Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Proximal Soil Sensing

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 December 2018) | Viewed by 26955

Share This Special Issue

Special Issue Editors

Dr. Raphael Viscarra Rossel

E-Mail Website
Guest Editor

Professor of Digital Soil Science & Agriculture, School of Molecular and Life Sciences, Curtin University, Kent St, Bentley, WA 6102, Australia
Interests: pedometrics; soil sensing; soil spectroscopy; data fusion; spatial modelling; digital soil mapping

Dr. Craig R. Lobsey

E-Mail Website
Guest Editor

The University of Southern Queensland, Toowoomba, QLD, Australia
Interests: proximal sensing; spectroscopy; precision agriculture; spatial modelling; software development; instrumentation and electronic engineering

Special Issue Information

Dear Colleagues,

Our current understanding of dynamic soil processes is constrained by a lack of efficient measurement capabilities for key soil properties (e.g. nutrients, carbon, biology, water, pH) across space and in time. The development of proximal soil sensing is essential for the dynamic characterisation of soil to help advance our current understanding of such processes and for monitoring them. Recent technological advances in miniaturised, low-power, sensors that are also wireless show considerable promise. Thus, for this special issue we welcome reviews and original research articles on the following topics:

New soil sensor technologies for sensing biological, physical, and chemical soil properties;
Development of integrated multi-sensor systems for monitoring soil condition and function (or soil health);
Subterranean wireless sensor systems used for monitoring biological, physical, and chemical soil properties;
Sensor data analytics, including signal processing, sampling, multivariate calibration, machine learning, Bayesian modelling, multi-sensor data fusion;
Novel applications of proximal soil sensing in environmental, agronomic, engineering, robotic, archaeologic, remote sensing and space applications;
Use of proximal soil sensing data in processed-based models at different spatial and temporal scales.

Proximal soil sensing refers to the development and use of sensors in the field, which obtain signals from soil when the sensor’s detector is in contact with or close to (within 2 m) the soil (Viscarra Rossel et al. 2011: Advances in Agronomy, Vol. 113). This definition precludes remote sensing and laboratory measurements of soil properties with benchtop instruments, although, we acknowledge that combining proximal with remote sensing may be advantageous in some applications; and that the development of many proximal sensors begins in the laboratory.

Dr. Raphael Viscarra Rossel
Dr. Craig R. Lobsey
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Proximal soil sensing
Sensing with electromagnetic frequencies: gamma, x-ray, ultraviolet, visible, infrared, terahertz, microwave and radio
Electrochemical and mechanical sensing
Microelectromechanical systems (MEMS)
Multi-sensor systems
Spectroscopy
Signal processing
Multivariate and Bayesian statistics
Sensor fusion
Environmental monitoring
Precision agriculture
Sensor networks
Wireless sensors
Subterranean sensing
Soil biology

Published Papers (4 papers)

Download All Papers

Research

15 pages, 1534 KiB

Open AccessArticle

Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe

by Xiaoshuai Pei, Kenneth A. Sudduth, Kristen S. Veum and Minzan Li

Sensors 2019, 19(5), 1011; https://doi.org/10.3390/s19051011 - 27 Feb 2019

Cited by 22 | Viewed by 5169

Abstract

Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343–2222 nm), apparent electrical conductivity (EC_a), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, EC_a, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties. Full article

(This article belongs to the Special Issue Proximal Soil Sensing)

► Show Figures

Figure 1

14 pages, 4358 KiB

Open AccessArticle

Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra

by Meihua Yang, Dongyun Xu, Songchao Chen, Hongyi Li and Zhou Shi

Sensors 2019, 19(2), 263; https://doi.org/10.3390/s19020263 - 11 Jan 2019

Cited by 94 | Viewed by 6897

Abstract

Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R²), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R² = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R² = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R² = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R² = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R² = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R² = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R² = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R² = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain. Full article

(This article belongs to the Special Issue Proximal Soil Sensing)

► Show Figures

Figure 1

15 pages, 3076 KiB

Open AccessArticle

Predicting Profile Soil Properties with Reflectance Spectra via Bayesian Covariate-Assisted External Parameter Orthogonalization

by Kristen S. Veum, Paul A. Parker, Kenneth A. Sudduth and Scott H. Holan

Sensors 2018, 18(11), 3869; https://doi.org/10.3390/s18113869 - 10 Nov 2018

Cited by 24 | Viewed by 3812

Abstract

In situ, diffuse reflectance spectroscopy (DRS) profile soil sensors have the potential to provide both rapid and high-resolution prediction of multiple soil properties for precision agriculture, soil health assessment, and other applications related to environmental protection and agronomic sustainability. However, the effects of soil moisture, other environmental factors, and artefacts of the in-field spectral data collection process often hamper the utility of in situ DRS data. Various processing and modeling techniques have been developed to overcome these challenges, including external parameter orthogonalization (EPO) transformation of the spectra. In addition, Bayesian modeling approaches may improve prediction over traditional partial least squares (PLS) regression. The objectives of this study were to predict soil organic carbon (SOC), total nitrogen (TN), and texture fractions using a large, regional dataset of in situ profile DRS spectra and compare the performance of (1) traditional PLS analysis, (2) PLS on EPO-transformed spectra (PLS-EPO), (3) PLS-EPO with the Bayesian Lasso (PLS-EPO-BL), and (4) covariate-assisted PLS-EPO-BL models. In this study, soil cores and in situ profile DRS spectrometer scans were obtained to ~1 m depth from 22 fields across Missouri and Indiana, USA. In the laboratory, soil cores were split by horizon, air-dried, and sieved (<2 mm) for a total of 708 samples. Soil properties were measured and DRS spectra were collected on these air-dried soil samples. The data were randomly split into training (n = 308), testing (n = 200), and EPO calibration (n = 200) sets, and soil textural class was used as the categorical covariate in the Bayesian models. Model performance was evaluated using the root mean square error of prediction (RMSEP). For the prediction of soil properties using a model trained on dry spectra and tested on field moist spectra, the PLS-EPO transformation dramatically improved model performance relative to PLS alone, reducing RMSEP by 66% and 53% for SOC and TN, respectively, and by 76%, 91%, and 87% for clay, silt, and sand, respectively. The addition of the Bayesian Lasso further reduced RMSEP by 4–11% across soil properties, and the categorical covariate reduced RMSEP by another 2–9%. Overall, this study illustrates the strength of the combination of EPO spectral transformation paired with Bayesian modeling techniques to overcome environmental factors and in-field data collection artefacts when using in situ DRS data, and highlights the potential for in-field DRS spectroscopy as a tool for rapid, high-resolution prediction of soil properties. Full article

(This article belongs to the Special Issue Proximal Soil Sensing)

► Show Figures

Figure 1

22 pages, 2216 KiB

Open AccessArticle

Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling

by Olutobi Adeyemi, Ivan Grove, Sven Peets, Yuvraj Domun and Tomas Norton

Sensors 2018, 18(10), 3408; https://doi.org/10.3390/s18103408 - 11 Oct 2018

Cited by 129 | Viewed by 8879

Abstract

Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a

R^{2}

value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system. Full article

(This article belongs to the Special Issue Proximal Soil Sensing)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Proximal Soil Sensing

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI