Special Issue "Proximal/Remote Sensing Coupled with Chemometrics in Vegetation and Soil Sciences"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Asa Gholizadeh
E-Mail Website
Guest Editor
Department of Soil Science and Soil Protection, Czech University of Life Sciences Prague, 16500 Prague, Czech Republic
Interests: soil spectroscopy; proximal and remote sensing in soil morning; Earth observation; soil contamination; chemometrics; machine learning in soil parameters analysis and monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammadmehdi Saberioon
E-Mail Website
Guest Editor
German Research Centre for Geosciences, 14473 Potsdam, Germany
Interests: applied remote sensing in different discipline of agriculture and environment studies; field and imaging spectroscopy; image and signal processing; machine learning and deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Fabio Castaldi
E-Mail Website
Guest Editor
ILVO- Flanders Research Institute for Agriculture, Fisheries and Food, Technology and Food Science, Agricultural Engineering, Burg. Van Gansberghelaan 115 bus 1, 9820 Merelbeke, Belgium
Interests: soil imaging spectroscopy; multi- and hyperspectral remote sensing; precision agriculture; Earth observation; geostatistics; sustainable agriculture; soil mapping; soil organic carbon; data fusion

Special Issue Information

Dear Colleagues,

Proximal and remote sensing as well as their data fusion allow many measurements to be made for environmental (e.g., soil and vegetation) monitoring at depth and in time; however, the derived data from these technologies may include weak, wide, and overlapping absorption bands. The hidden information therefore needs to be extracted to establish a proxy approach to detect vegetation and soil parameters. Chemometrics, the science of extracting information from different databases, including signal and image data by data-driven means, has the potential to address this issue using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science. Some machine learning algorithms have frequently tried to link proximal/remote sensing data in vegetation and soil variables. However, with the development of large spectral libraries, we need to seize more possibilities to utilize big data analytics to process the spectral data. More advanced machine learning methods as well as deep learning algorithms with higher capability of large-scale processing data might be a solution that supports more sophisticated modeling and permits the easy use of large amounts of computational resources for training such models. The proximal/remote sensing data coupled with chemometrics (e.g., advanced machine learning and especially deep learning methods) therefore offer tremendous but not fully exploited opportunities to monitor and map vegetation and soil variables across various disciplines and on vast spatial scales.

This Special Issue aims i) to report the up-to-date advancements and trends regarding the combination of chemometrics and proximal/remote sensing information by data fusion techniques and ii) to advance the application of chemometrics techniques for proximal/remote sensing-based vegetation and soil monitoring. We welcome contributions in terms of chemometrics methods, including but not limited to novel machine learning and deep learning technique application, potential, and challenges in proximal/remote sensing of vegetation and soil.

Dr. Asa Gholizadeh
Dr. Mohammadmehdi Saberioon
Dr. Fabio Castaldi
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 papers will be 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. Remote Sensing 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 2400 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

  • Chemometrics
  • Machine learning
  • Deep learning
  • Remote sensing of soil and vegetation
  • Proximal sensing of soil and vegetation
  • Soil monitoring and mapping
  • Data fusion

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Can Agricultural Management Induced Changes in Soil Organic Carbon Be Detected Using Mid-Infrared Spectroscopy?
Remote Sens. 2021, 13(12), 2265; https://doi.org/10.3390/rs13122265 - 09 Jun 2021
Viewed by 1205
Abstract
A major limitation to building credible soil carbon sequestration programs is the cost of measuring soil carbon change. Diffuse reflectance spectroscopy (DRS) is considered a viable low-cost alternative to traditional laboratory analysis of soil organic carbon (SOC). While numerous studies have shown that [...] Read more.
A major limitation to building credible soil carbon sequestration programs is the cost of measuring soil carbon change. Diffuse reflectance spectroscopy (DRS) is considered a viable low-cost alternative to traditional laboratory analysis of soil organic carbon (SOC). While numerous studies have shown that DRS can produce accurate and precise estimates of SOC across landscapes, whether DRS can detect subtle management induced changes in SOC at a given site has not been resolved. Here, we leverage archived soil samples from seven long-term research trials in the U.S. to test this question using mid infrared (MIR) spectroscopy coupled with the USDA-NRCS Kellogg Soil Survey Laboratory MIR spectral library. Overall, MIR-based estimates of SOC%, with samples scanned on a secondary instrument, were excellent with the root mean square error ranging from 0.10 to 0.33% across the seven sites. In all but two instances, the same statistically significant (p < 0.10) management effect was found using both the lab-based SOC% and MIR estimated SOC% data. Despite some additional uncertainty, primarily in the form of bias, these results suggest that large existing MIR spectral libraries can be operationalized in other laboratories for successful carbon monitoring. Full article
Show Figures

