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Proximal Soil Sensors in Precision Agriculture

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

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 4507

Special Issue Editor

Special Issue Information

Dear Colleagues,

To achieve food security, it is important to optimize the use of land resources and to facilitate information technologies suitable for revealing the spatial variability required for optimum and economic management of crop production inputs; this approach is known as precision agriculture (PA). Since the early 1990s, researchers involved in conceptualizing PA indicated the need for proximal soil sensors (sensors that help measure soil conditions while placing in close proximity to the soil being evaluated), as it is more immune to the quality of soil coverage and atmospheric conditions. Today, this need is more urgent given rising costs of energy, labor, crop production inputs (e.g., seeds, fertilizer, pesticides), as well as increased environmental concerns such as soil health, surface water quality, and greenhouse gas emissions. Recent innovations in geophysics, spectroscopy, sensor technology, computing and others have resulted in some sensor types that are affordable and acceptable by end users.

The main goal of this special issue on ‘Proximal Soil Sensors in Precision Agriculture’ is to capture the current state-of-the-art and contemporary progress and perspectives on proximal soil sensors for precision agriculture applications. This issue aims at bringing together contributions from designers, developers, modellers, users and decision makers of various proximal soil sensors and tools for precision agriculture applications. Contributions related to any proximal soil sensors and sensor systems with current, potential, and perceived applications in precision agriculture are encouraged.

Dr. Asim Biswas
Guest Editor

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Published Papers (3 papers)

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Research

12 pages, 11754 KiB  
Communication
Soil Particle Size Thresholds in Soil Spectroscopy and Its Effect on the Multivariate Models for the Analysis of Soil Properties
Sensors 2023, 23(22), 9171; https://doi.org/10.3390/s23229171 - 14 Nov 2023
Viewed by 554
Abstract
This study focused on one of the few but critical sample preparations required in soil spectroscopy (i.e., grinding), as well as the effect of soil particle size on the FTIR spectral database and the partial least squares regression models for the prediction of [...] Read more.
This study focused on one of the few but critical sample preparations required in soil spectroscopy (i.e., grinding), as well as the effect of soil particle size on the FTIR spectral database and the partial least squares regression models for the prediction of eight soil properties (viz., TC, TN, OC, sand, silt, clay, Olsen P, and CEC). Fifty soil samples from three Moroccan region were used. The soil samples underwent three preparations (drying, grinding, sieving) to obtain, at the end of the sample preparation step, three ranges of particle size, samples with sizes < 500 µm, samples with sizes < 250 µm, and a third range with particles < 125 µm. The multivariate models (PLSR) were set up based on the FTIR spectra recorded on the different obtained samples. The correlation coefficient (R2) and the root mean squared error of cross validation (RMSECV) were chosen as figures of merit to assess the quality of the prediction models. The results showed a general trend in improving the R2 as the finer particles were used (from <500 µm to 125 µm), which was clearly observed for TC, TN, P2O5, and CEC, whereas the cross-validation errors (RMSECV) showed an opposite trend. This confirmed that fine soil grinding improved the accuracy of predictive models for soil properties diagnosis in soil spectroscopy. Full article
(This article belongs to the Special Issue Proximal Soil Sensors in Precision Agriculture)
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18 pages, 2872 KiB  
Article
Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion
Sensors 2023, 23(18), 7707; https://doi.org/10.3390/s23187707 - 06 Sep 2023
Viewed by 635
Abstract
Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in [...] Read more.
Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images. Full article
(This article belongs to the Special Issue Proximal Soil Sensors in Precision Agriculture)
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18 pages, 2879 KiB  
Article
Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy
Sensors 2021, 21(20), 6745; https://doi.org/10.3390/s21206745 - 11 Oct 2021
Cited by 12 | Viewed by 2150
Abstract
The absorbance spectra for air-dried and ground soil samples from Ontario, Canada were collected in the visible and near-infrared (VIS-NIR) region from 343 to 2200 nm. The study examined thirteen combination of six preprocessing (1st derivative, 2nd derivative, Savitzky-Golay, Gap, SNV and Detrend) [...] Read more.
The absorbance spectra for air-dried and ground soil samples from Ontario, Canada were collected in the visible and near-infrared (VIS-NIR) region from 343 to 2200 nm. The study examined thirteen combination of six preprocessing (1st derivative, 2nd derivative, Savitzky-Golay, Gap, SNV and Detrend) method included in ‘prospectr’ R package along with four modeling approaches: partial least square regression (PLSR), cubist, random forest (RF), and extreme learning machine (ELM) for prediction of the soil organic matter (SOM). The 1st derivative + gap, 2nd derivative + gap and standard normal variance (SNV) were the best preprocessing algorithms. Thus, only these three preprocessing algorithms along with four modeling approaches were used for prediction of soil pH, electrical conductively (EC), %sand, %silt, %clay, %very coarse sand (VCS), %coarse sand (CS), %medium sand (ms) and %fine sand (fs). The results showed that OM, pH, %sand, %silt and %CS were all predicted with confidence (R2 > 0.60) and the combination of 1st derivative + gap and RF gained the best performance. A detailed comparison of the preprocessing and modeling algorithms for various soil properties in this study demonstrate that for better prediction of soil properties using VIS-NIR spectroscopy requires different preprocessing and modeling algorithms. However, in general RF and 1st derivative + gap can be labeled at the best combination of preprocessing and modelling algorithms. Full article
(This article belongs to the Special Issue Proximal Soil Sensors in Precision Agriculture)
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