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Evaluation of soil sensor fusion for mapping macronutrients and soil pH. Schirrmann, M., Gebbers, R., Kramer, E., & Seidel, J. Applied regression analysis: A research tool (2nd ed.). Vienna, Austria: R Foundation for Statistical Computing. R: A language and environment for statistical computing. Montreal, Canada: McGill University Press. Viscarra Rossel (Eds.), Proceedings of the second global workshop on proximal soil sensing, Canada (pp. Sensor data fusion for topsoil clay mapping of an agricultural field. Piikki, K., Söderström, M., & Stenberg, B. Multi-sensor data fusion for supervised land-cover classification using Bayesian and geostatistical techniques. The efficiency of various approaches to obtaining estimates of soil hydraulic properties. Identifying optimal spectral bands to assess soil properties with VNIR radiometry in semi-arid soils. Melendez-Pastor, I., Navarro-Pedreño, J., Gómez, I., & Koch, M. Mississauga, Ontario, Canada: Geonics Limited. Electromagnetic terrain conductivity measurement at low induction numbers. Stafford (Ed.), Proceedings of the 1st European conference on precision agriculture, England (pp. Spatial variability in soil-implications for precision agriculture. 423–430).The Netherlands: Wageningen Academic Publishers. Huijsmans (Eds.), Precision agriculture 2009: Proceedings of the 7th European conference on precision agriculture, The Netherlands (pp.
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Combined sensor system for mapping soil properties. Sensing soil properties in the laboratory, in situ and on-line-A review. International Journal of Remote Sensing, 25(2), 455–469. Effectiveness of spectroscopy in identification of swelling indicator clay minerals. Robust methods for partial least squares regression. Springsteen (Eds.), Applied spectroscopy: A compact reference for practitioners (pp.
#Target variable combine pasw statistics 18 manual#
Rencz (Ed.), Remote sensing for the earth sciences: Manual of remote sensing (3rd ed., Vol. Spectroscopy of rocks and minerals, and principles of spectrosocpy. Partial least squares regression as an alternative to current regression methods used in ecology. Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana. 267–281) Budapest, Hungary: Akademia Kiado.īrown, D. Coaki (Eds.), Second international symposium on information theory, Hungary (pp. Information theory and an extension of maximum likelihood principle. Thomas (Ed.), Sensor fusion-foundation and applications (pp. More efficient statistical data analysis methods are needed to handle a large volume of data effectively from multiple sensors for sensor data fusion.Īdamchuck, V. It is concluded that sensor data fusion can enhance the quality of soil sensing in precision agriculture once a proper set of sensors has been selected for fusion to estimate desired soil properties. The best data fusion results were found in a clayey field and the worst in a sandy field. Among data fusion methods, PLSR outperformed both SMLR and PCA + SMLR methods because it proved to have a better ability to deal with the multi-collinearity among the predictor variables of both sensors. The accuracy of predictions of TOC, TN and CN was also improved in some cases, but was not consistent in all fields. It was found that soil property models based on fusion methods significantly improved the accuracy of predictions of soil properties measureable by both sensors, such as clay, silt, sand, EC and pH from those based on either of the individual sensors. Soil properties investigated for data fusion included soil texture (clay, silt and sand), EC, pH, total organic carbon (TOC), total nitrogen (TN) and carbon to nitrogen ratio (CN).
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Stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and principal components analysis combined with stepwise multiple linear regression (PCA + SMLR) methods were used in three different fields. In this study, a data fusion was performed of a Vis–NIR spectrometer and an EM38 sensor for multiple soil properties. Sensor data fusion can potentially overcome this inability of a single sensor and can best extract useful and complementary information from multiple sensors or sources. The accuracy of a single sensor is often low because all proximal soil sensors respond to more than one soil property of interest.