Graphical abstract

Article
Strategies for the Development of Spectral Models for Soil Organic Matter Estimation
Remote Sens. 2021, 13(7), 1376; https://doi.org/10.3390/rs13071376 - 02 Apr 2021
Cited by 1 | Viewed by 801
Abstract
Visible (V), Near Infrared (NIR) and Short Waves Infrared (SWIR) spectroscopy has been indicated as a promising tool in soil studies, especially in the last decade. However, in order to apply this method, it is necessary to develop prediction models with the capacity [...] Read more.
Visible (V), Near Infrared (NIR) and Short Waves Infrared (SWIR) spectroscopy has been indicated as a promising tool in soil studies, especially in the last decade. However, in order to apply this method, it is necessary to develop prediction models with the capacity to capture the intrinsic differences between agricultural areas and incorporate them in the modeling process. High quality estimates are generally obtained when these models are applied to soil samples displaying characteristics similar to the samples used in their construction. However, low quality predictions are noted when applied to samples from new areas presenting different characteristics. One way to solve this problem is by recalibrating the models using selected samples from the area of interest. Based on this premise, the aim of this study was to use the spiking technique and spiking associated with hybridization to expand prediction models and estimate organic matter content in a target area undergoing different uses and management. A total of 425 soil samples were used for the generation of the state model, as well as 200 samples from a target area to select the subsets (10 samples) used for model recalibration. The spectral readings of the samples were obtained in the laboratory using the ASD FieldSpec 3 Jr. Sensor from 350 to 2500 nm. The spectral curves of the samples were then associated to the soil attributes by means of a partial least squares regression (PLSR). The state model obtained better results when recalibrated with samples selected through a cluster analysis. The use of hybrid spectral curves did not generate significant improvements, presenting estimates, in most cases, lower than the state model applied without recalibration. The use of the isolated spiking technique was more effective in comparison with the spiked and hybridized state models, reaching r2, square root of mean prediction error (RMSEP) and ratio of performance to deviation (RPD) values of 0.43, 4.4 g dm−3, and 1.36, respectively. Full article
Show Figures

Graphical abstract

Article
Hair Fescue and Sheep Sorrel Identification Using Deep Learning in Wild Blueberry Production
Remote Sens. 2021, 13(5), 943; https://doi.org/10.3390/rs13050943 - 03 Mar 2021
Cited by 1 | Viewed by 989
Abstract
Deep learning convolutional neural networks (CNNs) are an emerging technology that provide an opportunity to increase agricultural efficiency through remote sensing and automatic inferencing of field conditions. This paper examined the novel use of CNNs to identify two weeds, hair fescue and sheep [...] Read more.
Deep learning convolutional neural networks (CNNs) are an emerging technology that provide an opportunity to increase agricultural efficiency through remote sensing and automatic inferencing of field conditions. This paper examined the novel use of CNNs to identify two weeds, hair fescue and sheep sorrel, in images of wild blueberry fields. Commercial herbicide sprayers provide a uniform application of agrochemicals to manage patches of these weeds. Three object-detection and three image-classification CNNs were trained to identify hair fescue and sheep sorrel using images from 58 wild blueberry fields. The CNNs were trained using 1280x720 images and were tested at four different internal resolutions. The CNNs were retrained with progressively smaller training datasets ranging from 3780 to 472 images to determine the effect of dataset size on accuracy. YOLOv3-Tiny was the best object-detection CNN, detecting at least one target weed per image with F1-scores of 0.97 for hair fescue and 0.90 for sheep sorrel at 1280 × 736 resolution. Darknet Reference was the most accurate image-classification CNN, classifying images containing hair fescue and sheep sorrel with F1-scores of 0.96 and 0.95, respectively at 1280 × 736. MobileNetV2 achieved comparable results at the lowest resolution, 864 × 480, with F1-scores of 0.95 for both weeds. Training dataset size had minimal effect on accuracy for all CNNs except Darknet Reference. This technology can be used in a smart sprayer to control target specific spray applications, reducing herbicide use. Future work will involve testing the CNNs for use on a smart sprayer and the development of an application to provide growers with field-specific information. Using CNNs to improve agricultural efficiency will create major cost-savings for wild blueberry producers. Full article
Show Figures

Graphical abstract

Article
Detection of a Potato Disease (Early Blight) Using Artificial Intelligence
Remote Sens. 2021, 13(3), 411; https://doi.org/10.3390/rs13030411 - 25 Jan 2021
Cited by 2 | Viewed by 1381
Abstract
This study evaluated the potential of using machine vision in combination with deep learning (DL) to identify the early blight disease in real-time for potato production systems. Four fields were selected to collect images (n = 5199) of healthy and diseased potato plants [...] Read more.
This study evaluated the potential of using machine vision in combination with deep learning (DL) to identify the early blight disease in real-time for potato production systems. Four fields were selected to collect images (n = 5199) of healthy and diseased potato plants under variable lights and shadow effects. A database was constructed using DL to identify the disease infestation at different stages throughout the growing season. Three convolutional neural networks (CNNs), namely GoogleNet, VGGNet, and EfficientNet, were trained using the PyTorch framework. The disease images were classified into three classes (2-class, 4-class, and 6-class) for accurate disease identification at different growth stages. Results of 2-class CNNs for disease identification revealed the significantly better performance of EfficientNet and VGGNet when compared with the GoogleNet (FScore range: 0.84–0.98). Results of 4-Class CNNs indicated better performance of EfficientNet when compared with other CNNs (FScore range: 0.79–0.94). Results of 6-class CNNs showed similar results as 4-class, with EfficientNet performing the best. GoogleNet, VGGNet, and EfficientNet inference time values ranged from 6.8–8.3, 2.1–2.5, 5.95–6.53 frames per second, respectively, on a Dell Latitude 5580 using graphical processing unit (GPU) mode. Overall, the CNNs and DL frameworks used in this study accurately classified the early blight disease at different stages. Site-specific application of fungicides by accurately identifying the early blight infected plants has a strong potential to reduce agrochemicals use, improve the profitability of potato growers, and lower environmental risks (runoff of fungicides to water bodies). Full article
Show Figures

Graphical abstract

Article
Design and Development of a Smart Variable Rate Sprayer Using Deep Learning
Remote Sens. 2020, 12(24), 4091; https://doi.org/10.3390/rs12244091 - 15 Dec 2020
Cited by 1 | Viewed by 1333
Abstract
The uniform application (UA) of agrochemicals results in the over-application of harmful chemicals, increases crop input costs, and deteriorates the environment when compared with variable rate application (VA). A smart variable rate sprayer (SVRS) was designed, developed, and tested using deep learning (DL) [...] Read more.
The uniform application (UA) of agrochemicals results in the over-application of harmful chemicals, increases crop input costs, and deteriorates the environment when compared with variable rate application (VA). A smart variable rate sprayer (SVRS) was designed, developed, and tested using deep learning (DL) for VA application of agrochemicals. Real-time testing of the SVRS took place for detecting and spraying and/or skipping lambsquarters weed and early blight infected and healthy potato plants. About 24,000 images were collected from potato fields in Prince Edward Island and New Brunswick under varying sunny, cloudy, and partly cloudy conditions and processed/trained using YOLOv3 and tiny-YOLOv3 models. Due to faster performance, the tiny-YOLOv3 was chosen to deploy in SVRS. A laboratory experiment was designed under factorial arrangements, where the two spraying techniques (UA and VA) and the three weather conditions (cloudy, partly cloudy, and sunny) were the two independent variables with spray volume consumption as a response variable. The experimental treatments had six repetitions in a 2 × 3 factorial design. Results of the two-way ANOVA showed a significant effect of spraying application techniques on volume consumption of spraying liquid (p-value < 0.05). There was no significant effect of weather conditions and interactions between the two independent variables on volume consumption during weeds and simulated diseased plant detection experiments (p-value > 0.05). The SVRS was able to save 42 and 43% spraying liquid during weeds and simulated diseased plant detection experiments, respectively. Water sensitive papers’ analysis showed the applicability of SVRS for VA with >40% savings of spraying liquid by SVRS when compared with UA. Field applications of this technique would reduce the crop input costs and the environmental risks in conditions (weed and disease) like experimental testing. Full article
Show Figures

Graphical abstract

Article
Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection
Remote Sens. 2020, 12(20), 3394; https://doi.org/10.3390/rs12203394 - 16 Oct 2020
Cited by 2 | Viewed by 869
Abstract
Visible and near-infrared reflectance (VIS-NIR) spectroscopy is widely applied to estimate soil organic carbon (SOC). Intense and diverse human activities increase the heterogeneity in the relationships between SOC and VIS-NIR spectra in anthropogenic soil. This fact results in poor performance of SOC estimation [...] Read more.
Visible and near-infrared reflectance (VIS-NIR) spectroscopy is widely applied to estimate soil organic carbon (SOC). Intense and diverse human activities increase the heterogeneity in the relationships between SOC and VIS-NIR spectra in anthropogenic soil. This fact results in poor performance of SOC estimation models. To improve model accuracy and parsimony, we investigated the performance of two variable selection algorithms, namely competitive adaptive reweighted sampling (CARS) and random frog (RF), coupled with five spectral pretreatments. A total of 108 samples were collected from Jianghan Plain, China, with the SOC content and VIS-NIR spectra measured in the laboratory. Results showed that both CARS and RF coupled with partial least squares regression (PLSR) outperformed PLSR alone in terms of higher model accuracy and less spectral variables. It revealed that spectral variable selection could identify important spectral variables that account for the relationships between SOC and VIS-NIR spectra, thereby improving the accuracy and parsimony of PLSR models in anthropogenic soil. Our findings are of significant practical value to the SOC estimation in anthropogenic soil by VIS-NIR spectroscopy. Full article
Show Figures

Graphical abstract

Back to TopTop