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Accurate prediction of soil properties is essential for sustainable land management and precision agriculture. This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. Utilizing the LUCAS soil dataset, the model analyzes spectral data to estimate key soil properties, including organic carbon (OC), nitrogen (N), calcium carbonate (CaCO3), and pH (in H2O). The Long Short-Term Memory (LSTM) component captures temporal dependencies, the Convolutional Neural Network (CNN) extracts spatial features, and the attention mechanism highlights critical information within the data. Experimental results show that the proposed model achieves excellent prediction performance, with coefficient of determination (R2) values of 0.949 (OC), 0.916 (N), 0.943 (CaCO3), and 0.926 (pH), along with corresponding ratio of percent deviation (RPD) values of 3.940, 3.737, 5.377, and 3.352. Both R2 and RPD values exceed those of traditional machine learning models, such as partial least squares regression (PLSR), support vector machine regression (SVR), and random forest (RF), as well as deep learning models like CNN-LSTM and Gated Recurrent Unit (GRU). Additionally, the proposed model outperforms S-AlexNet in effectively capturing temporal and spatial patterns. These findings emphasize the potential of the proposed model to significantly enhance the accuracy and reliability of soil property predictions by capturing both temporal and spatial patterns effectively. [ABSTRACT FROM AUTHOR]

High-quality coal emits a smaller amount of harmful substances during the combustion process, which greatly reduces the environmental hazard. The sulfur content of coal is one of the important indicators that determine coal quality. The world's demand for high-quality coal is increasing. This is challenging for the coal mining industry. Therefore, how to quickly determine the sulfur content of coal in coal mining areas has always been a research difficulty. This study is the first to map the distribution of sulfur content in opencast coal mines using field-remote sensing data, and propose a novel method for evaluating coal mine composition. We collected remote sensing, field visible and near-infrared (Vis-NIR) spectroscopy data and built analytical models based on a tiny neural network based on the convolutional neural network. The experimental results show that the proposed method can effectively analyze the coal sulfur content. The coal recognition accuracy is 99.65%, the root-mean-square error is 0.073 and the R is 0.87, and is better than support vector machines and partial least squares methods. Compared with traditional methods, the proposed method shows many advantages and superior performance. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is a very important indicator of soil quality, where even small changes can have large effects on the global carbon cycle, climate change, soil fertility and plant growth conditions. The aims of this study were to investigate changes in soil SOC using two types of biostimulants, to confirm the quality of different SOC determination methods, and to estimate fertiliser requirements and winter crop income and costs. Two methods were used to determine the concentration of SOC: the first was conventional sampling of two soil layers and laboratory analysis. The second method measured SOC using a mounted proximal soil sensor with an integrated visible and near-infrared (VIS-NIR) spectroscopic system. Experimental studies were carried out in three scenarios, of which SC1 used a biostimulant consisting of two components: one for soil (humus) and one for plants (leaf-special), SC2 used a biostimulant consisting of many bacterial species and SC3 was a control without biostimulant. The results of the 3-year experimental studies showed that the use of VIS-NIR spectroscopy in the field allows the prediction of soil SOC concentrations with sufficient accuracy. The coefficient of determination R2 ranged from 0.819 to 0.959 compared to the conventional laboratory SOC test. In the SC1 and SC2 scenarios with biostimulants, R2 was higher than in the SC3 control. The use of biostimulants significantly increased the yield of winter wheat and winter oilseed rape and reduced the need for nitrogen fertiliser per tonne of grain yield for both winter wheat (23.0–37.3 %) and winter oilseed rape (6.3–32.1 %). The economic analysis showed that the application of biostimulants increased the relative profit in all years from EUR 54 ha−1 (for winter oilseed rape) to EUR 143 ha−1 (for winter wheat) compared to the control scenario.

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. [ABSTRACT FROM AUTHOR]

In recent years, due to breeding improvements, strawberries with low anthocyanin content and a white rind are now available, and they are highly valued in the market. Strawberries with white skin color do not turn red when ripe, making it difficult to judge ripeness. The soluble solids content (SSC) is an indicator of fruit quality and is closely related to ripeness. In this study, visible–near-infrared (Vis-NIR) spectroscopy and near-infrared (NIR) spectroscopy are used for non-destructive evaluation of the SSC. Vis-NIR (500–978 nm) and NIR (908–1676 nm) data collected from 180 samples of "Tochigi iW1 go" white strawberries and 150 samples of "Tochigi i27 go" red strawberries are investigated. The white strawberry SSC model developed by partial least squares regression (PLSR) in Vis-NIR had a determination coefficient R2p of 0.89 and a root mean square error prediction (RMSEP) of 0.40%; the model developed in NIR showed satisfactory estimation accuracy with an R2p of 0.85 and an RMSEP of 0.43%. These estimation accuracies were comparable to the results of the red strawberry model. Absorption derived from anthocyanin and chlorophyll pigments in white strawberries was observed in the Vis-NIR region. In addition, a dataset consisting of red and white strawberries can be used to predict the pigment-independent SSC. These results contribute to the development of methods for a rapid fruit sorting system and the development of an on-site ripeness determination system. [ABSTRACT FROM AUTHOR]

Addressing within-field spatial variability for nitrogen (N) management to avoid over and under-use of nitrogen is crucial for optimizing crop productivity and ensuring environmental sustainability. In this study, we investigated the economic, environmental, and agronomic benefits of variable rate nitrogen application in potato (Solanum tuberosum L.). An online visible and near-infrared (vis-NIR) spectroscopy sensor was utilized to predict soil moisture content (MC), pH, total organic carbon (TOC), extractable phosphorus (P), potassium (K), magnesium (Mg), and cation exchange capacity (CEC) using a partial least squares regression (PLSR) models. The crop's normalized difference vegetation index (NDVI) from Sentinel-2 satellite images was incorporated into online measured soil data to derive fertility management zones (MZs) maps after homogenous raster and clustering analyses. The MZs maps were categorized into high fertile (VR-H), medium–high fertile (VR-MH), medium–low fertile (VR-ML), and low fertile (VR-L) zones. A parallel strip experiment compared variable rate nitrogen (VR-N) with uniform rate (UR) treatments, adjusting nitrogen levels based on fertility zones as 50% less for VR-H, 25% less for VR-MH, 25% more for VR-ML, and 50% more for VR-L zones compared to the UR treatment. The results showed that the VR-H zone received a 50% reduction in N fertilizer input and demonstrated a significantly higher crop yield compared to the UR treatment. This implies a potential reduction in negative environmental impact by lowering fertilizer costs while maintaining robust crop yields. In total, the VR-N treatment received an additional 1.2 Kg/ha of nitrogen input, resulting in a crop yield increase of 1.89 tons/ha. The relative gross margin for the VR-N treatment compared to the UR treatment is 374.83 EUR/ha, indicating substantial profitability for the farmer. To further optimize environmental benefits and profitability, additional research is needed to explore site-specific applications of all farm resources through precision agricultural technologies. [ABSTRACT FROM AUTHOR]

The status of soil nutrient content is a fundamental factor affecting changes in soil quality, influencing the growth conditions and yield levels of crops. The practicality of combining visible and near-infrared spectroscopy to evaluate soil organic matter (SOM) and total nitrogen (TN) may give an alternative to soil physicochemical examination in the laboratory, which is laborious and contaminative. A total of 394 Ferralsols soil samples were gathered from navel orange orchards located in the province of Jiangxi, China. To enhance the spectrum information, the spectra were preprocessed using five different techniques, including lg(1/R), multiplicative scatter correction, standard normal variate), detrending and Savitzky-Golay smoothing. Four variable selection algorithms—competitive adaptive reweighted sampling, successive projections algorithm, random frog, and genetic algorithm – were combined with three multivariate methods—partial least squares regression, multiple linear regression, and least squares support vector machine. The most efficient strategy combines LSSVM calibration methods with GA and lg(1/R) preprocessing. It generates values for the determination coefficient of prediction, root mean square error of prediction, and residual predictive deviation that are as follows: 0.8948, 0.1597, and 3.0949, respectively, for SOM; and 0.9129, 0.0021, and 3.4014, respectively, for TN. The results indicate that this method can accurately determine the SOM and TN in agricultural land soil, facilitating the timely adjustment of soil management measures. [ABSTRACT FROM AUTHOR]

