Search Results (74)

In recent years, heavy metal pollution in farmland soil has become a crisis due to human activities or natural impacts, with particular emphasis on cases from China, where this issue is prominent, greatly affecting crop production and food safety. In the context of a low heavy metal (HM) content in farmland soil, which is difficult to monitor in real time, effective and rapid monitoring of soil plays a decisive role in subsequent targeted protection measures. To this end, this paper provides a narrative review of the application of spectral sensing technology on the basis of the quantitative inversion of heavy metal content in farmland soil using different platforms (ground, airborne, and spaceborne). The sensing process evaluates the mechanism by which soil produces different weak spectral features from the perspective of the heterogeneity of farmland soil. Different methods used for the quantitative inversion of heavy metals (by studying the correlation between soil heavy metals and organic matter, clay minerals, metal oxides, crop vegetation index, etc.) and their feasibility were clarified. At the same time, relevant research on key technologies used in various processes—such as follow-up pretreatment, spectral feature extraction, and the establishment of inversion models for spectral data of different farmland soil types—was summarized, with a primary focus on cases in China. Finally, the challenges, applications, and research directions related to heavy metal spectral inversion in farmland soil were discussed. Full article

Adzuki bean rust disease is an important factor restricting the yield of the adzuki bean. Late prevention and control at the early stage of the disease will lead to crop failure. Traditional diagnosis methods of adzuki bean rust disease mainly rely on field observations and laboratory tests, which are inefficient, time-consuming, highly dependent on professional knowledge, and cannot meet the requirements of modern agriculture for rapid and accurate diagnosis. To address this issue, a diagnosis method of adzuki bean rust disease was proposed using spectroscopy and deep learning methods. First, visible/near-infrared (UV/VNIR) spectroscopy was used to extract the spectral information of leaves, and discrete wavelet transform (DWT) was applied to preprocess and smooth the original canopy spectral data to effectively reduce the impact of noise interference. Second, the competitive adaptive reweighted sampling (CARS) algorithm was implemented in the range of 425–825 nm to determine the optimal characteristic wavenumbers, thereby reducing data redundancy. Finally, 51 characteristic wavenumbers were selected and imported into the LeNet-5 deep learning model for simulation and evaluation. The results showed that the accuracy, precision, recall, and F1 score on the test set were 99.65%, 98.04%, 99.01%, and 98.52%, respectively. The proposed DWT-CARS-LeNet-5 model can diagnose adzuki bean rust quickly, accurately, and non-destructively. This method can provide a cutting-edge solution for improving the accuracy of prevention and control of adzuki bean rust disease in agricultural practice. Full article

Remote sensing from unoccupied aerial systems (UASs) has witnessed exponential growth. The increasing use of imaging spectroscopy sensors and RGB cameras on UAS platforms demands accurate, cross-comparable multi-sensor data. Inherent errors during image capture or processing can introduce spatial offsets, diminishing spatial accuracy and hindering cross-comparison and change detection analysis. To address this, we demonstrate the use of an optical flow algorithm, eFOLKI, for co-registering imagery from two pushbroom imaging spectroscopy sensors (VNIR and NIR/SWIR) to an RGB orthomosaic. Our study focuses on two ecologically diverse vegetative sites in Tasmania, Australia. Both sites are structurally complex, posing challenging datasets for co-registration algorithms with initial georectification spatial errors of up to 9 m planimetrically. The optical flow co-registration significantly improved the spatial accuracy of the imaging spectroscopy relative to the RGB orthomosaic. After co-registration, spatial alignment errors were greatly improved, with RMSE and MAE values of less than 13 cm for the higher-spatial-resolution dataset and less than 33 cm for the lower resolution dataset, corresponding to only 2–4 pixels in both cases. These results demonstrate the efficacy of optical flow co-registration in reducing spatial discrepancies between multi-sensor UAS datasets, enhancing accuracy and alignment to enable robust environmental monitoring. Full article

