Abstract
Kriging has been shown to be the best interpolator to interpolate maps in precision agriculture. However, Kriging requires a high number of sampling points to generate accurate maps. Recently, machine learning (ML) techniques have shown the potential to produce maps with a lower number of sampling points. In addition, using ML map generation can be automated and use much more feature information to improve map quality. Therefore, the objective of this study was to implement a ML technique and compare it to IDW and to Ordinary Kriging (OK). The ML algorithm used was the Support Vector Machine (SVM). Software based on the SVM method was developed (Smart-Map) using the Python language. This software was tested in an area of 204 ha cultivated with soybeans. The performance of the SVM method was compared to traditional interpolation methods, IDW and Ordinary Kriging (OK). Based on the analysis of 10 soil attributes, OK had better performance than IDW and the ML method when the Moran’s I (Index) values were significant and higher than 0.67. With a low density of points and low degrees of spatial autocorrelation, the ML method performed better than IDW and OK.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Brungard, C. W., Boettinger, J. L., Duniway, M. C., Wills, S. A., & Edwards, T. C. (2015). Machine learning for predicting soil classes in three semi-arid landscapes. Geoderma, 239, 68–83. https://doi.org/10.1016/j.geoderma.2014.09.019
Carré, F., McBratney, A. B., & Minasny, B. (2007). Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping. Geoderma, 141(1–2), 1–14. https://doi.org/10.1016/j.geoderma.2007.01.018
Celisse, A., & Robin, S. (2008). Nonparametric density estimation by exact leave-p-out cross-validation. Computational Statistics and Data Analysis, 52(5), 2350–2368. https://doi.org/10.1016/j.csda.2007.10.002
Chen, S., Wang, S., Shukla, M. K., Wu, D., Guo, X., Li, D., & Du, T. (2019). Delineation of management zones and optimization of irrigation scheduling to improve irrigation water productivity and revenue in a farmland of Northwest China. Precision Agriculture, 21(3), 655–677. https://doi.org/10.1007/s11119-019-09688-0
Coelho, A. L. F., Queiroz, D. M., Valente, D. S. M., de Pinto, F., & A. de C. (2018). An open-source spatial analysis system for embedded systems. Computers and Electronics in Agriculture, 154, 289–295. https://doi.org/10.1016/j.compag.2018.09.019
Cressie, N. (1990). The Origins of Kriging. Mathematical Geology, 22(3), 239–252. https://doi.org/10.1007/BF00889887
da Matta Campbell, P. M., Francelino, M. R., Filho, E. I. F., de Azevedo Rocha, P., & de Azevedo, B. C. (2019). Digital mapping of soil attributes using machine learning. Revista Ciencia Agronomica, 50(4), 519–528. https://doi.org/10.5935/1806-6690.20190061
Gama Design Software. (2000). GS+ User´s Guide Version 5. Gamma Design Software. Plainwell.
Giacomin, G., Carvalho, M. B., de Santos, A., & P., Medeiros, N. das G., & Ferraz, A. S. (2014). Comparative analysis of interpolation methods for surface models. Revista Brasileira De Cartografia, 66, 1315–1329.