High-quality coal emits a smaller amount of harmful substances during the combustion process, which greatly reduces the environmental hazard. The sulfur content of coal is one of the important indicators that determine coal quality. The world’s demand for high-quality coal is increasing. This is challenging for the coal mining industry. Therefore, how to quickly determine the sulfur content of coal in coal mining areas has always been a research difficulty. This study is the first to map the distribution of sulfur content in opencast coal mines using field-remote sensing data, and propose a novel method for evaluating coal mine composition. We collected remote sensing, field visible and near-infrared (Vis-NIR) spectroscopy data and built analytical models based on a tiny neural network based on the convolutional neural network. The experimental results show that the proposed method can effectively analyze the coal sulfur content. The coal recognition accuracy is 99.65%, the root-mean-square error is 0.073 and the R is 0.87, and is better than support vector machines and partial least squares methods. Compared with traditional methods, the proposed method shows many advantages and superior performance.

Many global environments face increasing pressures on soil resources, and effective, scalable methods for assessment of soil condition and capital are essential to respond to tangible soil threats. This situation is common across Pacific Island Countries and Territories (PICTs), where high throughput soil analysis laboratories are limited, and issues such as soil organic carbon decline, acidification and fertility declines are present. Soil spectral inference presents an opportunity in such regions to provide rapid insights into soil capital and condition, though the need for robust calibration libraries remains a limiting factor. This work investigates the utility of a regionally appropriate spectral library, the New Zealand Soil Spectral Library (NZSSL) to support the development of soil spectral inference in data-poor environments, such as PICTs, through a case study on the island of Tongatapu in The Kingdom of Tonga. We contrast the performance of existing partial least squares regression (PLSR) models developed for New Zealand soils on soils from Tongatapu and explore the opportunities for enhancement of predictions formed through memory-based learning (MBL) supplemented with local data. Our work shows the potential for cost-effective and timely soil monitoring through soil spectral inference in PICTs. The work further underscores the importance of regional cooperation and data-sharing for addressing soil security.

Accurate prediction of soil properties is essential for sustainable land management and precision agriculture. This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. Utilizing the LUCAS soil dataset, the model analyzes spectral data to estimate key soil properties, including organic carbon (OC), nitrogen (N), calcium carbonate (CaCO3), and pH (in H2O). The Long Short-Term Memory (LSTM) component captures temporal dependencies, the Convolutional Neural Network (CNN) extracts spatial features, and the attention mechanism highlights critical information within the data. Experimental results show that the proposed model achieves excellent prediction performance, with coefficient of determination (R2) values of 0.949 (OC), 0.916 (N), 0.943 (CaCO3), and 0.926 (pH), along with corresponding ratio of percent deviation (RPD) values of 3.940, 3.737, 5.377, and 3.352. Both R2 and RPD values exceed those of traditional machine learning models, such as partial least squares regression (PLSR), support vector machine regression (SVR), and random forest (RF), as well as deep learning models like CNN-LSTM and Gated Recurrent Unit (GRU). Additionally, the proposed model outperforms S-AlexNet in effectively capturing temporal and spatial patterns. These findings emphasize the potential of the proposed model to significantly enhance the accuracy and reliability of soil property predictions by capturing both temporal and spatial patterns effectively.

ABSTRACT: Duck eggs are widely-consumed food and cooking ingredient. The heavier yolk weight (YW) corresponds to a larger size and greater value. However, there is no nondestructive method available to estimate the weight of the yolk. Accurate weight prediction of duck egg yolks must combine both phenotypic and internal information. In this research, we used Visible-Near Infrared (VIS-NIR) spectroscopy to obtain internal information of duck eggs, and a high-definition camera to capture their phenotypic features. YW was predicted by combining the reduced spectral and RGB image information with the whole egg weight. We also investigated the impact of color and thickness of the duck egg on spectral transmittance (ST), as these factors can influence the extent of ST. The results showed that the spectral curves of duck eggs produced 2 peaks and 1 valley, which may be caused by the dual-frequency absorption of the C-H group and O-H group, and can be used to symbolize the internal information of duck eggs. The ST was somewhat affected by the color and thickness of the duck eggshell. Before modelling, Principal component analysis (PCA) was used to significantly reduce the dimensionality of the RGB image with spectral data. A partial least squares regression (PLSR) model was utilized to fit all the features. The test set yielded a coefficient of determination (R2) of 0.82 and a Root Mean Squared Error (RMSE) of 1.05 g. After removing the eggshell's color and thickness features, the model showed an R2 of 0.79 and an RMSE of 1.11 g. This study demonstrated that the yolk weight of duck eggs can be estimated using VIS-NIR spectroscopy, RGB images and whole egg weight. Furthermore, the effects of shell color and thickness can be neglected.

Theoretical models to describe and evaluate sorption of hydrocarbons on soil particles are well documented but experimental results in the literature are scarce. This study describes the use of a spectral index to evaluate sorption phenomena in oil-contaminated soils at micro and macro levels. Experiments involved oil-dosing from 0.25 mL to 4 mL and the application of a spectral index to estimate the sorption capacity of different soils based on change in statistically derived Critical Point (CP). Results show that sorption of oil varies with soil type, specifically the proportion of clay in a soil/sediment and the clay species, in the order smectite > illite > kaolinite. In support of theoretical models, this work proposes a model from experimental data that explains observed phenomena of soil-oil interaction with changing soil composition. The location and prominence of the CP is also strongly affected by soil moisture, particularly in coarse-grained soils, potentially providing insight to soil-oil–water interaction. The methods developed using a simple synthetic crude provide potential to explore other contamination scenarios including different soil compositions, types of petroleum products and different environmental controls.

Though soil nutrients play important roles in maintaining soil fertility and crop growth, their estimation requires direct soil sampling followed by laboratory analysis incurring huge cost and time. In this research work, soil nutrients were predicted using VIs-NIR reflectance spectroscopy (range 350–2500 nm) with Partial Least Squares Regression (PLSR) and Support Vector Machine Regression Model (SVMR) model through principal component analysis. Two hundred soil samples were collected from Tarekswar, Hooghly, West Bengal, India to predict eight selected soil nutrients, such as soil organic carbon (OC), pH, available nitrogen (N), available phosphorus (P), available potassium(K), electric conductivity (EC), zinc (Zn) and soil texture (sand, silt, and clay) levels. The OC content was predicted with sound accuracy (R2: 0.82, RPD: 2.28, RMSE: 0.13, RPIQ: 4.15 FD-SG), followed by P (R2: 0.71, RPD: 1.83, RMSE: 4575, RPIQ: 3.44 1st derivative). The soil parameters sensitive to the particular band of visible spectrum were also identified viz. wavelengths of 409, 444, 591 and 592 nm for OC, 430 and 505 nm for P, 464 nm for K; 580 nm for Zn, 492,511,596 and 698 nm for N; 493, 569 and 665 nm for EC; 492,567 and 652 nm for pH; 457 nm for sand and 515 nm for clay.The soil nutrient levels were predicted by PLSR and SVMR models through PCA and Sentinel 2 imagery and soil suitability map were also generated for seven soil parameters such as OC, pH, EC, N, P, K and clay content. Through map query tool in ArcGIS software environment the PLSR and SVMR model successfully identify the suitability class with level of accuracy of 87.2% and 88.9%, respectively, against the direct soil analysis based suitability mapping.The machine learning technique based soil nutrient and soil suitability prediction can be easily adopted in different regions. This will reduce the cost of laboratory soil analysis and optimize the total time requirement.