With the increasing demand for wood in the wood market and the frequent trade of high-value wood, the accurate identification of wood varieties has become essential. This study employs two hyperspectral imaging systems—visible and near-infrared spectroscopy (VNIR) and short-wave infrared spectroscopy (SWIR)—in combination with a deep learning model to propose a method for wood species identification. Spectral data from wood samples were obtained through hyperspectral imaging technology, and classification was performed using a combination of convolutional neural networks (CNNs) and Transformer models. Multiple spectral preprocessing and feature extraction techniques were applied to enhance data quality and model performance. The experimental results show that the full-band modeling is significantly better than the feature-band modeling in terms of classification accuracy and robustness. Among them, the classification accuracy of SWIR reaches 100%, the number of model parameters is 1,286,228, the total size of the model is 4.93 MB, and the Floating Point Operations (FLOPs) is 1.29 M. Additionally, the Shapley Additive Explanation (SHAP) technique was utilized for model interpretability, revealing key spectral bands and feature regions that the model emphasizes during classification. Compared with other models, CNN-Transformer is more effective in capturing the key features. This method provides an efficient and reliable tool for the wood industry, particularly in wood processing and trade, offering broad application potential and significant economic benefits. Full article

Different sulfates (Ca-, Mg, and Fe- sulfates) have been extensively detected on the Martian surface. As one of the Martian sulfates, the presence of ferrous sulfates will provide valuable clues about the redox environment, hydrological processes, and climatic history of ancient Mars. In this study, three hydrated ferrous sulfates were prepared in the laboratory by heating dehydration reactions. These samples were analyzed using X-ray Diffraction (XRD) to confirm their phase and homogeneity. Subsequently, Raman, mid-infrared (MIR) spectra, visible near-infrared (VNIR) spectra, and laser-induced breakdown spectroscopy (LIBS) were measured and analyzed. The results demonstrate that the spectra of three hydrated ferrous sulfates exhibit distinctive features (e.g., the v₁ and v₃ features of ${\mathrm{S}\mathrm{O}}_4^{2-}$ tetrahedra in their Raman and MIR spectra) that can offer new insights for identifying different ferrous sulfates on Mars and aid in the interpretation of in-situ data collected by instruments such as the Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals (SHERLOC), SuperCam, and ChemCam, etc. Full article

Hyperspectral imaging technology holds great potential for various stages of the mining life cycle, both in active and abandoned mines, from exploration to reclamation. The technology, however, has yet to achieve large-scale industrial implementation and acceptance. While hyperspectral satellite imagery yields high spectral resolution, a high signal-to-noise ratio (SNR), and global availability with breakthrough systems like EnMAP, EMIT, GaoFen-5, PRISMA, and Tanager-1, limited spatial and temporal resolution poses challenges for the mining sectors, which require decimetre-to-centimetre-scale spatial resolution for applications such as reconciliation and environmental monitoring and daily temporal revisit times, such as for ore/waste estimates and geotechnical assessments. Hyperspectral imaging from drones (Uncrewed Aerial Systems; UASs) offers high-spatial-resolution data relevant to the pit/mine scale, with the capability for frequent, user-defined re-visit times for areas of limited extent. Areas of interest can be defined by the user and targeted explicitly. Collecting data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions offers the detection of different minerals and surface alteration patterns, potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. This is related to but not exclusive to detecting deleterious minerals for different processes (e.g., clays, iron oxides, talc), secondary iron oxides indicating the leakage of acid mine drainage for rehabilitation efforts, swelling clays potentially affecting rock integrity and stability, and alteration minerals used to vector toward economic mineralisation (e.g., dickite, jarosite, alunite). In this paper, we review applicable instrumentation, software components, and relevant studies deploying hyperspectral imaging datasets in or appropriate to the mining sector, with a particular focus on hyperspectral VNIR-SWIR UASs. Complementarily, we draw on previous insights from airborne, satellite, and ground-based imaging systems. We also discuss common practises for UAS survey planning and ground sampling considerations to aid in data interpretation. Full article

The identification of minerals, particularly clay minerals, using visible, near-infrared, and short-wave infrared (VNIR-SWIR) spectroscopy has gained prominence due to its efficiency and the advancement of remote hyperspectral sensors. However, identifying minerals in polymineralic samples remains challenging due to overlapping absorption features. This study prepared systematic binary mixtures of two smectites (dioctahedral and trioctahedral) with common non-clay minerals (calcite, dolomite, gypsum, quartz, and feldspar). Spectra from these mixtures were obtained using the ASD FieldSpec 4 Hi-Res spectroradiometer and analyzed with continuum removal and second derivative preprocessing to define detection limits. These limits indicate the minimum percentage of each mineral required for clear identification in various smectite–non-clay combinations. After continuum removal, smectites are identified at ≥5%–10% in mixtures with carbonates, quartz, and feldspar, but ≥70% is needed for gypsum. Non-clay minerals have detection limits of ≥70% for calcite and 20% for gypsum in the presence of smectites, while dolomite remains undetectable. The second derivative improves these limits, enabling smectite identification at 5% in carbonate mixtures and 5%–15% in gypsum mixtures. Calcite detection limits are 65%, and dolomite can be identified at ≥65% and ≥85% with dioctahedral and trioctahedral smectites, respectively. Gypsum detection limits are reduced to 10%, while quartz and feldspar cannot be identified due to lacking absorption features. Full article

