Current Science

Open Access Open Access  Restricted Access Subscription Access

Comparison of Data Mining Approaches for Estimating Soil Nutrient Contents Using Diffuse Reflectance Spectroscopy


Affiliations
1 International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP-320, Mali
2 Indian Institute of Technology Kharagpur, Kharagpur 721 302, India
3 International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India
 

Diffuse reflectance spectroscopy (DRS) operating in wavelength range of 350-2500 nm is emerging as a rapid and non-invasive approach for estimating soil nutrient content. The success of the DRS approach relies on the ability of the data mining algorithms to extract appropriate spectral features while accounting for non-linearity and complexity of the reflectance spectra. There is no comparative assessment of spectral algorithms for estimating nutrient content of Indian soils. We compare the performance of partialleast- squares regression (PLSR), support vector regression (SVR), discrete wavelet transformation (DWT) and their combinations (DWT-PLSR and DWT-SVR) to estimate soil nutrient content. The DRS models were generated for extractable phosphorus (P), potassium (K), sulphur (S), boron (B), zinc (Zn), iron (Fe) and aluminium (Al) content in Vertisols and Alfisols and were compared using residual prediction deviation (RPD) of validation dataset. The best DRS models yielded accurate predictions for P (RPD = 2.27), Fe (RPD = 2.91) in Vertisols and Fe (RPD = 2.43) in Alfisols, while B (RPD = 1.63), Zn (RPD = 1.49) in Vertisols and K (RPD = 1.89), Zn (RPD = 1.41) in Alfisols were predicted with moderate accuracy. The DWT-SVR outperformed all other approaches in case of P, K and Fe in Vertisols and P, K and Zn in Alfisols; whereas, the PLSR approach was better for B, Zn and Al in Vertisols and B, Fe and Al in Alfisols. The DWT-SVR approach yielded parsimonious DRS models with similar or better prediction accuracy than PLSR approach. Hence, the DWT-SVR may be considered as a suitable data mining approach for estimating soil nutrients in Alfisols and Vertisols of India.

Keywords

Diffuse Reflectance Spectroscopy, Discrete Wavelet Transformation, Partial-Least-Squares Regression, Soil Nutrient Contents, Support Vector Regression.
User
Notifications
Font Size

  • Ben-Dor, E. and Banin, A., Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. Soil Sci. Soc. Am. J., 1995, 59, 364–372.

  • Chang, C., Laird, D. A., Mausbach, M. J. and Hurburgh, C. R., Near-infrared reflectance spectroscopy–principal components regression analyses of soil properties. Soil Sci. Soc. Am. J., 2001, 65, 480–490.

  • Sarathjith, M. C., Das, B. S., Wani, S. P. and Sahrawat, K. L., Dependency measures for assessing the covariation of spectrally active and inactive soil properties. Soil Sci. Soc. Am. J., 2014, 78, 1522–1530.

  • Brown, D. J., Shepherd, K. D., Walsh, M. G., Mays, M. D. and Reinsch, T. G., Global soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma, 2006, 132, 273–290.

  • Saxena, R. K., Vermal, K. S., Srivastava, R., Av, A. K. B., Shiwalkar, A. A. and Londhel, S. L., Spectral reflectance properties of some dominant soils occurring on different altitudinal zones in Uttaranchal Himalayas. Agropedology, 2003, 13, 35–43.

  • Srivastava, R., Prasad, J. and Saxena, R., Spectral reflectance properties of some shrink-swell soils of Central India as influenced by soil properties. Agropedology, 2004, 14, 45–54.

  • Santra, P., Sahoo, R. N., Das, B. S., Samal, R. N., Pattanaik, A. K.and Gupta, V. K., Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwaveinfrared (VIS–NIR–SWIR) region. Geoderma, 2009, 152, 338–349.

  • Sarathjith, M. C., Das, B. S., Vasava, H. B., Mohanty, B., Sahadevan, A. S., Wani, S. P. and Sahrawat, K. L., Diffuse reflectance spectroscopic approach for the characterization of soil aggregate size distribution. Soil Sci. Soc. Am. J., 2014, 78, 369– 376.

  • Srivastava, R., Sarkar, D., Mukhopadhayay, S. S., Sood, A., Singh, M., Nasre, R. A. and Dhale, S. A., Development of hyperspectral model for rapid monitoring of soil organic carbon under precision farming in the Indo-Gangetic Plains of Punjab, India. J. Indian Soc. Remote Sensing, 2015, 43(4), 1–9.

  • Das, B. S., Sarathjith, M. C., Santra, P., Sahoo, R. N., Srivastava, R., Routray, A. and Ray, S. S., Hyperspectral remote sensing: opportunities, status and challenges for rapid soil assessment in India. Curr. Sci., 2015, 108(5), 860.

  • Sherman, D. and Waite, T., Electronic spectra of Fe (super 3+) oxides and oxide hydroxides in the near IR to near UV. Am. Mineral, 1985, 70, 1262–1269.

  • Viscarra Rossel, R. A. and Behrens, T., Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma, 2010, 158, 46–54.

  • Malley, D. and Yesmin, L., Application of near-infrared spectroscopy in analysis of soil mineral nutrients. Commun. Soil Sci. Plant Anal., 1999, 30, 999–1012.

  • Viscarra Rossel, R. A., Walvoort, D. J. J., McBratney, A. B., Janik, L. J. and Skjemstad, J. O., Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma, 2006, 131, 59-75.