This study aimed to determine the integrated freshness index (IFI) of eggs using Vis-NIR spectroscopy and optimized support vector regression, which gave the first insight into the freshness quality of eggs from the biochemical essence of quality changes. In this work, Vis-NIR transmission spectra of brown-shell and pink-shell egg samples were analyzed between 500 nm and 900 nm. Standard normal variables (SNV) were used to normalize the spectral data, and the Shuffled Frog Leaping Algorithm (SFLA) and Competitive Adaptive Reweighted Sampling (CARS) were used to choose the optimal wavelengths. The quantitative analysis model of IFI was developed using a support vector regression (SVR) that was optimized using Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). After conducting a comparative analysis, it was determined that the GA-SVR model based on 63 wavelengths screened by the SFLA best predicted IFI with a training set coefficient of determination (Rc2) of 0.900, root means square error (RMSEC) of 0.005, a prediction set coefficient of determination (Rp2) of 0.816, root mean square error (RMSEP) of 0.012 and relative analysis error (RPD) of 2.077. The results demonstrate that the model can be used to simultaneously perform nondestructive detection of two distinct egg IFI variants, suggesting broader applicability and enhanced model reliability.

In the present work the effect of alkali ions (Li, Na, K) and the combination of them on structural and spectroscopic properties of Nd3+ doped silicate glasses were systematically investigated. Results from infrared and Raman spectroscopy, as well as density and refractive index, were used to investigate the effect on the structural properties of the silicate glasses. Absorption and emission spectra of the glass samples were measured in the UV-VIS and NIR ranges. Relatively large, stimulated emission cross-section at 1.06 μm was obtained for samples containing a combination of alkali ions, especially with potassium. Moreover, the splitting induced by the Stark effect on the hypersensitive transition of Nd3+,4I9/2 → 4G5/2, 2G7/2 was larger for the samples containing potassium, probably due to its larger ionic size. Judd-Ofelt theory was applied to evaluate the phenomenological intensity parameters Ω2, Ω4, Ω6, transition probabilities, radiative lifetimes and branching ratios related to Nd3+ ions, calculated from optical absorption and luminescence spectra and the results were compared to the values obtained for different Nd-doped glasses reported in the literature.

Though soil nutrients play important roles in maintaining soil fertility and crop growth, their estimation requires direct soil sampling followed by laboratory analysis incurring huge cost and time. In this research work, soil nutrients were predicted using VIs-NIR reflectance spectroscopy (range 350–2500 nm) with Partial Least Squares Regression (PLSR) and Support Vector Machine Regression Model (SVMR) model through principal component analysis. Two hundred soil samples were collected from Tarekswar, Hooghly, West Bengal, India to predict eight selected soil nutrients, such as soil organic carbon (OC), pH, available nitrogen (N), available phosphorus (P), available potassium(K), electric conductivity (EC), zinc (Zn) and soil texture (sand, silt, and clay) levels. The OC content was predicted with sound accuracy (R2: 0.82, RPD: 2.28, RMSE: 0.13, RPIQ: 4.15 FD-SG), followed by P (R2: 0.71, RPD: 1.83, RMSE: 4575, RPIQ: 3.44 1st derivative). The soil parameters sensitive to the particular band of visible spectrum were also identified viz. wavelengths of 409, 444, 591 and 592 nm for OC, 430 and 505 nm for P, 464 nm for K; 580 nm for Zn, 492,511,596 and 698 nm for N; 493, 569 and 665 nm for EC; 492,567 and 652 nm for pH; 457 nm for sand and 515 nm for clay. The soil nutrient levels were predicted by PLSR and SVMR models through PCA and Sentinel 2 imagery and soil suitability map were also generated for seven soil parameters such as OC, pH, EC, N, P, K and clay content. Through map query tool in ArcGIS software environment the PLSR and SVMR model successfully identify the suitability class with level of accuracy of 87.2% and 88.9%, respectively, against the direct soil analysis based suitability mapping. The machine learning technique based soil nutrient and soil suitability prediction can be easily adopted in different regions. This will reduce the cost of laboratory soil analysis and optimize the total time requirement. [ABSTRACT FROM AUTHOR]

Grape quality and ripeness play a crucial role in producing exceptional wines with high-value characteristics, which requires an effective assessment of grape ripeness. The primary purpose of this research is to explore the possible application of visible–near-infrared spectral (Vis-NIR) technology for classifying the maturity stages of wine grapes based on quality indicators. The reflection spectra of Cabernet Sauvignon grapes were recorded using a spectrometer in the spectral range of 400 nm to 1029 nm. After measuring the soluble solids content (SSC), total acids (TA), total phenols (TP), and tannins (TN), the grape samples were categorized into five maturity stages using a spectral clustering method. A traditional supervised classification method, a support vector machine (SVM), and two deep learning techniques, namely stacked autoencoders (SAE) and one-dimensional convolutional neural networks (1D-CNN), were employed to construct a discriminant model and investigate the association linking grape maturity stages and the spectral responses. The spectral data went through three commonly used preprocessing methods, and feature wavelengths were extracted using a competitive adaptive reweighting algorithm (CARS). The spectral data model preprocessed via multiplicative scattering correction (MSC) outperformed the other two preprocessing methods. After preprocessing, a comparison was made between the discriminant models established with full and effective spectral data. It was observed that the SAE model, utilizing the feature spectrum, demonstrated superior overall performance. The classification accuracies of the calibration and prediction sets were 100% and 94%, respectively. This study showcased the dependability of combining Vis-NIR spectroscopy with deep learning methods for rapidly and accurately distinguishing the ripeness stage of grapes. It has significant implications for future applications in wine production and the development of optoelectronic instruments tailored to the specific needs of the winemaking industry. [ABSTRACT FROM AUTHOR]

In recent years, due to breeding improvements, strawberries with low anthocyanin content and a white rind are now available, and they are highly valued in the market. Strawberries with white skin color do not turn red when ripe, making it difficult to judge ripeness. The soluble solids content (SSC) is an indicator of fruit quality and is closely related to ripeness. In this study, visible–near-infrared (Vis-NIR) spectroscopy and near-infrared (NIR) spectroscopy are used for non-destructive evaluation of the SSC. Vis-NIR (500–978 nm) and NIR (908–1676 nm) data collected from 180 samples of “Tochigi iW1 go” white strawberries and 150 samples of “Tochigi i27 go” red strawberries are investigated. The white strawberry SSC model developed by partial least squares regression (PLSR) in Vis-NIR had a determination coefficient R2p of 0.89 and a root mean square error prediction (RMSEP) of 0.40%; the model developed in NIR showed satisfactory estimation accuracy with an R2p of 0.85 and an RMSEP of 0.43%. These estimation accuracies were comparable to the results of the red strawberry model. Absorption derived from anthocyanin and chlorophyll pigments in white strawberries was observed in the Vis-NIR region. In addition, a dataset consisting of red and white strawberries can be used to predict the pigment-independent SSC. These results contribute to the development of methods for a rapid fruit sorting system and the development of an on-site ripeness determination system.