Vertical stratification in marine sediment profiles indicates physical and chemical sedimentary processes and, thus, is the first step in sedimentary research and in studying their relationship with global climate change. Traditional technologies for studying vertical stratification have low efficiency; thus, new technologies are highly needed. Recently, visible and near-infrared spectroscopy (VNIR) has been explored to rapidly determine sediment parameters, such as clay content, particle size, total carbon (TC), total nitrogen (TN), and so on. Here, we explored vertical stratification in a sediment column in the South China Sea using VNIR. The sediment column was 160 cm and divided into 160 samples by 1 cm intervals. All samples were classified into three layers by depth, that is, 0–50 cm (the upper layer), 50–100 cm (the middle layer), and 100–160 cm (the bottom layer). Concentrations of TC and TN in each sample were measured by Elementa Vario EL III. Visible and near-infrared reflectance spectra of each sample were collected by Agilent Cary 5000. A global model and several classification models for vertical stratification in sediments were established by a Support Vector Machine (SVM) after the characteristic spectra were identified using Competitive Adaptive Reweighted Sampling. In the classification models, K-means clustering and Density Peak Clustering (DPC) were employed as the unsupervised clustering algorithms. The results showed that the stratification was successful by VNIR, especially when using the combination of unsupervised clustering and machine learning algorithms. The correct classification rate (CCR) was much higher in the classification models than in the global model. And the classification models had a higher CCR using K-means combined with SVM (94.8%) and using DPC combined with SVM (96.0%). The higher CCR might be derived from the chemical classification. Indeed, similar results were also found in the chemical stratification. This study provided a theoretical basis for the rapid and synchronous measurement of chemical and physical parameters in sediment profiles by VNIR. Full article

Quickly determining the metal content in plants and subsequently identifying geochemical anomalies can provide clues and guidance for predicting the location and scale of concealed ore bodies in vegetation-covered areas. Although visible, near-infrared and shortwave infrared (VNIR–SWIR) reflectance spectroscopy at wavelengths ranging from 400 to 2500 nm has been proven by many researchers to be a fast, accurate and nondestructive approach for estimating the contents of copper (Cu), lead (Pb), zinc (Zn) and other metal elements in plants, relatively few studies have been conducted on the estimation of lithium (Li) in plants. Therefore, the potential of applying VNIR–SWIR spectroscopy techniques for estimating the Li content in plants was explored in this study. The Jingerquan Li mining area in Hami, Xinjiang, China, was chosen. Three sampling lines were established near a pegmatite deposit and in a background region, canopy reflectance spectra were obtained for desert plants and Li contents were determined in the laboratory; then, quantitative relationships were established between nine different transformed spectra (including both integer and fractional orders) and the Li content was estimated using partial least squares regression (PLSR). The results showed that models constructed using high-order derivative spectra (with an order greater than or equal to 1) significantly outperformed those based on original and low-order derivative spectra (with an order less than 1). Notably, the model based on a 1.1-order derivative spectrum displayed the best performance. Furthermore, the performance of the model based on the two-layer wavelet coefficients of the 1.1-order derivative spectrum was further improved compared with that of the model based on only the 1.1-order derivative spectrum. The coefficient of determination ($R_{pre}^2$) and the ratio of performance to deviation (RPD) for the validation set increased from 0.6977 and 1.7656 to 0.7044 and 1.8446, respectively, and the root mean square error (RMSEpre) decreased from 2.5735 to 2.4633 mg/kg. These results indicate that quickly and accurately estimating the Li content in plants via the proposed spectroscopic analysis technique is feasible and effective; however, appropriate spectral preprocessing methods should be selected before hyperspectral estimation models are constructed. Overall, the developed hybrid spectral transformation approach, which combines wavelet coefficients and derivative spectra, displayed excellent application potential for estimating the Li content in plants. Full article

