Use of a triple-sensor fusion system for on-the-go measurement of soil compaction

Naderi-Boldaji, Mojtaba; Sharifi, Ahmad; Alimardani, Reza; Hemmat, Abbas; Keyhani, Alireza; Loonstra, E.H.; Weisskopf, Peter; Stettler, Matthias; Keller, Thomas

doi:10.1016/j.still.2012.10.002

Cited by 22 publications

(17 citation statements)

References 37 publications

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“…Sensors to measure soil strength can be used when mapping spatial variation in soil compaction, but they are affected by several other soil properties such as moisture, organic matter (SOM) content and texture. Because more information than measurements of soil strength is needed to understand soil compaction, much of the literature on combining proximal soil sensors relates to soil compaction measurements (Lui et al, 1996;Hummel et al, 2004;Mouazen & Ramon, 2006;Hemmat & Adamchuk, 2008;Kweon et al, 2009;Naderi-Boldaji et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

Wetterlind

Piikki

Stenberg

2015

European J Soil Science

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SummaryIn soil mapping, combining information from conceptually different proximal soil sensors can increase the accuracy of prediction and robustness of the model when compared with using individual sensors. In this study the predictability of soil texture (clay, silt and sand fractions) and soil organic matter (SOM) content was tested with a commercial integrated soil profiling tool that included sensors for measuring apparent electrical conductivity (EC a ), reflectance in the visible and near-infrared (vis-NIR) parts of the electromagnetic spectrum and insertion force (IF). The measurements were made at 20 locations on each of two Swedish farms. At every location, sensor measurements were made at 1.5-cm intervals from the soil surface to a depth of 0.8 m. Soil samples were collected close to the sensor measurement points and analysed for texture and SOM content. Farm-specific calibrations were developed for texture and SOM with each sensor separately and with combinations of all three sensors. The calibrations were made using both partial least squares regression (PLSR) and simple linear regression. The results for the two farms were quite consistent in terms of rank in prediction performance between the individual sensors and the sensor combinations. The vis-NIR spectrometer was the best individual sensor for predicting the soil properties tested on both farms, with root mean square error of cross-validation (RMSECV) of 0.3-0.5% for SOM, about 6% for clay and silt and 10-11% for sand. The inclusion of IF reduced the RMSECV for predictions of SOM content by about 10%. For soil texture, including EC a reduced the RMSECV on average for all particle size fractions by 5-10%. However, the small improvements obtained by combining sensors do not provide strong support for combining vis-NIR sensor measurements with measurements of EC a and or IF.

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Section: Introductionmentioning

confidence: 99%

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

Wetterlind

Piikki

Stenberg

2015

European J Soil Science

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“…potassium – K, uranium – U, thorium – Th and total count – TC) as demonstrated by Naderi‐Boldaji et al . ().…”

Section: Methodsmentioning

confidence: 99%

“…The final data matrix available consists of 43 188 measurements ( Figure 1c) and four windows (i.e. potassium -K, uranium -U, thorium -Th and total count -TC) as demonstrated by Naderi-Boldaji et al (2013).…”

Section: Ancillary Datamentioning

confidence: 99%

An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed γ‐ray spectrometer data

Huang

Bishop

Triantafilis

2017

Soil Use and Management

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The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time‐consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy‐to‐measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma‐ray (γ‐ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E‐BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is ~12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the γ‐ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the γ‐ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries.

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“…Most of the fields were located at the Agroscope research station in Zürich, one field was close to Solothurn and another was near Basel. In addition, a data set (48 measurement points) from a previous study (Naderi-Boldaji et al, 2013a) conducted near Bern, Switzerland, was included (F7, cf. Table 1).…”

Section: Field Measurementsmentioning

confidence: 99%

“…This can be achieved by a 'sensor fusion system' as demonstrated by e.g. Naderi-Boldaji et al (2013a).…”

Section: Introductionmentioning

confidence: 99%

Predicting the relative density from on-the-go horizontal penetrometer measurements at some arable top soils in Northern Switzerland

Naderi-Boldaji

Weisskopf

Stettler

et al. 2016

Soil and Tillage Research

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This study sought to develop empirical models to predict soil relative density (ρ rel ) from measurements of horizontal penetrometer resistance (PR) and soil water content (θ g ) in a wide range of soil textures. This permits the comparison of the state of soil compactness in different soil textures. It was hypothesised that model coefficients would be texture-dependent when soil compactness was expressed as bulk density (ρ d ) and that a model with constant coefficients could be obtained when soil compactness was expressed in terms of ρ rel (obtained as the ratio of ρ d to reference bulk density (ρ ref )). Field measurements were conducted in 2014 using a horizontal penetrometer at 0.25 m depth in 10 fields in Switzerland with a wide range of soil textures covering sandy loam, silt loam, loam, clay loam and clay (clay concentration, (CC) = 153-585 g kg -1 and organic matter concentration, (OM) = 9-168 g kg -1 ). At selected locations along the penetrometer measurement transects, cylindrical soil cores were sampled for determination of soil texture, OM, θ g and ρ d . Soil water potential and effective stress (σ') were also estimated for each location. Standard Proctor tests were performed on eight soils with variable textures.Proctor density was well described as a function of CC and OM (R 2 adj = 0.97, RMSE= 0.046 Mg m -3 ) and was used as reference density to obtain ρ rel . From this we developed a model for prediction of ρ rel from PR and σ' that allows comparisons between soils without changes in model coefficients. However, σ' cannot be obtained from on-the-go measurements a nd the model is therefore of limited value for soil compaction mapping. A model for estimating ρ rel from PR and θ g yielded satisfactory predictions (R 2 adj = 0.66, RMSE= 3.3%), although θ g is a texture-dependent measure of soil water that cannot be compared across soils. Moreover, ρ d was well predicted from PR and θ g (R 2 adj = 0.93, RMSE= 0.05 Mg m -3 ), possibly because all our measurements were carried out at similar soil water potential, which implies that θ g carries soil 3 textural information. Future research should test the proposed equations for a wide range of soil water potential values. The findings presented can be of use in developing measurement systems for mapping soil compactness that combine the proposed prediction functions with horizontal penetrometer and water content sensor systems.

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Use of a triple-sensor fusion system for on-the-go measurement of soil compaction

Cited by 22 publications

References 37 publications

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed γ‐ray spectrometer data

Predicting the relative density from on-the-go horizontal penetrometer measurements at some arable top soils in Northern Switzerland

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