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

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 30686

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Dr. Asa Gholizadeh

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Faculty of Agrobiology, Food and Natural Resources, Department of Soil Science and Soil Protection, Czech University of Life Sciences Prague, 16500 Prague, Czech Republic
Interests: proximal and remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Mohammadmehdi Saberioon

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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
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Dr. Fabio Castaldi

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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

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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 (8 papers)

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Research

22 pages, 3424 KiB

Open AccessArticle

Spectral Method for Liming Recommendation in Oxisol Based on the Prediction of Chemical Characteristics Using Interval Partial Least Squares Regression

by Glaucio Leboso Alemparte Abrantes dos Santos, Marcos Renan Besen, Renato Herrig Furlanetto, Luís Guilherme Teixeira Crusiol, Marlon Rodrigues, Amanda Silveira Reis, Karym Mayara de Oliveira, Carolina Fedrigo Coneglian, Roney Berti de Oliveira, Marcelo Augusto Batista and Marcos Rafael Nanni

Remote Sens. 2022, 14(9), 1972; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14091972 - 20 Apr 2022

Cited by 2 | Viewed by 1869

Abstract

Thousands of chemical analyses are carried out annually with the aim of recommending soil correction; however, these analyses are expensive, destructive, time-consuming, and can be harmful to the environment. As an alternative to conventional analysis methods, diffuse reflectance spectroscopy has been proposed as an option for evaluating the chemical characteristics of soil. The selection of variables has also emerged as an alternative to improve the performance of PLSR (partial least squares regression), as it decreases the root mean square error (RMSE) and increases the accuracy of the models. However, few studies have used a previous selection of variables for the construction of PLSR models to estimate the chemical characteristics of soil. In this context, the hypothesis in this study was that it is possible to calculate the liming recommendation in Oxisol based on the chemical characteristics estimated by PLSR, with a previous selection of variables using iPLS (Interval PLS). The objective was to calculate the need for liming based on chemical characteristics estimated via iPLS selection and PLSR modeling of specific wavelengths of soil reflectance. The experimental area was treated with different application rates of limestone, with and without incorporation, and phosphogypsum was applied in additional treatments. Soil assessments were carried out 5, 12, 24, and 36 months after the application of the treatments, using six layers: 0.00–0.05, 0.05–0.10, 0.10–0.20, 0.20–0.30, 0.30–0.40 and 0.40–0.60 m. Samples were subjected to conventional laboratory analyses, and spectral readings (400–2500 nm) were obtained with a spectroradiometer. The spectral curves were subjected to the iPLS variable selection method to generate PLSR models of the chemical characteristics used to calculate the liming recommendation. The chemical characteristics of the soil, such as Ca²⁺, sum of bases (SB), effective cation exchange capacity (CTCe), cation exchange capacity (CTC), and base saturation (BS), could be estimated, with values of R² ranging from 0.83 to 0.92 in the calibration and validation steps, and from 0.84 to 0.90 for the prediction step (in the fourth assessment). The liming recommendation calculated based on the chemical characteristics predicted from the PLSR models showed a strong correlation (r > 0.86) with the liming recommendation calculated by conventional laboratory techniques. The fourth soil assessment yielded the best correlation coefficient (r = 0.95). Full article

(This article belongs to the Special Issue Proximal/Remote Sensing Coupled with Chemometrics in Vegetation and Soil Sciences)

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19 pages, 4341 KiB

Open AccessArticle

Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance Spectroscopy

by Wu Yu, Yongsheng Hong, Songchao Chen, Yiyun Chen and Lianqing Zhou

Remote Sens. 2022, 14(6), 1303; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061303 - 08 Mar 2022

Cited by 6 | Viewed by 2135

Abstract

Visible and near-infrared (Vis–NIR) spectroscopy can provide a rapid and inexpensive estimation for soil organic carbon (SOC). However, with respect to field in situ spectroscopy, external environmental factors likely degrade the model accuracy. Among these factors, moisture has the greatest effect on soil spectra. The external parameter orthogonalization (EPO) algorithm in combination with the Chinese soil spectroscopic database (Dataset A, 1566 samples) was investigated to eliminate the interference of the external parameters for SOC estimation. Two different methods of EPO development, namely, laboratory-rewetting archive soil samples and field-collecting actual moist samples, were compared to balance model performance and analytical cost. Memory-based learning (MBL), a local modeling technique, was introduced to compare with partial least square (PLS), a global modeling method. A total of 250 soil samples from Central China were collected. Of these samples, 120 dry ground samples (Dataset B) were rewetted to different moisture levels to develop EPO P1 matrix. Seventy samples (Dataset C) containing field-moist intact and laboratory dry ground soils were used to establish EPO P2 matrix. The remaining 60 samples (Dataset D) also containing field-moist intact and laboratory dry ground soils were employed to validate the spectral models developed based on Dataset A. Results showed that EPO could correct the effect of external factors on soil spectra. For PLS, the validation statistics were as follows: no correction, validation R² = 0.02; P1 correction, validation R² = 0.56; and P2 correction, validation R² = 0.57. For MBL, the validation results were as follows: no correction, validation R² = 0.06; P1 correction, validation R² = 0.65; and P2 correction, validation R² = 0.69. The P2 consistently yielded better results than P1 did but simultaneously increased the sampling time and economic cost. The use of the P1 matrix and the MBL algorithm was recommended because it could reduce the cost of establishing in situ models for SOC. Full article