Despite advances in conservation–restoration treatments, most surface cleaning tests are subjectively evaluated. Scores according to qualitative criteria are employed to assess results, but these can vary by user and context. This paper presents a range of cleaning efficacy and homogeneity evaluation metrics for appraising cleaning trials, which minimise user bias by measuring quantifiable changes in the appearance and characteristic spectral properties of surfaces. The metrics are based on various imaging techniques (optical imaging by photography using visible light (VIS); spectral imaging in the visible-to-near-infrared (VNIR) and shortwave infrared (SWIR) ranges; chemical imaging by Fourier transform infrared (FTIR) spectral mapping in the mid-infrared (MIR) range; and scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDX) element mapping). They are complemented by appearance measurements (glossimetry and colourimetry). As a case study showcasing the low-cost to high-end metrics, agar gel spray cleaning tests on exposed ground and unvarnished oil paint mock-ups are reported. The evaluation metrics indicated that spraying agar (prepared with citric acid in ammonium hydroxide) at a surface-tailored pH was as a safe candidate for efficacious and homogenous soiling removal on water-sensitive oil paint and protein-bound ground. Further research is required to identify a gel-based cleaning system for oil-bound grounds. Full article

The geographical origin of foods greatly influences their quality and price, leading to adulteration between high-priced and low-priced regions in the market. The rapid detection of such adulteration is crucial for food safety and fair competition. To detect the adulteration of Polygonati Rhizoma from different regions, we proposed LIBS-VNIR fusion based on the deep learning network (LVDLNet), which combines laser-induced breakdown spectroscopy (LIBS) containing element information with visible and near-infrared spectroscopy (VNIR) containing molecular information. The LVDLNet model achieved accuracy of 98.75%, macro-F measure of 98.50%, macro-precision of 98.78%, and macro-recall of 98.75%. The model, which increased these metrics from about 87% for LIBS and about 93% for VNIR to more than 98%, significantly improved the identification ability. Furthermore, tests on different adulterated source samples confirmed the model’s robustness, with all metrics improving from about 87% for LIBS and 86% for VNIR to above 96%. Compared to conventional machine learning algorithms, LVDLNet also demonstrated its superior performance. The results indicated that the LVDLNet model can effectively integrate element information and molecular information to identify the adulterated Polygonati Rhizoma. This work shows that the scheme is a potent tool for food identification applications. Full article

Visible near-infrared spectroscopy (VNIR) is extensively researched for obtaining soil property information due to its rapid, cost-effective, and environmentally friendly advantages. Despite its widespread application and significant achievements in soil property analysis, current soil prediction models continue to suffer from low accuracy. To address this issue, we propose a convolutional neural network model that can achieve high-precision soil property prediction by creating 2D multi-channel inputs and applying a multi-scale spatial attention mechanism. Initially, we explored two-dimensional multi-channel inputs for seven soil properties in the public LUCAS spectral dataset using the Gramian Angular Field (GAF) method and various preprocessing techniques. Subsequently, we developed a convolutional neural network model with a multi-scale spatial attention mechanism to improve the network’s extraction of relevant spatial contextual information. Our proposed model showed superior performance in a statistical comparison with current state-of-the-art techniques. The RMSE (R²) values for various soil properties were as follows: organic carbon content (OC) of 19.083 (0.955), calcium carbonate content (CaCO₃) of 24.901 (0.961), nitrogen content (N) of 0.969 (0.933), cation exchange capacity (CEC) of 6.52 (0.803), pH in H₂O of 0.366 (0.927), clay content of 4.845 (0.86), and sand content of 12.069 (0.789). Our proposed model can effectively extract features from visible near-infrared spectroscopy data, contributing to the precise detection of soil properties. Full article

Imaging spectroscopy allows us to identify surface materials by analyzing the spectra resulting from the light–material interaction. In this preliminary study, we analyze a pair of hyperspectral cubes acquired by PRISMA (on 20 April 2021) and EO1- Hyperion (on 4 July 2015) over the Indonesian Lusi mud eruption. We show the potential suitability of using the two sensors for characterizing the mineralogical features in demanding “wet and muddy” environments such as Lusi. We use spectral library reflectance spectra like Illite Chlorite from the USGS spectral library, which are known to be associated with Lusi volcanic products, to identify minerals. In addition, we have measured the reflectance spectra and composition of Lusi sampled mud collected in November 2014. Finally, we compare them with reflectance spectra from EO1-Hyperion and PRISMA. The use of hyperspectral sensors at improved SNR, such as PRISMA, has shown the potential to determine the mineral composition of Lusi PRISMA data, which allowed the distinction of areas with different turbidities as well. Artifacts in the VNIR spectral region of the L2 PRISMA reflectance product were found, suggesting that future work needs to take into account an independent atmospheric correction rather than using the L2D PRISMA product. Full article

