Soil mapping for precision agriculture using support vector machines combined with inverse distance weighting

Springer Science and Business Media LLC - Tập 23 Số 4 - Trang 1189-1204 - 2022
Gustavo Willam Pereira1, Domingos Sárvio Magalhães Valente1, Daniel Marçal de Queiroz1, Nerílson Terra Santos2, Elpídio Inácio Fernandes Filho3
1Department of Agricultural Engineering, Universidade Federal de Viçosa (UFV), Viçosa, Brazil
2Department of Statistics, Universidade Federal de Viçosa (UFV), Viçosa, Brazil
3Department of Soils, Universidade Federal de Viçosa (UFV), Viçosa, Brazil

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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