Near Infrared Reflectance Spectroscopy: Prediksi Cepat dan Simultan Kadar Unsur Hara Makro pada Tanah Pertanian

https://doi.org/10.22146/agritech.42430

Devianti Devianti(1), Sufardi Sufardi(2), Zulfahrizal Zulfahrizal(3), Agus Arip Munawar(4*)

(1) Jurusan Teknik Pertanian, Universitas Syiah Kuala, Jl. T Hasan Krueng Kalee No. 3, Kopelma Darussalam, Banda Aceh 23111
(2) Jurusan Ilmu Tanah, Universitas Syiah Kuala, Jl. T Hasan Krueng Kalee No. 3, Kopelma Darussalam Banda Aceh
(3) Jurusan Teknik Pertanian, Universitas Syiah Kuala, Jl. T Hasan Krueng Kalee No. 3, Kopelma Darussalam, Banda Aceh 23111
(4) Department of Agricultural Engineering, Syiah Kuala University, Aceh
(*) Corresponding Author

Abstract


Plants need an ideal and healthy soil condition for their growth and a sufficient amount of soil macronutrients. To determine soil nutrients, several methods have been widely employed. Yet, most of them are based on solvent extraction, which is normally time-consuming, requires complicated sample preparation, and sometimes involves chemical materials. Thus, a novel, fast and simultaneous method is required as an alternative method used to predict soil macronutrients in a short period and without involving chemical materials. Near infrared spectroscopy (NIRS) can be considered for this need, since this method is fast, environmentally friendly, and non-destructive. Therefore, the main objective of this study is to apply an NIRS method to predict soil macronutrients (N, P, and K). The diffuse reflectance spectrum was acquired for soil samples in a wavelength range from 1000–2500 nm. Spectra data were corrected using a smoothing method whilst prediction models were developed using principal component regression (PCR) and partial least square regression (PLSR). Prediction accuracy and robustness were evaluated using these following statistical indicators: correlation coefficient (r), root mean square error (RMSEC) and residual predictive deviation (RPD). The results showed that NIRS was able to predict soil macronutrients simultaneously with a maximum correlation coefficient r = 0.97 for N prediction, r = 0.99 for P prediction, and r = 0.95 for K prediction. Thus, it may be concluded that an NIRS method is feasible to be applied as a novel, reliable and fast method to predict soil macronutrients (N, P, and K) simultaneously.

Keywords


Infrared; macronutrients; NIRS; prediction; soil



References

Chatterjee, S., Dey, N., & Sen, S. (2018). Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications. Sustainable Computing: Informatics and Systems. https://doi.org/10.1016/j.suscom.2018.09.002.

Christy, C. D. (2008). Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. Computers and Electronics in Agriculture, 61(1), 10–19. https://doi.org/10.1016/j.compag.2007.02.010.

Corradini, F., Bartholomeus, H., Huerta Lwanga, E., Gertsen, H., & Geissen, V. (2019). Predicting soil microplastic concentration using vis-NIR spectroscopy. Science of the Total Environment, 650, 922–932. https://doi.org/10.1016/j.scitotenv.2018.09.101.

Guo, L., Zhao, C., Zhang, H., Chen, Y., Linderman, M., Zhang, Q., & Liu, Y. (2017). Comparisons of spatial and non-spatial models for predicting soil carbon content based on visible and near-infrared spectral technology. Geoderma, 285, 280–292. https://doi.org/10.1016/j.geoderma.2016.10.010.

Jarmer, T., Vohland, M., Lilienthal, H., & Schnug, E. (2008). Estimation of Some Chemical Properties of an Agricultural Soil by Spectroradiometric Measurements. Pedosphere, 18(2), 163–170. https://doi.org/10.1016/s1002-0160(08)60004-1.

Kooistra, L., Wehrens, R., Buydens, L. M. C., Leuven, R. S. E. W., & Nienhuis, P. H. (2001). Possibilities of soil spectroscopy for the classification of contaminated areas in river floodplains. ITC Journal, 3(4), 337–344. https://doi.org/10.1016/S0303-2434(01)85041-8.

Ludwig, B., Schmilewski, G., & Terhoeven-Urselmans, T. (2006). Use of near infrared spectroscopy to predict chemical parameters and phytotoxicity of peats and growing media. Scientia Horticulturae, 109(1), 86–91. https://doi.org/10.1016/j.scienta.2006.02.020.

Martínez-España, R., Bueno-Crespo, A., Soto, J., Janik, L. J., & Soriano-Disla, J. M. (2018). Developing an intelligent system for the prediction of soil properties with a portable mid-infrared instrument. Biosystems Engineering, 7. https://doi.org/10.1016/j.biosystemseng.2018.09.013.

Mohamed, E. S., Saleh, A. M., Belal, A. B., & Gad, A. A. (2018). Application of near-infrared reflectance for quantitative assessment of soil properties. Egyptian Journal of Remote Sensing and Space Science, 21(1), 1–14. https://doi.org/10.1016/j.ejrs.2017.02.001.