Gomes, L. C., Faria, R. M., de Souza, E., Veloso, G. V., Schaefer, C. E. G. R., & Filho, E. I. F. (2019). Modelling and mapping soil organic carbon stocks in Brazil. Geoderma, 340, 337–350. https://doi.org/10.1016/j.geoderma.2019.01.007
Gregorutti, B., Michel, B., & Saint-Pierre, P. (2017). Correlation and variable importance in random forests. Statistics and Computing, 27(3), 659–678. https://doi.org/10.1007/s11222-016-9646-1
Groher, T., Heitkämper, K., Walter, A., & Liebisch, F. (2020). Status quo of adoption of precision agriculture enabling technologies in Swiss plant production. Precision Agriculture, 21(6), 1327–1350. https://doi.org/10.1007/s11119-020-09723-5
Guo, P. T., Li, M. F., Luo, W., Tang, Q. F., Liu, Z. W., & Lin, Z. M. (2015). Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach. Geoderma, 237–238, 49–59. https://doi.org/10.1016/j.geoderma.2014.08.009
Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., & Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6, e5518. https://doi.org/10.7717/peerj.5518
Heung, B., Bulmer, C. E., & Schmidt, M. G. (2014). Predictive soil parent material mapping at a regional-scale: A Random Forest approach. Geoderma, 214–215, 141–154. https://doi.org/10.1016/j.geoderma.2013.09.016
Heung, B., Ho, H. C., Zhang, J., Knudby, A., Bulmer, C. E., & Schmidt, M. G. (2016). An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma, 265, 62–77. https://doi.org/10.1016/j.geoderma.2015.11.014
Khaledian, Y., & Miller, B. A. (2020). Selecting appropriate machine learning methods for digital soil mapping. Applied Mathematical Modelling, 81, 401–418. https://doi.org/10.1016/j.apm.2019.12.016
Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259–263. https://doi.org/10.1127/0941-2948/2006/0130
Legendre, P., & Fortin, M.-J. (1989). Spatial pattern and ecological analysis. Vegetatio, 80, 107–138.
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors (switzerland), 18(8), 1–29. https://doi.org/10.3390/s18082674
Martins, R. N., Valente, D. S. M., Rosas, J. T. F., Santos, F. S., Dos Santos, F. F. L., & Nascimento, M. (2020). Site-specific nutrient management zones in soybean field using multivariate analysis: an approach based on variable rate fertilization. Communications in Soil Science and Plant Analysis, 51(5), 687–700. https://doi.org/10.1080/00103624.2020.1729793
McKay, M. D., Beckman, R. J., & Conover, W. J. (1979). Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 42(1), 239–245.
Minasny, B., & McBratney, a. B. (2007). Latin hipercube sampling as tool for digital soil mapping. Developments in Soil Science, 31(1997), 153–606. https://doi.org/10.1016/S0166-2481(06)31012-4
Moharana, P. C., Jena, R. K., Pradhan, U. K., Nogiya, M., Tailor, B. L., & Singh, R. S. (2020). Geostatistical and fuzzy clustering approach for delineation of site-specific management zones and yield-limiting factors in irrigated hot arid environment of India. Precision Agriculture, 21(2), 426–448. https://doi.org/10.1007/s11119-019-09671-9
Moral, F. J., Rebollo, F. J., & Serrano, J. M. (2020). Delineating site-specific management zones on pasture soil using a probabilistic and objective model and geostatistical techniques. Precision Agriculture, 21(3), 620–636. https://doi.org/10.1007/s11119-019-09686-2
Mulder, V. L., de Bruin, S., & Schaepman, M. E. (2012). Representing major soil variability at regional scale by constrained Latin Hypercube Sampling of remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 21(1), 301–310. https://doi.org/10.1016/j.jag.2012.07.004
Nabiollahi, K., Taghizadeh-Mehrjardi, R., Shahabi, A., Heung, B., Amirian-Chakan, A., Davari, M., & Scholten, T. (2021). Assessing agricultural salt-affected land using digital soil mapping and hybridized random forests. Geoderma, 385, 114858. https://doi.org/10.1016/j.geoderma.2020.114858
Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., et al. (2018). Evaluation of digital soil mapping approaches with large sets of environmental covariates. The Soil, 4(1), 1–22. https://doi.org/10.5194/soil-4-1-2018
Nyéki, A., Kerepesi, C., & B, D., Benczúr, A., & Nagy, G. M. J. (2021). Application of spatio - temporal data in site - specific maize yield prediction with machine learning methods. Precision Agriculture. https://doi.org/10.1007/s11119-021-09833-8
Oliver, M., & Webster, R. (1990). Kriging: A method of interpolation for geographical information systems. International Journal ofGeographical Information System, 4(3), 313–332. https://doi.org/10.1080/02693799008941549
Olsson, A. M. J., & Sandberg, G. E. (2002). Latin hypercube sampling for stochastic finite element analysis. Journal of Engineering Mechanics, 128(1), 121–125.