An accurate assessment of soil fertility is crucial for monitoring environmental dynamics, improving agricultural productivity, and achieving sustainable land management and utilization. The inherent complexity and spatiotemporal heterogeneity of soils result in significant challenges in soil fertility assessment. Therefore, this study focused on developing a rapid, economical, and precise approach to evaluate soil fertility through the application of visible-near-infrared spectroscopy (VNIR). To achieve this, we utilized the Land Use and Cover Area Frame Survey (LUCAS) dataset and employed a variety of prediction models, including partial least squares regression, support vector machines (SVMs), random forest, and convolutional neural networks, to estimate various soil properties and overall soil fertility. The results showed that the SVM model had the highest prediction accuracy, particularly for clay content (coefficient of determination (R²) = 0.79, ratio of performance to interquartile range (RPIQ) = 3.04), pH (R² = 0.84, RPIQ = 4.54), total nitrogen (N) (R² = 0.80, RPIQ = 2.40), and cation exchange capacity (CEC) (R² = 0.83, RPIQ = 3.16). A soil fertility index (SFI) was developed based on factor analysis, integrating nine essential soil properties: clay content, silt content, sand content, pH, carbonate content, N, soluble phosphorus, soluble potassium, and CEC. We compared direct and indirect prediction models for estimating SFI and found that both models showed high accuracy (mean value of R² = 0.80, mean value of RPIQ = 2.21). Additionally, SFI was classified into five classes to provide insights for precision agriculture. The kappa coefficient was 0.63, which indicated that the SFI evaluation results between VNIR and chemical analysis were relatively consistent. This study provides a theoretical foundation of real-time soil fertility monitoring for the optimization of agricultural practices. Full article

In the pursuit of enhancing the wine production process through the utilization of new technologies in viticulture, this study presents a novel approach for the rapid assessment of wine grape maturity levels using non-destructive, in situ infrared spectroscopy and artificial intelligence techniques. Building upon our previous work focused on estimating sugar content (${}^{\circ }$Brix) from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, this research expands its scope to encompass pH and titratable acidity, critical parameters determining the grape maturity degree, and in turn, wine quality, offering a more representative estimation pathway. Data were collected from four grape varieties—Chardonnay, Malagouzia, Sauvignon Blanc, and Syrah—during the 2023 harvest and pre-harvest phenological stages in the vineyards of Ktima Gerovassiliou, northern Greece. A comprehensive spectral library was developed, covering the VNIR–SWIR spectrum (350–2500 nm), with measurements performed in situ. Ground truth data for pH, titratable acidity, and sugar content were obtained using conventional laboratory methods: total soluble solids (TSS) (${}^{\circ }$Brix) by refractometry, titratable acidity by titration (expressed as mg tartaric acid per liter of must) and pH by a pH meter, analyzed at different maturation stages in the must samples. The maturity indicators were predicted from the point hyperspectral data by employing machine learning algorithms, including Partial Least Squares regression (PLS), Random Forest regression (RF), Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), in conjunction with various pre-processing techniques. Multi-output models were also considered to simultaneously predict all three indicators to exploit their intercorrelations. A novel multi-input–multi-output CNN model was also proposed, incorporating a multi-head attention mechanism and enabling the identification of the spectral regions it focuses on, and thus having a higher interpretability degree. Our results indicate high accuracy in the estimation of sugar content, pH, and titratable acidity, with the best models yielding mean $R^2$ values of 0.84, 0.76, and 0.79, respectively, across all properties. The multi-output models did not improve the prediction results compared to the best single-output models, and the proposed CNN model was on par with the next best model. The interpretability analysis highlighted that the CNN model focused on spectral regions associated with the presence of sugars (i.e., glucose and fructose) and of the carboxylic acid group. This study underscores the potential of portable spectrometry for real-time, non-destructive assessments of wine grape maturity, thereby providing valuable tools for informed decision making in the wine production industry. By integrating pH and titratable acidity into the analysis, our approach offers a holistic view of grape quality, facilitating more comprehensive and efficient viticultural practices. Full article