Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study

Ji, Wenjun; Adamchuk, Viacheslav I.; Chen, Songchao; Su, Ahmad Suhaizi Mat; Ismail, Ashraf A.; Gan, Qianjun; Shi, Zhou; Biswas, Asim

doi:10.1016/j.geoderma.2019.01.006

Cited by 80 publications

(33 citation statements)

References 56 publications

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“…It can improve the prediction because it makes use of complementary information from different sensors (Mouazen, Alhwaimel, Kuang, & Waine, ). Different methods have been used, including (a) directly concatenating various sensor data (Viscarra Rossel, Walvoort, McBratney, Janik, & Skjemstad, ), (b) converting sensor data using principal component analysis (PCA) and then concatenating principal components (PCs) (Ji et al, ), (c) using outer product analysis (OPA) to fuse different spectra (Terra, Viscarra Rossel, & Demattê, ; Xu, Chen, et al, ), (d) different sensors used as fixed effects and random effects respectively (Cardelli et al, ; Wang et al, ) and (e) model averaging to combine model results developed from individual sensors (O'Rourke, Minasny, Holden, & McBratney, ; O'Rourke, Stockmann, et al, ; Terra et al, ; Xu, Chen, et al, ; Xu, Zhao, et al, ).…”

Section: Introductionmentioning

confidence: 99%

Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties

Zhang

Hartemink

2019

European J Soil Science

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We tested one front-end data fusion method to combine visible near-infrared (vis-NIR) and portable X-ray fluorescence (PXRF) spectra for predicting different soil properties and investigated the contribution of different sensor data. A total of 197 soil samples were collected from 25 Alfisols and Mollisols in south-central Wisconsin, USA. Soils were analysed in the laboratory for clay, sand, silt content, total carbon (TC), total nitrogen (TN) and pH. Air-dried soil samples were scanned with vis-NIR and PXRF spectrometers. A principal component analysis (PCA) was applied to each of the vis-NIR and PXRF spectra to extract the first 10 principal components (PCs), which were used in the Cubist model. Five types of input data were compared for constructing the models using a front-end data fusion approach, including: (a) 10 PCs from vis-NIR spectra, (b) 10 PCs from spectra of PXRF beam 1 (XRF40), (c) 10 PCs from spectra of PXRF beam 2 (XRF10), (d) concatenating 20 PCs from two PXRF spectra (XRF40 + 10), and (e) concatenating 30 PCs from two PXRF spectra and vis-NIR spectra (XRF40 + 10 + NIR). In addition, the performances of the preprocessing methods (four smoothing methods with or without background removal) of PXRF spectra were compared. Multiple linear regression was also used to predict soil properties directly from 11 PXRF-estimated elements. Our results suggest that smoothing should be applied to the PXRF spectra prior to developing predictive models.PXRF spectra and elemental data can predict soil texture with validation R 2 > 0.85, better than using solely vis-NIR spectra, and combining vis-NIR and PXRF spectra improved the prediction. Combining PXRF and vis-NIR can also predict TC and TN moderately well. Soil pH cannot be predicted from vis-NIR or PXRF spectra in this dataset due to its weak correlations with other soil properties and elements. It is concluded that PXRF spectra can be solely used to estimate soil texture, whereas combining vis-NIR and PXRF spectra via PCA should be used to estimate TC and TN. Highlights• The use of front-end data fusion method to combine vis-NIR and PXRF spectra for predicting soil properties.

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Section: Introductionmentioning

confidence: 99%

Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties

Zhang

Hartemink

2019

European J Soil Science

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“…Therefore, the data fusion method has been applied to effectively improve the performance of the measurement system in various fields [34]. One of the areas that actively use data fusion is precision agriculture [23,35] and data fusion has been applied using various analytical sensors to establish soil mapping and analyze the nutrient status of soil [23,36,37].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Spectroscopy and Cobalt Electrochemistry Data for Estimating Phosphate Concentration in Hydroponic Solution

Kim

Choi

Kim

et al. 2019

Sensors

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Phosphate is a key element affecting plant growth. Therefore, the accurate determination of phosphate concentration in hydroponic nutrient solutions is essential for providing a balanced set of nutrients to plants within a suitable range. This study aimed to develop a data fusion approach for determining phosphate concentrations in a paprika nutrient solution. As a conventional multivariate analysis approach using spectral data, partial least squares regression (PLSR) and principal components regression (PCR) models were developed using 56 samples for calibration and 24 samples for evaluation. The R2 values of estimation models using PCR and PLSR ranged from 0.44 to 0.64. Furthermore, an estimation model using raw electromotive force (EMF) data from cobalt electrodes gave R2 values of 0.58–0.71. To improve the model performance, a data fusion method was developed to estimate phosphate concentration using near infrared (NIR) spectral and cobalt electrochemical data. Raw EMF data from cobalt electrodes and principle component values from the spectral data were combined. Results of calibration and evaluation tests using an artificial neural network estimation model showed that R2 = 0.90 and 0.89 and root mean square error (RMSE) = 96.70 and 119.50 mg/L, respectively. These values are sufficiently high for application to measuring phosphate concentration in hydroponic solutions.

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“…To overcome the limitations of a single sensor in regional environmental monitoring, multi-sensor data fusion technology has been widely used in many environmental target monitoring processes [20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

Temperature Sequential Data Fusion Algorithm Based on Cluster Hierarchical Sensor Networks

Yang

Nan

Jin

2020

Sensors

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The process of extracting gold by biological oxidation involves oxidizing the refractory high-sulfur and high-arsenic ore with the help of bacteria to decompose the wrapping material of gold to extract the gold. Therefore, maximizing the activity of bacteria will directly affect the efficiency of gold extraction, for which it is particularly important to maintain the pulp temperature in the oxidation tank at the optimal bacteria breeding temperature. However, gold mines are generally located in mountainous areas, and the large temperature difference between day and night in winter, coupled with the influence of wind and snow, creates variations in the temperature in the oxidation tank. The traditional temperature measurement method cannot fully reflect the temperature change of the oxidation tank. As a multi-field application method, sensor information fusion can effectively address the problem of pulp temperature measurement. First, we analyzed the heat transfer principle inside the oxidation tank, and designed the cluster hierarchical sensor network according to the spatial position of each oxidation tank and the environmental interference factors. The network structure is divided into three layers; the bottom of the sensor to collect pulp temperature data shows a spiral distribution in the inner wall of the oxidation tank. Each cluster head node sensor is used as an intermediate layer to complete local measurement fusion estimation. Finally, the fusion center is taken as the upper layer to realize the global state fusion estimation. Secondly, in the data processing of the bottom temperature sensor, the traditional unscented Kalman filter (UKF) algorithm is improved and the fading memory matrix is added to improve the identification of nonlinear modeling errors. The sequential observation fusion estimator (SOFE) algorithm is embedded in the measurement update to improve the performance of local measurement fusion. Finally, in the global state fusion estimation, the sequential analysis is combined with the inverse covariance intersection, and the sequential analysis and inverse covariance intersection-global state fusion estimation (SICI-GSFE) algorithm is proposed. Through calculation and simulation, the results show that the external interference can be reduced by combining all the temperature state estimations, and the accuracy of the best global temperature state estimation is improved.

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Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study

Cited by 80 publications

References 56 publications

Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties

Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties

Fusion of Spectroscopy and Cobalt Electrochemistry Data for Estimating Phosphate Concentration in Hydroponic Solution

Temperature Sequential Data Fusion Algorithm Based on Cluster Hierarchical Sensor Networks

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