  • Shepherd, K. and Walsh, M., Development of reflectance spectral libraries for characterization of soil properties. Soil Sci. Soc. Am. J., 2002, 66, 988–998.

  • Mouazen, A. M., Kuang, B., De Baerdemaeker, J. and Ramon, H., 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, 2010, 158, 23–31.

  • Friedman, J. H., Greedy function approximation: a gradient boosting machine. Ann. Stat., 2001, 29, 1189–1232.

  • Boulesteix, A. and Strimmer, K., Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief.Bioinform., 2007, 8, 32–44.

  • Vohland, M., Besold, J., Hill, J. and Fründ, H., Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy. Geoderma, 2011, 166, 198–205.

  • Viscarra Rossel, R. A. and Lark, R. M., Improved analysis and modelling of soil diffuse reflectance spectra using wavelets. Eur. J. Soil Sci., 2009, 60, 453–464.

  • Sahadevan, A. S., Shrivastava, P., Das, B. S. and Sarathjith, M. C., Discrete wavelet transform approach for the estimation of crop residue mass from spectral reflectance. IEEE J. Sel. Top. Appl

  • Earth Observ. Remote Sensing, 2014, 7(6), 2490–2495.

  • Lotse, E. G., Datta, N. P., Tomar, K. P. and Motsara, K. P., Mineralogical composition of some red and black soils of India. In Proceedings of the National Science Academy, Springer, 1972, pp.216–226.

  • Wold, S., Martens, H. and Wold, H., The Multivariate Calibration Problem in Chemistry Solved by the PLS Method. Springer, Berlin, Heidelberg, 1983, pp. 286–293.

  • Santra, P., Singh, R. and Sarathjith, M., Reflectance spectroscopic approach for estimation of soil properties in hot arid western Rajasthan, India. Environ. Earth, 2015, 1–43.

  • Vapnik, V., Golowich, S. E. and Smola, A., Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems 9, 1996, pp. 281–287.

  • Ramirez-Lopez, L., Schmidt, K., Behrens, T., van Wesemael, B., Dematte, J. A. and Scholten, T., Sampling optimal calibration sets in soil infrared spectroscopy. Geoderma, 2014, 226, 140–150.

  • Daubechies, I., Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math., 1988, 41(7), 909–996.

  • Trygg, J. and Wold, S., PLS regression on wavelet compressed NIR spectra. Chemometr. Intell. Lab. Syst., 1998, 42(1), 209–220.

  • Abdi, D., Tremblay, G.F., Ziadi, N., Belanger, G. and Parent, L.-E., Predicting soil phosphorus-related properties using nearinfrared reflectance spectroscopy. Soil Sci. Soc. Am. J., 2012, 76, 2318–2326.


Abstract Views: 430

PDF Views: 132




  • Comparison of Data Mining Approaches for Estimating Soil Nutrient Contents Using Diffuse Reflectance Spectroscopy

Abstract Views: 430  |  PDF Views: 132

Authors

M. C. Sarathjith
International Crops Research Institute for the Semi-Arid Tropics, Bamako, BP-320, Mali
B. S. Das
Indian Institute of Technology Kharagpur, Kharagpur 721 302, India
S. P. Wani
International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India
K. L. Sahrawat
International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Hyderabad 502 324, India
A. Gupta
Indian Institute of Technology Kharagpur, Kharagpur 721 302, India

Abstract


Diffuse reflectance spectroscopy (DRS) operating in wavelength range of 350-2500 nm is emerging as a rapid and non-invasive approach for estimating soil nutrient content. The success of the DRS approach relies on the ability of the data mining algorithms to extract appropriate spectral features while accounting for non-linearity and complexity of the reflectance spectra. There is no comparative assessment of spectral algorithms for estimating nutrient content of Indian soils. We compare the performance of partialleast- squares regression (PLSR), support vector regression (SVR), discrete wavelet transformation (DWT) and their combinations (DWT-PLSR and DWT-SVR) to estimate soil nutrient content. The DRS models were generated for extractable phosphorus (P), potassium (K), sulphur (S), boron (B), zinc (Zn), iron (Fe) and aluminium (Al) content in Vertisols and Alfisols and were compared using residual prediction deviation (RPD) of validation dataset. The best DRS models yielded accurate predictions for P (RPD = 2.27), Fe (RPD = 2.91) in Vertisols and Fe (RPD = 2.43) in Alfisols, while B (RPD = 1.63), Zn (RPD = 1.49) in Vertisols and K (RPD = 1.89), Zn (RPD = 1.41) in Alfisols were predicted with moderate accuracy. The DWT-SVR outperformed all other approaches in case of P, K and Fe in Vertisols and P, K and Zn in Alfisols; whereas, the PLSR approach was better for B, Zn and Al in Vertisols and B, Fe and Al in Alfisols. The DWT-SVR approach yielded parsimonious DRS models with similar or better prediction accuracy than PLSR approach. Hence, the DWT-SVR may be considered as a suitable data mining approach for estimating soil nutrients in Alfisols and Vertisols of India.

Keywords


Diffuse Reflectance Spectroscopy, Discrete Wavelet Transformation, Partial-Least-Squares Regression, Soil Nutrient Contents, Support Vector Regression.

References





DOI: https://doi.org/10.18520/cs%2Fv110%2Fi6%2F1031-1037