Total soluble solids (TSS) is a key variable taken into account in determining optimal grape maturity for harvest. In this work, partial least square (PLS) regression models were developed to estimate TSS content for Godello, Verdejo (white), Mencía, and Tempranillo (red) grape varieties based on diffuse spectroscopy measurements. To identify the most suitable spectral range for TSS prediction, the regression models were calibrated for four datasets that included the following spectral ranges: 400–700 nm (visible), 701–1000 nm (near infrared), 1001–2500 nm (short wave infrared) and 400–2500 nm (the entire spectral range). We also tested the standard normal variate transformation technique. Leave-one-out cross-validation was implemented to evaluate the regression models, using the root mean square error (RMSE), coefficient of determination (R2), ratio of performance to deviation (RPD), and the number of factors (F) as evaluation metrics. The regression models for the red varieties were generally more accurate than the models of those for the white varieties. The best regression model was obtained for Mencía (red): R2 = 0.72, RMSE = 0.55 °Brix, RPD = 1.87, and factors n = 7. For white grapes, the best result was achieved for Godello: R2 = 0.75, RMSE = 0.98 °Brix, RPD = 1.97, and factors n = 7. The methodology used and the results obtained show that it is possible to estimate TSS content in grapes using diffuse spectroscopy and regression models that use reflectance values as predictor variables. Spectroscopy is a non-invasive and efficient technique for determining optimal grape maturity for harvest. [ABSTRACT FROM AUTHOR]

The estimation of the level of the soil organic carbon (SOC) content plays an important role in assessing the soil health state. Visible and Near Infrared Diffuse Reflectance Spectroscopy (Vis-NIR DRS) is a fast and cheap tool for measuring the SOC. However, when this technology is applied on a larger area, the soil prediction accuracy decreases due to the heterogeneity of the samples. In this paper, we first investigate the global model performance in the LUCAS EU-wide topsoil database. Then, different clustering strategies were tested, including the k-means clustering based on the principal component analysis (PCA) and hierarchical clustering, combined with the partial least squares regression (PLSR) models, and a clustering based on a local PLSR approach. The best validation results were obtained for the local PLSR approach with R2 = 0.75, root mean squared error of prediction (RMSEP) = 13.38 g/kg and ratio of performance to interquartile range (RPIQ) = 2.846, but the algorithm running time was 30.05 s. Similar results were obtained for the k-means clustering method with R2 = 0.75, RMSEP = 14.61 g/kg and RPIQ = 2.844, at only 4.52 s. This study demonstrates that the PLSR approach based on k-means clustering is able to achieve similar prediction accuracy as the local PLSR approach, while significantly improving the algorithm speed. This provides the theoretical basis for adapting the spectral soil model to the needs of real-time SOC quantification.

The coal concentration in mine water is the main indicator of mine water discharge. The accurate determination of coal concentration is of great significance for the purification and secondary utilization of mine water. To study the spectral inversion method of coal concentration in mine water, samples with different coal concentrations of 0mg/L-1000mg/L are prepared in this paper, and the ASD Field Spec 4 (350-2500nm) spectrometer is used for spectral collection. It is found that the maximum influence of different coal content on spectral reflectance is 0.9. Based on this, the CK-CNN (C-K-Convolutional Neural Networks) inversion model of coal content in mine water is proposed. This model uses the CARS (Competitive Adapative Reweighted Sampling) algorithm to extract sensitive wave bands and uses CNN (Convolutional Neural Networks) to establish spectral inversion model in sensitive wave bands, K-fold cross-validation is used to optimize the model, the model inversion accuracy is R2 = 0.9994, RMSE=6.1401, RPD=41.9692. In this study, CK-CNN was compared with five models: SPA+RF, CARS+RF, SPA+CNN, All Band +CNN, and CARS+CNN. The results show that the CK-CNN model has the best effect. In addition, the concentration of water coal in Jiaozuo Zhongma Coal Mine is 18.75mg/L, the actual concentration measured in the laboratory is 18.92mg/L, and the inversion error is 0.17mg/L. The inversion results meet the requirements of laboratory measurement in GB11901-1989. The research results show that the hyperspectral remote sensing in the visible-near-infrared band can quickly detect the coal concentration in the mine water. The CK-CNN model provides a new method for the determination of the coal content in the mine water. It has great significance to promote research on the influence of the coal concentration in the mine water on the Vis-NIR spectrum.

The accurate detection of honey adulteration is paramount for maintaining the quality and authenticity of honey products. In this study, we introduce a novel feature selection method, termed Optimal Subspace Wavelength Reduction (OSWR), and integrate it with reflectance Visible-Near Infrared (Vis-NIR) spectroscopy to enhance the discrimination between pure and adulterated honey and predict adulteration levels. OSWR efficiently addresses the dimensionality challenge of large spectral datasets, reducing 2151 wavelengths to a compact and informative set of 39 wavelengths. We comprehensively evaluate machine learning (ML) models, focusing on OSWR as a pivotal component of our methodology. Our results reveal remarkable success in discriminating among pure honey, adulterated honey, and sugar syrup, with an impressive classification accuracy of 96.67% achieved using OSWR, coupled with Standard Normal Variate (SNV) preprocessing, Linear Discriminant Analysis (LDA) feature extraction, and K-Nearest Neighbors (KNN) classification. Furthermore, this study demonstrates the effectiveness of OSWR for predicting adulteration levels, where it achieves an accuracy of as high as 92.67% when coupled with SNV, LDA, and KNN. This work highlights the potential of OSWR as a feature selection method in the context of honey adulteration detection. Through the integration of Vis-NIR spectroscopy and OSWR, our approach offers a tool for enhancing honey products’ quality and authenticity assessment, potentially simplifying spectral data analysis.

Global pressures to improve soil organic carbon sequestration and soil health in general amongst the world’s agricultural soils are creating a demand for improved practice to drive positive and sustainable changes in the natural capital of soils. Incentive programs aimed to promote this must be informed by accurate observations of the state of soils, both temporally and spatially. Soil spectral inference is a useful method for capturing the state of soils cost-effectively, but the price of standard laboratory grade visible and near-infrared (Vis-NIR) sensors can limit its application. Further, the acquisition of spectra by these laboratory grade sensors is performed primarily in air-dried and ground condition, adding a time lag to information retrieval. Recently, low-cost, portable miniaturised near-infrafred (NIR) spectrometers have become available and have shown to be a viable alternative for the measurement of several agronomically important soil properties, which are also vital to the maintenance of soil health, including soil organic carbon (SOC), and cation exchange capacity (CEC). However, the implementation of new spectrometers, to new locations requires the creation of new spectral libraries, an expensive and labour-intensive process requiring large amounts of soil analytical and spectral data gathering. Thus, existing, laboratory grade Vis-NIR spectral libraries present a high-quality and high-resolution resource to leverage. This work demonstrates how existing spectral library resources can be accessed with cheaper, portable miniaturised NIR spectrometers with appropriate spectral filtering, and appropriate transformation matrices. In addition, the work shows that by correcting for the influences of spectral differences between soils scanned in field condition, and those prepared for analysis in the laboratory, greater uptake of spectral inference as a tool to evaluate the state of soils can be enabled. This work also demonstrates how large existing laboratory grade spectral libraries such as the CSIRO national Australian Vis-NIR soil spectral library can be queried and using memory-based learning or similar methods, such as RS-Local, and the most appropriate samples may be identified to be used for the prediction of soil properties. This work builds off an existing framework for the use of soil spectral inference for monitoring the state of soil, the Australian 2021 Soil Organic Carbon Credits Methodology Determination. Methods are demonstrated for the prediction of nine agronomically important soil properties, SOC, pH in water, pH in CaCl2, electrical conductivity, CEC, and exchangeable Ca, K, Mg and Na. For SOC a model using only 20 local samples was produced in this work with a Lin’s concordance correlation coefficient (LCCC) of 0.72, surpassing both the minimum requirement under the carbon credits methodology determination (LCCC 0.6), and a 50 sample local only model (LCCC 0.61). This example demonstrates that a significant further potential cost saving in laboratory analysis across soil monitoring projects can be achieved through selectively leveraging a large spectral library resource.

Agricultural soils serve as crucial storage sites for soil organic carbon (SOC). Their appropriate management is pivotal for mitigating climate change. Continuous monitoring is imperative to evaluate spatial and temporal changes in SOC within agricultural fields. In-field datasets of Vis-NIR soil spectra were collected on a long-term experimental site using an on-the-go spectrophotometer. Data processing for continuous SOC prediction involves a two-step modeling approach. In Step 1, a partial least square (PLSR) regression model is trained to establish a relationship between the SOC content and spectral information, including spectral preprocessing. In Step 2, the predicted SOC content obtained from the PLSR models is interpolated using ordinary kriging. Among the tested spectral preprocessing techniques and semivariogram models, Savitzky–Golay and the Gap-Segment derivative preprocessing along with a Gaussian semivariogram model, yielded the best performance resulting in a root mean square error of 1.24 and 1.26 g kg−1. A striping effect due to the transect-based data collection was addressed by testing the effectiveness of extending the spatial separation distance, employing data aggregation, and defining the distribution based on treatment plots using block kriging. Overall, the results highlight the high potential of on-the-go spectral Vis-NIR data for field-scale spatial-temporal monitoring of SOC. [ABSTRACT FROM AUTHOR]

Soil aggregate size and stability are two important factors affecting soil erodibility to wind erosion. In this study, we developed new models to quantify soil erodibility to wind erosion by looking at the mean weighted aggregate diameter (MWD) and the wind erodible fraction (EF) of the soil. These two erodibility indices together with the spectral reflection of soil in the range of Vis– NIR (400–2500 nm) were measured in 511 soil samples. Pedo-transfer functions (PTF) and Spectro-transfer functions (STF) were built using the Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), considering train(70%) and test (30%) datasets. Result showed that shear strength (SS), organic matter (OM), penetration resistance (PR), and clay content had a significant coefficient (p

Fruit quality assessment plays a crucial role in determining their market value, consumer acceptance, and post-harvest management. In recent years, spectroscopic techniques have gained significant attention as non-destructive methods for evaluating fruit quality. In this study, we propose a novel deep-learning network, called GCNN-LSTM-AT, for the prediction of five important parameters of tangerines using visible and near-infrared spectroscopy (Vis–NIR). The quality attributes include soluble solid content (SSC), total acidity (TA), acid–sugar ratio (A/S), firmness, and Vitamin C (VC). The proposed model combines the strengths of graph convolutional network (GCN), convolutional neural networks (CNNs), and long short-term memory (LSTM) to capture both spatial and sequential dependencies in the spectra data, and incorporates an attention mechanism to enhance the discriminative ability of the model. To investigate the effectiveness and stability of the model, comparisons with three traditional machine-learning algorithms—moving window partial least squares (MWPLS), random forest (RF), and support vector regression (SVR)—and two deep neural networks—DeepSpectra2D and CNN-AT—are provided. The results have shown that the GCNN-LSTM-AT network outperforms other algorithms and models, achieving accurate predictions for SSC ( R 2 : 0.9885, RMSECV: 0.1430 ∘ Brix), TA ( R 2 : 0.8075, RMSECV: 0.0868%), A/S ( R 2 : 0.9014, RMSECV: 1.9984), firmness ( R 2 : 0.9472, RMSECV: 0.0294 kg), and VC ( R 2 : 0.7386, RMSECV: 29.4104 mg/100 g) of tangerines. [ABSTRACT FROM AUTHOR]

Kiwifruit consumption has increased due to its rich nutritional properties. Although 'Hayward' continues to be the main cultivar, others, such as yellow fleshed 'Jintao', are of increasing interest. The objective of this research was to evaluate the acceptability and storage performance of these two cultivars. Sensory evaluation of green 'Hayward' and yellow 'Jintao' kiwifruit were performed along cold storage for three seasons/years to follow the organoleptic characteristics through ripening, as well as the acquisition of their spectra by Vis-NIR. For 'Jintao' were performed two sensory evaluations per year at 2.5- and 4.5-months' storage and for 'Hayward' at 2.5-, 4.5- and 5.5-months' storage. The nonparametric Mann–Whitney test and Kruskal–Wallis ANOVA were performed to test the significant differences between the mean ranks among the storage time. A non-metric multidimensional scaling plot method using the ALSCAL algorithm in a seven-point Likert scale was applied to determine the relationships in the data, and a new approach using the receiver operating characteristic (ROC) analysis was tested. The last revealed that, for both cultivars, sweetness, acidity and texture were the variables with better scores for General flavor. Aroma was also important on 'Jintao'. A strong correlation between soluble solids content (SSC) and reflectance was found for both cultivars, with the 635–780 nm range being the most important. Regarding firmness, a good correlation with reflectance spectra was observed, particularly in 'Hayward' kiwifruit. Based on these results, Vis-NIR can be an objective alternative to explore for determination of the optimum eating-ripe stage. [ABSTRACT FROM AUTHOR]

Ripeness significantly affects the commercial values and sales of fruits. In order to monitor the change of grapes' quality parameters during ripening, a rapid and nondestructive method of visible-near-infrared spectral (Vis-NIR) technology was utilized in this study. Firstly, the physicochemical properties of grapes at four different ripening stages were explored. Data evidenced increasing color in redness/greenness (a*) and Chroma (C*) and soluble solids (SSC) content and decreasing values in color of lightness (L*), yellowness/blueness (b*) and Hue angle (h*), hardness, and total acid (TA) content as ripening advanced. Based on these results, spectral prediction models for SSC and TA in grapes were established. Effective wavelengths were selected by the competitive adaptive weighting algorithm (CARS), and six common preprocessing methods were applied to pretreat the spectra data. Partial least squares regression (PLSR) was applied to establish models on the basis of effective wavelengths and full spectra. The predictive PLSR models built with full spectra data and 1st derivative preprocessing provided the best values of performance parameters for both SSC and TA. For SSC, the model showed the coefficients of determination for calibration ( R C a l 2 ) and prediction ( R P r e 2 ) set of 0.97 and 0.93, respectively, the root mean square error for calibration set (RMSEC) and prediction set (RMSEP) of 0.62 and 1.27, respectively; and the RPD equal to 4.09. As for TA, the optimum values of R C a l 2 , R P r e 2 , RMSEC, RMSEP and RPD were 0.97, 0.94, 0.88, 1.96 and 4.55, respectively. The results indicated that Vis-NIR spectroscopy is an effective tool for the rapid and non-destructive detection of SSC and TA in grapes. [ABSTRACT FROM AUTHOR]

Soil exchange cations are a basic indicator of soil quality and environmental clean-up potential. The accurate and efficient acquisition of information on soil cation content is of great importance for the monitoring of soil quality and pollution prevention. At present, few scholars focus on soil exchangeable cations using remote sensing technology. This study proposes a new method for estimating soil cation content using hyperspectral data. In particular, we introduce Boruta and successive projection (SPA) algorithms to screen feature variables, and we use Guangdong Province, China, as the study area. The backpropagation neural network (BPNN), genetic algorithm–based back propagation neural network (GABP) and random forest (RF) algorithms with 10-fold cross-validation are implemented to determine the most accurate model for soil cation (Ca2+, K+, Mg2+, and Na+) content estimations. The model and hyperspectral images are combined to perform the spatial mapping of soil Mg2+ and to obtain the spatial distribution information of images. The results show that Boruta was the optimal algorithm for determining the characteristic bands of soil Ca2+ and Na+, and SPA was the optimal algorithm for determining the characteristic bands of soil K+ and Mg2+. The most accurate estimation models for soil Ca2+, K+, Mg2+, and Na+ contents were Boruta-RF, SPA-GABP, SPA-RF and Boruta-RF, respectively. The estimation effect of soil Mg2+ (R2 = 0.90, ratio of performance to interquartile range (RPIQ) = 3.84) was significantly better than the other three elements (Ca2+: R2 = 0.83, RPIQ = 2.47; K+: R2 = 0.83, RPIQ = 2.58; Na+: R2 = 0.85, RPIQ = 2.63). Moreover, the SPA-RF method combined with HJ-1A HSI images was selected for the spatial mapping of soil Mg2+ content with an R2 of 0.71 and RPIQ of 2.05. This indicates the ability of the SPA-RF method to retrieve soil Mg2+ content at the regional scale. [ABSTRACT FROM AUTHOR]

Early crop disease detection is one of the most important tasks in plant protection. The purpose of this work was to evaluate the early wheat leaf rust detection possibility using hyperspectral remote sensing. The first task of the study was to choose tools for processing and analyze hyperspectral remote sensing data. The second task was to analyze the wheat leaf biochemical profile by chromatographic and spectrophotometric methods. The third task was to discuss a possible relationship between hyperspectral remote sensing data and the results from the wheat leaves, biochemical profile analysis. The work used an interdisciplinary approach, including hyperspectral remote sensing and data processing methods, as well as spectrophotometric and chromatographic methods. As a result, (1) the VIS-NIR spectrometry data analysis showed a high correlation with the hyperspectral remote sensing data; (2) the most important wavebands for disease identification were revealed (502, 466, 598, 718, 534, 766, 694, 650, 866, 602, 858 nm). An early disease detection accuracy of 97–100% was achieved from fourth dai (day/s after inoculation) using SVM. [ABSTRACT FROM AUTHOR]

We describe the procedures and results of a geological field analysis campaign in the Río Tinto area. This geologically/biologically well-documented site with its rock/water/biology interaction represents an ideal open-air laboratory where to collect spectral data and samples useful for testing space instruments. During the field campaign, we collected a large set of VIS-NIR (0.35–2.5 μm) measurements using the ASD FieldSpec4 portable spectrometer both on biosignature-bearing rocks and on alteration hydrated products (sulfates, clays, oxides, etc.). Furthermore, as a comparison to the data collected in the field, we report the results of the micro-Raman analyses carried out in the laboratory on the collected mineral/rock samples. This work was conducted in the framework of the Mars Multispectral Imager for Subsurface Studies (Ma_MISS) instrument that is a miniaturized visible and near-infrared (VIS-NIR) spectrometer (0.5–2.3 μm) devoted to the Martian subsurface exploration and integrated into the drilling system of the ESA Rosalind Franklin rover mission. Ma_MISS will acquire spectral data on the Martian subsurface from the excavated borehole wall. The scientific results obtained by this campaign confirm that the Río Tinto site is important for enriching the scientific community’s grasp on the Martian environment and for obtaining key information on the mineralogical and geochemical evolution of the Martian surface/subsurface. In addition, this work provides crucial preparation for the exploitation and interpretation of the scientific data that the Ma_MISS instrument will supply during the active phase of the mission. This activity is also useful for defining the priorities of the astrobiological objectives on the ground.

Traditional biochemical methods are resource- and time-consuming; therefore, there is a need for cost-effective alternatives. A spectral analysis is one of the non-destructive techniques that are more widely used for fruit quality determination; however, references are needed for traditional methods. In this study, visible and near-infrared (Vis-NIR) spectroscopy was used to analyze the internal quality attributes of tomatoes. For the first time, 80 varieties with large differences in fruit size, shape, color, and internal structure were used for an analysis. The aim of this study was to develop models suitable to predict a taste index, as well as the content of lycopene, flavonoids, β-carotene, total phenols, and dry matter of intact tomatoes based on Vis-NIR reflectance spectra. The content of phytochemicals was determined in 80 varieties of tomatoes. A total of 140 Vis-NIR reflectance spectra were obtained using the portable spectroradiometer RS-3500 (Spectral Evolution Inc.). Partial least squares regression (PLS) and multiple scatter correction (MSC) were used to develop calibration models. Our results indicated that PLS models with good prediction accuracies were obtained. The present study showed the high capability of Vis-NIR spectroscopy to determine the content of lycopene and dry matter of intact tomatoes with a determination coefficient of 0.90 for both parameters. A regression fit of R 2   = 0.86, R 2   = 0.84, R 2   = 0.82, and R 2 =   0 .73 was also achieved for the taste index, flavonoids, β-carotene, and total phenols, respectively. [ABSTRACT FROM AUTHOR]

The nutritional diagnosis of crops is carried out through costly elemental analyses of different plant organs, particularly leaves, in the laboratory. However, visible and near-infrared (Vis-NIR) spectroscopy of unprocessed plant samples has a high potential as a faster, non-destructive, environmental-friendly alternative to elemental analyses. In this work, the potential of this technique to estimate the concentrations of macronutrients such as nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg), and micronutrients such as iron (Fe), manganese (Mn) and boron (B), in persimmon (Diospyros kaki L.) 'Rojo Brillante' leaves, has been investigated. Throughout the crop cycle variable rates of N and K were applied to obtain six nutritional status levels in persimmon trees in an experimental orchard. Then, leaves were systematically sampled throughout the cropping season from the different nutritional levels and spectral reflectance measurements were acquired in the 430–1040 nm wavelength range. The concentrations of nutrients were determined by inductively coupled plasma optical emission spectrometry (ICP-OES) for P, K, Ca, Mg, Fe, Mn and B after microwave digestion, while the Kjeldahl method was used for N. Then, partial least squares regression (PLS-R) was used to model the concentrations of these nutrients from the reflectance measurements of the leaves. The model was calibrated using 75% of the samples while the remaining 25% were left as the independent test set for external validation. The results of the test set indicated an acceptable validation for most of the nutrients, with determination coefficients (R2) of 0.74 for N and P, 0.54 for K, 0.77 for Ca, 0.60 for Mg, 0.39 for Fe, 0.69 for Mn and 0.83 for B. These findings support the potential use of Vis-NIR spectrometric techniques as an alternative to conventional laboratory methods for the persimmon nutritional status diagnosis although more research is needed to know how the models developed one year perform in ensuing years. [ABSTRACT FROM AUTHOR]

Soil nutrients play vital roles in vegetation growth and are a key indicator of land degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is important for ecological conservation, degradation monitoring, and precision farming. Currently, visible and near-infrared (Vis–NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis–NIR inversion models is extremely dependent on the number of samples. Limited samples may lead to low prediction accuracy of the models. Therefore, modeling and prediction based on a small sample size remain a challenge. This study proposes a method for the simultaneous augmentation of soil spectral and nutrient data (total nitrogen (TN), soil organic matter (SOM), total potassium oxide (TK2O), and total phosphorus pentoxide (TP2O5)) using a generative adversarial network (GAN). The sample augmentation range and the level of accuracy improvement were also analyzed. First, 42 soil samples were collected from the pika disturbance area on the QTP. The collected soils were measured in the laboratory for Vis–NIR and TN, SOM, TK2O, and TP2O5 data. A GAN was then used to augment the soil spectral and nutrient data simultaneously. Finally, the effect of adding different numbers of generative samples to the training set on the predictive performance of a convolutional neural network (CNN) was analyzed and compared with another data augmentation method (extended multiplicative signal augmentation, EMSA). The results showed that a GAN can generate data very similar to real data and with better diversity. A total of 15, 30, 60, 120, and 240 generative samples (GAN and EMSA) were randomly selected from 300 generative samples to be included in the real data to train the CNN model. The model performance first improved and then deteriorated, and the GAN was more effective than EMSA. Further shortening the interval for adding GAN data revealed that the optimal ranges were 30–40, 50–60, 30–35, and 25–35 for TK2O, TN, TP2O5, and SOM, respectively, and the validation set accuracy was maximized in these ranges. Therefore, the above method can compensate to some extent for insufficient samples in the hyperspectral prediction of soil nutrients, and can quickly and accurately estimate the content of soil TK2O, TN, TP2O5, and SOM. [ABSTRACT FROM AUTHOR]