(This article belongs to the Special Issue Proximal/Remote Sensing Coupled with Chemometrics in Vegetation and Soil Sciences)

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18 pages, 2596 KiB

Open AccessArticle

Can Agricultural Management Induced Changes in Soil Organic Carbon Be Detected Using Mid-Infrared Spectroscopy?

by Jonathan Sanderman, Kathleen Savage, Shree R. S. Dangal, Gabriel Duran, Charlotte Rivard, Michel A. Cavigelli, Hero T. Gollany, Virginia L. Jin, Mark A. Liebig, Emmanuel Chiwo Omondi, Yichao Rui and Catherine Stewart

Remote Sens. 2021, 13(12), 2265; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122265 - 09 Jun 2021

Cited by 8 | Viewed by 3744

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 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

(This article belongs to the Special Issue Proximal/Remote Sensing Coupled with Chemometrics in Vegetation and Soil Sciences)

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16 pages, 3048 KiB

Open AccessArticle

Strategies for the Development of Spectral Models for Soil Organic Matter Estimation

by Everson Cezar, Marcos Rafael Nanni, Luís Guilherme Teixeira Crusiol, Liang Sun, Mônica Sacioto Chicati, Renato Herrig Furlanetto, Marlon Rodrigues, Rubson Natal Ribeiro Sibaldelli, Guilherme Fernando Capristo Silva, Karym Mayara de Oliveira and José A. M. Demattê

Remote Sens. 2021, 13(7), 1376; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071376 - 02 Apr 2021

Cited by 8 | Viewed by 2740

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 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

(This article belongs to the Special Issue Proximal/Remote Sensing Coupled with Chemometrics in Vegetation and Soil Sciences)

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18 pages, 9320 KiB

Open AccessArticle

Hair Fescue and Sheep Sorrel Identification Using Deep Learning in Wild Blueberry Production

by Patrick J. Hennessy, Travis J. Esau, Aitazaz A. Farooque, Arnold W. Schumann, Qamar U. Zaman and Kenny W. Corscadden

Remote Sens. 2021, 13(5), 943; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050943 - 03 Mar 2021

Cited by 13 | Viewed by 3472

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 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

(This article belongs to the Special Issue Proximal/Remote Sensing Coupled with Chemometrics in Vegetation and Soil Sciences)

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17 pages, 2252 KiB

Open AccessArticle

Detection of a Potato Disease (Early Blight) Using Artificial Intelligence

by Hassan Afzaal, Aitazaz A. Farooque, Arnold W. Schumann, Nazar Hussain, Andrew McKenzie-Gopsill, Travis Esau, Farhat Abbas and Bishnu Acharya

Remote Sens. 2021, 13(3), 411; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030411 - 25 Jan 2021

Cited by 35 | Viewed by 6131

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 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

(This article belongs to the Special Issue Proximal/Remote Sensing Coupled with Chemometrics in Vegetation and Soil Sciences)

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17 pages, 4009 KiB

Open AccessEditor’s ChoiceArticle

Design and Development of a Smart Variable Rate Sprayer Using Deep Learning

by Nazar Hussain, Aitazaz A. Farooque, Arnold W. Schumann, Andrew McKenzie-Gopsill, Travis Esau, Farhat Abbas, Bishnu Acharya and Qamar Zaman

Remote Sens. 2020, 12(24), 4091; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244091 - 15 Dec 2020

Cited by 35 | Viewed by 6031

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) 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

(This article belongs to the Special Issue Proximal/Remote Sensing Coupled with Chemometrics in Vegetation and Soil Sciences)

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18 pages, 5532 KiB

Open AccessArticle

Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection

by Lu Xu, Yongsheng Hong, Yu Wei, Long Guo, Tiezhu Shi, Yi Liu, Qinghu Jiang, Teng Fei, Yaolin Liu, Abdul M. Mouazen and Yiyun Chen

Remote Sens. 2020, 12(20), 3394; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203394 - 16 Oct 2020

Cited by 22 | Viewed by 3146

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 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

(This article belongs to the Special Issue Proximal/Remote Sensing Coupled with Chemometrics in Vegetation and Soil Sciences)

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