Olsson, A., Sandberg, G., & Dahlblom, O. (2003). On Latin hypercube sampling for structural reliability analysis. Structural Safety, 25(1), 47–68.
Parmley, K. A., Higgins, R. H., Ganapathysubramanian, B., Sarkar, S., & Singh, A. K. (2019). Machine learning approach for prescriptive plant breeding. Scientific Reports, 9(1), 17132. https://doi.org/10.1038/s41598-019-53451-4
QGIS Development Team. (2018). QGIS Geographic Information System. Open Source Geoespacial Found. Proj. QGIS Development Team. 2018.
Rey, S. J., & Anselin, L. (2010). PySAL: A Python Library of Spatial Analytical Methods. In M. Fischer & A. Getis (Eds.), Handbook of Applied Spatial Analysis. Berlin, Heidelberg: Springer.
Sekulić, A., Kilibarda, M., Heuvelink, G. B. M., Nikolić, M., & Bajat, B. (2020). Random Forest Spatial Interpolation. Remote Sensing, 12(10), 1–29. https://doi.org/10.3390/rs12101687
Shaddad, S. M., Madrau, S., Castrignanò, A., & Mouazen, A. M. (2016). Data fusion techniques for delineation of site-specific management zones in a field in UK. Precision Agriculture, 17(2), 200–217. https://doi.org/10.1007/s11119-015-9417-6
Shields, M. D., & Zhang, J. (2016). The generalization of Latin hypercube sampling. Reliability Engineering and System Safety, 148, 96–108. https://doi.org/10.1016/j.ress.2015.12.002
Trangmar, B. B., Yost, R. S., & Uehara, G. (1985). Applications of geostatistics to spatial studies of soil properties. Advances in Agronomy, 38(1), 45–94. https://doi.org/10.1016/S0065-2113(08)60673-2
Vallentin, C., Dobers, E. S., Itzerott, S., Kleinschmit, B., & Spengler, D. (2020). Delineation of management zones with spatial data fusion and belief theory. Precision Agriculture, 21(4), 802–830. https://doi.org/10.1007/s11119-019-09696-0
Webster, R., & Oliver, M. A. (1992). Sample adequately to estimate variograms of soil properties. Journal of Soil Science, 43, 177–192. https://doi.org/10.5771/0038-6073-2013-1-2-191
Webster, R., & Oliver, M. (2001). Geostatistics for Environmental Scientists. John Wiley & Sons.
Were, K., Bui, D. T., Dick, Ø. B., & Singh, B. R. (2015). A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, 52, 394–403. https://doi.org/10.1016/j.ecolind.2014.12.028
Wong, D. W. S. (2017). Interpolation: Inverse-distance weighting. International Encyclopedia of Geography People the Earth Environment and Technology. https://doi.org/10.1002/9781118786352.wbieg0066
WRB-IUSS. (2015). World reference base for soil resources 2014, update 2015: International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106. Rome: Food and Agriculture Organization of the United Nations.
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, 29–43. https://doi.org/10.1016/j.geoderma.2017.09.013
Yost, R. S., Uehara, G., & Fox, R. L. (1982). Geostatistical analysis of soil chemical properties of large land areas. II. Kriging. Soil Science Society of America Journal, 46(5), 1033–1037. https://doi.org/10.2136/sssaj1982.03615995004600050029x
Acknowledgements
The authors thank the CNPq (National Counsel for Scientific and Technological Development of Brazil), CAPES (Coordination for the Improvement of Higher Education Personnel—Finance Code 001) and FAPEMIG (Minas Gerais Research Funding Foundation) for supporting this work.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pereira, G.W., Valente, D.S.M., de Queiroz, D.M. et al. Soil mapping for precision agriculture using support vector machines combined with inverse distance weighting. Precision Agric 23, 1189–1204 (2022). https://doi.org/10.1007/s11119-022-09880-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11119-022-09880-9