Comparison of three measurement models of soil nitrate-nitrogen based on ion-selective electrodes
Keywords:
ion-selective electrode, soil nitrate-nitrogen, measurement model, accuracyAbstract
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.References
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[2] Zhou J. Determination and analysis of soil nitrogen concentration. Information of Agricultural Science and Technology, 2008; 15: 40–41. (in Chinese)
[3] Li S J. Brief analysis on function of nitrogen fertilizer in agricultural production and current existing questions. Heilongjiang Agricultural Sciences, 2010; 1: 41–44. (in Chinese)
[4] Zhang M, Ang S S, Nguyen V C, Li L. Rapid nitrate detection system based on ion selective electrode. Transactions of the CSAE, 2009; 25(Supp. 2): 235–239. (in Chinese)
[5] Giles J F, Reuss J O, Ludwick A E. Prediction of nitrogen status of sugar beet by soil analysis. Agronomy Journal, 1975; 67(4): 454–459.
[6] Du S F, Cao S S, Pan Q, Zhu Y. Study on interference factors and measurement model of soil nitrate-nitrogen detection based on ion selective electrode. Transactions of the CSAM, 2016; 47(9): 171–179. (in Chinese)
[7] Zhang L N, Zhang M, Ren H Y, Pu P, Kong P. Feasibility of rapid detection of soil nitrate-nitrogen content using Superfloc127 in ion-selective electrode. Transactions of the CSAE, 2015; 31(Supp1): 196–204. (in Chinese)
[8] Tully K L, Weil R. Ion-selective electrode offers accurate, inexpensive method for analyzing soil solution nitrate in remote regions. Communications in Soil Science and Plant Analysis, 2014; 45(14): 1974–1980.
[9] Claudio Z, Dermot D. Opportunities and challenges of using ion-selective electrodes in environmental monitoring and wearable sensors. Electrochimica Acta, 2012; 84(1): 29–34.
[10] Zhang L N, Zhang M, Ren H Y, Pu P, Kong P, Zhao H J. Comparative
investigation on soil nitrate-nitrogen and available potassium measurement capability by using solid-state and PVC ISE. Computers and Electronics in Agriculture, 2014; 112: 83–91. (in Chinese)
[11] Francesco D G, Eric H S, Maria G, Pietro S, Aparna G, Zenyth S. Assessment of ionic interferences to nitrate and potassium analyses with ion-selective electrodes. Communications in Soil Science and Plant Analysis, 2010; 41(14): 1750–1768.
[12] Huang L. Determination of nitrate nitrogen in water by ion selective electrode method. Chinese Journal of Public Health, 1999; 15(3): 220–220. (in Chinese)
[13] Sun X D, Zhang H L, Liu Y D. Nondestructive assessment of quality of Nanfeng mandarin fruit by a portable near infrared spectroscopy. Int J Agric & Biol Eng, 2009; 2(1): 65–71.
[14] Chang Z K. Research and development of a prototype of instrument for soil nitrate measurement based on ion selective electrode. China Agricultural University, 2014. (in Chinese)
[15] Du S F, Cao S S, Pan Q, Zhu Y, Feng L. Improving accuracy of soil nitrate-nitrogen detection based on ion selective electrode. Transactions of the CSAM, 2016; 47(1): 118–125. (in Chinese)
[16] Mei D. Studies on simultaneous determination of multicomponent by ion-selective electrodes analytical method. Tianjin University, 2005. (in Chinese)
[17] Jin W L, Zhou M Y. Study on calibration of binocular stereovision based on BP neural network with different layers. Optical Technique, 2015; 41(1): 72–76. (in Chinese)
[18] Li S K, Suo X M, Bai Z Y, Qi Z L, Liu X H, Gao S J, et al. The machine recognition for population feature of wheat images based on BP neural network. Journal of Integrative Agriculture, 2002; 1(8): 885–889.
[19] Pau M, María G, Pablo G. An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data. Computers and Electronics in Agriculture, 2013; 91: 75–86.
[20] Li T, Zhang M, Ji Y H, Sha S, Jiang Y Q, Li M Z. Management of CO2 in a tomato greenhouse using WSN and BPNN techniques. Int J Agric & Biol Eng, 2015; 8(4): 43–51.
[21] Sun B Q, Pan Q S, Feng Y J, Zhang C S. Research on improving the training speed of BP network. Journal of Harbin Institute of Technology, 2001; 4: 439–441. (in Chinese)
[22] Sun W W. Study on Improved Algorithm and Application of BP Neural Network. Chongqing University, 2009. (in Chinese)
[23] Deng J, Yang J M. An improvement of learning algorithm for BP neural network. Journal of Donghua University, 2005; 31(3): 123–126. (in Chinese)
[24] Haruhiko T, Hidehiko K, Terumine H. Partially weight minimization approach for fault tolerant multilayer neural networks. International Joint Conference on Neural Networks, 2002; 2: 1092–1096.
[25] Liu T S. The Research and Application on BP Neural Network Improvement. Northeast Agricultural University, 2011. (in Chinese)
[26] Zhao X M. Controlling the platform phenomenon in BP neural network model. Journal of Taizhou University, 2002; 24(3): 7–9. (in Chinese)
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Published
2020-03-02
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Du, S., Pan, Q., Xu, Y., & Cao, S. (2020). Comparison of three measurement models of soil nitrate-nitrogen based on ion-selective electrodes. International Journal of Agricultural and Biological Engineering, 13(1), 211–216. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/3599
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Information Technology, Sensors and Control Systems
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