This work illustrates a novel prototype of a transmittance hyperspectral imaging (HSI) scanner, operating in the 400–900 nm range, and designed on purpose for non-invasive analysis of photographic materials, such as negatives, films and slides. The instrument provides high-quality spectral data and high-definition spectral images on targets of small size (e.g., 35 mm film strips) and is the first example of HSI instrumentation specifically designed for applications in the photographic conservation field. The instrument was tested in laboratory and on a set of specimens selected from a damaged photographic archive. This experimentation, though preliminary, demonstrated the soundness of a technical approach based on HSI for large-scale spectroscopic characterization of photographic archival materials. The obtained results encourage the continuation of experimentation of HSI as an advanced tool for photography conservation. [ABSTRACT FROM AUTHOR]

Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400–1700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400–1100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used. [ABSTRACT FROM AUTHOR]

Robust soil organic matter (SOM) mapping is required by farms, but their generation requires a large number of samples to be chemically analyzed, which is cost prohibitive. Recently, research has shown that visible and near-infrared (vis-NIR) reflectance spectroscopy is a fast and accurate technique for estimating SOM in a cost-effective manner. However, few studies have focused on using vis-NIR spectroscopy as a covariate to improve the accuracy of spatial modeling. In this study, our objective was to compare the mapping accuracy from a spatial model using kriging methods with and without the covariate of vis-NIR spectroscopy. We split the 261 samples into a calibration set (104) for building the spectral predictive model, a test set for generating the vis-NIR augmented set from the prediction of the fitted spectral predictive model (131), and a validation set (26) for evaluating map accuracy. We used two datasets (235 samples) for Kriging: a laboratory-based dataset (Ld, observations from calibration and test datasets) and a laboratory-based dataset with vis-NIR augmented predictions (Au.p, observations from calibration and predictions from test dataset), a laboratory-based dataset with vis-NIR spectra as the covariance (Ld.co) and augmented dataset with predictions using vis-NIR with vis-NIR spectra for the covariance (Au.p.co). The first one to seven accumulated principal components of vis-NIR spectra were used as the covariates when we used the measurement of Ld.co and Au.p.co. The map accuracy was evaluated by the validation set for the four datasets using Kriging. The results indicated that adding vis-NIR spectra as covariates had great potential in improving the map accuracy using kriging, and much higher accuracies were observed for Ld.p.co (RMSE of 5.51 g kg−1) and Au.p.co (RMSE of 5.66 g kg−1) than without using vis-NIR spectra as covariates for Ld (RMSE of 7.12 g kg−1) and Au.p (RMSE of 7.69 g kg−1). With a similar model performance to Ld.p.co, Au.p.co can reduce the cost of laboratory analysis for 60% of soil samples, demonstrating its advantage in cost-efficiency for spatial modeling of soil information. Therefore, we conclude that vis-NIR spectra can be used as a cost-effective technique to obtain augmented data to improve fine-resolution spatial mapping of soil information. [ABSTRACT FROM AUTHOR]

Grape quality and ripeness play a crucial role in producing exceptional wines with high-value characteristics, which requires an effective assessment of grape ripeness. The primary purpose of this research is to explore the possible application of visible–near-infrared spectral (Vis-NIR) technology for classifying the maturity stages of wine grapes based on quality indicators. The reflection spectra of Cabernet Sauvignon grapes were recorded using a spectrometer in the spectral range of 400 nm to 1029 nm. After measuring the soluble solids content (SSC), total acids (TA), total phenols (TP), and tannins (TN), the grape samples were categorized into five maturity stages using a spectral clustering method. A traditional supervised classification method, a support vector machine (SVM), and two deep learning techniques, namely stacked autoencoders (SAE) and one-dimensional convolutional neural networks (1D-CNN), were employed to construct a discriminant model and investigate the association linking grape maturity stages and the spectral responses. The spectral data went through three commonly used preprocessing methods, and feature wavelengths were extracted using a competitive adaptive reweighting algorithm (CARS). The spectral data model preprocessed via multiplicative scattering correction (MSC) outperformed the other two preprocessing methods. After preprocessing, a comparison was made between the discriminant models established with full and effective spectral data. It was observed that the SAE model, utilizing the feature spectrum, demonstrated superior overall performance. The classification accuracies of the calibration and prediction sets were 100% and 94%, respectively. This study showcased the dependability of combining Vis-NIR spectroscopy with deep learning methods for rapidly and accurately distinguishing the ripeness stage of grapes. It has significant implications for future applications in wine production and the development of optoelectronic instruments tailored to the specific needs of the winemaking industry.

A Tungsten-Halogen (TH) lamp is the most popular light source in NIR spectroscopy and hyperspectral imaging, which requires a warm-up to reach very high temperatures of up to 250 °C and take a long time for radiation stabilization. Consequently, it has a large enough volume to enable heat dissipation to prevent the thermal runaway of the electric circuit and turn out its power efficiency very low. These are major barriers for miniaturizing spectral systems and hyperspectral imaging devices. However, TH lamps can be replaced by pc-NIR LEDs in order to avoid high temperature and large volume. We compared the spectral emission of the available commercial pc-NIR LEDs under the same condition. As a replacement for the TH lamp, the VIS + NIR LED module was developed to combine a warm-white LED and pc-NIR LEDs. In order to feature out the availability of the VIS + NIR LED module against the TH lamp, they were used as the light source for evaluating the Soluble Solid Content (SSC) of an apple through VIS-NIR spectroscopy. The results show a remarkable feasibility in the performance of the partial least square (PLS) model using the VIS + NIR LED module; during PLS calibration, the correlation coefficient (R) values are 0.664 and 0.701, and the Mean Square Error (MSE) values are 0.681 and 0.602 for the TH lamp and VIS + NIR LED module, respectively. In VIS-NIR spectroscopy, this study indicates that the TH lamp could be replaceable with a warm-white LED and pc-NIR LEDs. [ABSTRACT FROM AUTHOR]

This study aimed to determine the integrated freshness index (IFI) of eggs using Vis-NIR spectroscopy and optimized support vector regression, which gave the first insight into the freshness quality of eggs from the biochemical essence of quality changes. In this work, Vis-NIR transmission spectra of brown-shell and pink-shell egg samples were analyzed between 500 nm and 900 nm. Standard normal variables (SNV) were used to normalize the spectral data, and the Shuffled Frog Leaping Algorithm (SFLA) and Competitive Adaptive Reweighted Sampling (CARS) were used to choose the optimal wavelengths. The quantitative analysis model of IFI was developed using a support vector regression (SVR) that was optimized using Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). After conducting a comparative analysis, it was determined that the GA-SVR model based on 63 wavelengths screened by the SFLA best predicted IFI with a training set coefficient of determination (Rc2) of 0.900, root means square error (RMSEC) of 0.005, a prediction set coefficient of determination (Rp2) of 0.816, root mean square error (RMSEP) of 0.012 and relative analysis error (RPD) of 2.077. The results demonstrate that the model can be used to simultaneously perform nondestructive detection of two distinct egg IFI variants, suggesting broader applicability and enhanced model reliability. [ABSTRACT FROM AUTHOR]

A porcine model was used to investigate the feasibility of using VIS-NIR spectroscopy to differentiate between degrees of ischemia–reperfusion injury in the small intestine. Ten pigs were used in this study and four segments were created in the small intestine of each pig: (1) control, (2) full arterial and venous mesenteric occlusion for 8 h, (3) arterial and venous mesenteric occlusion for 2 h followed by reperfusion for 6 h, and (4) arterial and venous mesenteric occlusion for 4 h followed by reperfusion for 4 h. Two models were built using partial least square discriminant analysis. The first model was able to differentiate between the control, ischemic, and reperfused intestinal segments with an average accuracy of 99.2% with 10-fold cross-validation, and the second model was able to discriminate between the viable versus non-viable intestinal segments with an average accuracy of 96.0% using 10-fold cross-validation. Moreover, histopathology was used to investigate the borderline between viable and non-viable intestinal segments. The VIS-NIR spectroscopy method together with a PLS-DA model showed promising results and appears to be well-suited as a potentially real-time intraoperative method for assessing intestinal ischemia–reperfusion injury, due to its easy-to-use and non-invasive nature. [ABSTRACT FROM AUTHOR]

