, but the arable land has been reducing year after year
It has been proved theoretically that a three-layer Back Propagation (BP) neural network is able to approximate to any functions[21] as long as the hidden layer nodes are sufficient, so a three-layer BP neural network was adopted as the measurement model
Concentrations of nitrate ions in leaching solutions were calculated by the linear regression model, multiple linear regression model and BP neural network model respectively, and they were compared with values detected by the spectrophotometric method in Beijing Center for Physical and Chemical Analysis (BCPCA)
Summary
The Chinese population has been increasing dramatically, but the arable land has been reducing year after year. Improving land productivity is the necessary way to improve the overall grain production capacity and it is an important measure to guarantee food security[1]. There are many kinds of soil nitrate-N measurement models based on ISE. The choice of measurement model is one of the important factors affect nitrate nitrogen measurement precision. The traditional measurement model of soil nitrate-N is a linear regression model[10], which can be used to characterize the relationship between reading of nitrate ISE and the concentration of nitrate ions. One of them is the multiple linear regression model, which reflects the linear relationships between ISEs and ion concentrations. This study analyzed the linear regression model, multiple linear regression model and BP neural network model, and used them to measure the soil nitrate-N concentrations. The measurement accuracies of three models were compared in order to find out the soil nitrate-N measurement model with the highest accuracy
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