Moros, J., Martínez-Sánchez, M. J., Pérez-Sirvent, C., Garrigues, S., & de la Guardia, M. (2009). Testing of the Region of Murcia soils by near infrared diffuse reflectance spectroscopy and chemometrics. Talanta, 78(2), 388–398. https://doi.org/10.1016/j.talanta.2008.11.041.

Mouazen, A. M., Kuang, B., De Baerdemaeker, J., & Ramon, H. (2010). Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy. Geoderma, 158(1–2), 23–31. https://doi.org/10.1016/j.geoderma.2010.03.001.

Munawar, A. A., Hörsten, D. V., Mörlein, D., Pawelzik, E., & Wegener, J. K. (2013). Rapid and non-destructive prediction of mango sweetness and acidity using near infrared spectroscopy. In Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI) (Vol. P-211).

Munawar, A. A., von Hörsten, D., Wegener, J. K., Pawelzik, E., & Mörlein, D. (2016). Rapid and non-destructive prediction of mango quality attributes using Fourier transform near infrared spectroscopy and chemometrics. Engineering in Agriculture, Environment and Food, 9(3). https://doi.org/10.1016/j.eaef.2015.12.004.

Nicolaï, B. M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K. I., & Lammertyn, J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology, 46(2), 99–118. https://doi.org/10.1016/j.postharvbio.2007.06.024.

Peltre, C., Thuriès, L., Barthès, B., Brunet, D., Morvan, T., Nicolardot, B., … Houot, S. (2011). Near infrared reflectance spectroscopy: A tool to characterize the composition of different types of exogenous organic matter and their behaviour in soil. Soil Biology and Biochemistry, 43(1), 197–205. https://doi.org/10.1016/j.soilbio.2010.09.036.

Shen, Z. Q., Shan, Y. J., Peng, L., & Jiang, Y. G. (2013). Mapping of total carbon and clay contents in glacial till soil using On-the-Go Near-Infrared reflectance spectroscopy and partial least squares regression. Pedosphere, 23(3), 305–311. https://doi.org/10.1016/S1002-0160(13)60020-X.

Soriano-Disla, J. M., Janik, L. J., Forrester, S. T., Grocke, S. F., Fitzpatrick, R. W., & McLaughlin, M. J. (2019). The use of mid-infrared diffuse reflectance spectroscopy for acid sulfate soil analysis. Science of the Total Environment, 646, 1489–1502. https://doi.org/10.1016/j.scitotenv.2018.07.383.

Vaknin, Y., Ghanim, M., Samra, S., Dvash, L., Hendelsman, E., Eisikowitch, D., & Samocha, Y. (2011). Predicting Jatropha curcas seed-oil content, oil composition and protein content using near-infrared spectroscopy-A quick and non-destructive method. Industrial Crops and Products, 34(1), 1029–1034. https://doi.org/10.1016/j.indcrop.2011.03.011

Wang, K., Zhao, Y., Yang, Z., Lin, Z., Tan, Z., Du, L., & Liu, C. (2018). Concentration and characterization of groundwater colloids from the northwest edge of Sichuan basin, China. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 537(September 2017), 85–91. https://doi.org/10.1016/j.colsurfa.2017.08.032.

Wang, L., Cheng, Y., Lamb, D., Dharmarajan, R., Chadalavada, S., & Naidu, R. (2019). Application of infrared spectrum for rapid classification of dominant petroleum hydrocarbon fractions for contaminated site assessment. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 207, 183–188. https://doi.org/10.1016/j.saa.2018.09.024.

Waruru, B. K., Shepherd, K. D., Ndegwa, G. M., & Sila, A. M. (2016). Estimation of wet aggregation indices using soil properties and diffuse reflectance near infrared spectroscopy: An application of classification and regression tree analysis. Biosystems Engineering, 152, 148–164. https://doi.org/10.1016/j.biosystemseng.2016.08.003.

Xing, Z., Tian, K., Du, C., Li, C., Zhou, J., & Chen, Z. (2019). Agricultural soil characterization by FTIR spectroscopy at micrometer scales: Depth profiling by photoacoustic spectroscopy. Geoderma, 335(August 2018), 94–103. https://doi.org/10.1016/j.geoderma.2018.08.003.

Xu, S., Zhao, Y., Wang, M., & Shi, X. (2018). Comparison of multivariate methods for estimating selected soil properties from intact soil cores of paddy fields by Vis–NIR spectroscopy. Geoderma, 310(September 2017), 29–43. https://doi.org/10.1016/j.geoderma.2017.09.013.

Zhao, L., Hong, H., Liu, J., Fang, Q., Yao, Y., Tan, W., … Algeo, T. J. (2017). Assessing the utility of visible-to-shortwave infrared reflectance spectroscopy for analysis of soil weathering intensity and paleoclimate reconstruction. Palaeogeography, Palaeoclimatology, Palaeoecology, 512, 80–94. https://doi.org/10.1016/j.palaeo.2017.07.007.



DOI: https://doi.org/10.22146/agritech.42430

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