Strawberries are a high-value fruit with distinctive characteristics, including having a bright red color and juicy texture. The importance of their texture qualities requires the development of non-destructive analytical methods. This study focuses on the use of silicon-based visible–near infrared (Vis-NIR) spectroscopy to predict the texture qualities of strawberries. The highest correlation values (r) of prediction of firmness were 0.81 (transmittance) and 0.78 (reflectance), while those of brittleness were 0.78 (transmittance) and 0.77 (reflectance). It was found that transmittance mode can predict the texture qualities of strawberries better than reflectance mode. Savitzky-Golay filtering improved the prediction accuracy for most characteristics. The results showed that Vis-NIR spectroscopy, combined with partial least square regression analysis and Savitzky-Golay smoothing, can predict the texture qualities of strawberries at moderate to high accuracy. Further studies are needed to reduce the effects of individual sample sizes and improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Chemical oxygen demand (COD) is one of the indicators used to monitor the level of pollution in surface water. To recycle agricultural water resources, it is crucial to monitor, in a timely manner, whether COD in surface water exceeds the agricultural water control standard. A diagnostic model of surface water pollution was developed using visible near-infrared spectroscopy (Vis-NIR) combined with partial least squares discriminant analysis (PLS–DA). A total of 127 surface water samples were collected from Guangzhou, Guangdong, China. The COD content was measured using the potassium dichromate method. The spectra of the surface water samples were recorded using a Vis-NIR spectrometer, and the spectral data were pre-processed using four different methods. To improve the accuracy and simplicity of the model, the synthetic minority oversampling technique (SMOTE) and the competitive adaptive reweighted sampling (CARS) algorithm were used to enhance model performance. The best PLS–DA model achieved an accuracy of 88%, and the SMOTE–PLS–DA model had an accuracy of 94%. The SMOTE algorithm could improve the accuracy of the model despite the sampling imbalance. The CARS–SMOTE–PLS–DA model achieved 97% accuracy, and the CARS band selection technique improved the simplicity and accuracy of the discrimination model. The CARS–SMOTE–PLS–DA model improved the discrimination accuracy by 9% over that of the PLS–DA model. This method can not only save human and material resources but is also a new way for real-time online discrimination of COD in surface water. [ABSTRACT FROM AUTHOR]

Measuring the soil salinity using visible and near-infrared (vis–NIR) reflectance spectra is considered a fast and cost-effective method. For monitoring purposes, estimating soils with low salinity measured as electrical conductivity (EC) using vis–NIR spectra is still understudied. In this research, 399 legacy soil samples from six regions of Southern Xinjiang, China with low EC values were used. Reflectance spectra were measured in the laboratory on dried and ground soil samples using a portable vis–NIR spectrometer. By using 10-fold cross-validation, three algorithms–partial least-squares regression (PLSR), random forest (RF), and Cubist–were employed to develop statistical models of EC. The model performance evaluation was obtained by the relative importance of variants. In terms of accuracy assessment of soil EC prediction, the results demonstrated that the Cubist model performed better ( R 2 = 0.67, RMSE = 0.16 mS/cm, RPIQ = 2.28) than both PLSR and RF. Despite similar variants for modelling, the RF model performed somewhat better than that of the PLSR. Additionally, the 610 nm and 790 nm wavelengths only demonstrated significant promise for predicting low soil EC values when used in the Cubist mode. The current research recommends the use of Cubist to estimate the low soil salinity using the vis–NIR reflectance spectra. [ABSTRACT FROM AUTHOR]

Insect-affected pests, as an important indicator in inspection and quarantine, must be inspected in the imports and exports of fruits like “Yali” pears (a kind of duck head-shaped pear). Therefore, the insect-affected pests in Yali pears should be previously detected in an online, real-time, and accurate manner during the commercial sorting process, thus improving the import and export trade competitiveness of Yali pears. This paper intends to establish a model of online and real-time discrimination for recessive insect-affected pests in Yali pears during commercial sorting. The visible-near-infrared (Vis-NIR) spectra of Yali samples were pretreated to reduce noise interference and improve the spectral signal-to-noise ratio (SNR). The Competitive Adaptive Reweighted Sampling (CARS) method was adopted for the selection of feature modeling variables, while Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Block Attention Module-Convolutional Neural Networks (CBAM-CNN) were used to establish online discriminant models. T-distributed Stochastic Neighbor Embedding (T-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) were used for the clustering and attention distribution display of spectral features of deep learning models. The results show that the online discriminant model obtained by SGS pretreatment combined with the CBAM-CNN deep learning method exhibits the best performance, with 96.88 and 92.71% accuracy on the calibration set and validation set, respectively. The prediction time of a single pear is 0.032 s, which meets the online sorting requirements.

Agricultural soils serve as crucial storage sites for soil organic carbon (SOC). Their appropriate management is pivotal for mitigating climate change. Continuous monitoring is imperative to evaluate spatial and temporal changes in SOC within agricultural fields. In-field datasets of Vis-NIR soil spectra were collected on a long-term experimental site using an on-the-go spectrophotometer. Data processing for continuous SOC prediction involves a two-step modeling approach. In Step 1, a partial least square (PLSR) regression model is trained to establish a relationship between the SOC content and spectral information, including spectral preprocessing. In Step 2, the predicted SOC content obtained from the PLSR models is interpolated using ordinary kriging. Among the tested spectral preprocessing techniques and semivariogram models, Savitzky–Golay and the Gap-Segment derivative preprocessing along with a Gaussian semivariogram model, yielded the best performance resulting in a root mean square error of 1.24 and 1.26 g kg−1. A striping effect due to the transect-based data collection was addressed by testing the effectiveness of extending the spatial separation distance, employing data aggregation, and defining the distribution based on treatment plots using block kriging. Overall, the results highlight the high potential of on-the-go spectral Vis-NIR data for field-scale spatial-temporal monitoring of SOC.

Fruit quality assessment plays a crucial role in determining their market value, consumer acceptance, and post-harvest management. In recent years, spectroscopic techniques have gained significant attention as non-destructive methods for evaluating fruit quality. In this study, we propose a novel deep-learning network, called GCNN-LSTM-AT, for the prediction of five important parameters of tangerines using visible and near-infrared spectroscopy (Vis–NIR). The quality attributes include soluble solid content (SSC), total acidity (TA), acid–sugar ratio (A/S), firmness, and Vitamin C (VC). The proposed model combines the strengths of graph convolutional network (GCN), convolutional neural networks (CNNs), and long short-term memory (LSTM) to capture both spatial and sequential dependencies in the spectra data, and incorporates an attention mechanism to enhance the discriminative ability of the model. To investigate the effectiveness and stability of the model, comparisons with three traditional machine-learning algorithms—moving window partial least squares (MWPLS), random forest (RF), and support vector regression (SVR)—and two deep neural networks—DeepSpectra2D and CNN-AT—are provided. The results have shown that the GCNN-LSTM-AT network outperforms other algorithms and models, achieving accurate predictions for SSC (R2: 0.9885, RMSECV: 0.1430 ∘Brix), TA (R2: 0.8075, RMSECV: 0.0868%), A/S (R2: 0.9014, RMSECV: 1.9984), firmness (R2: 0.9472, RMSECV: 0.0294 kg), and VC (R2: 0.7386, RMSECV: 29.4104 mg/100 g) of tangerines.