VNIR spectroscopy for assessment of post-fire impacts on soil properties using linear and non-linear calibration methods

Current economic development in tropical regions (especially in India, China, and Brazil) is putting tremendous pressure on tropical forest cover. Some of the dominant and economically important species are planted at large scale in these countries. Teak and bamboo are two important species of tropical regions because of their commercial and conservation values. Accurate estimates of foliar chemistry can help in evaluating the health status of vegetation in these regions. An attempt has been made to derive canopy level estimation of chlorophyll and leaf area index (LAI) for these species utilizing Hyperion data. Partial least square (PLS) regression analysis was carried out to identify the correlation between measured parameters (chlorophyll and LAI) and Hyperion reflectance spectra. PLS regression identified 600–750 nm as a sensitive spectral region for chlorophyll and 1000–1507 nm for LAI. The PLS regression model tested in this study worked well for the estimation of chlorophyll (R 2 = 0.90, root mean square error (RMSE) = 0.182 for teak and R 2 = 0.84, RMSE = 0.113 for bamboo) and for the estimation of LAI (R 2 = 0.87, RMSE = 0.425). The lower predictive error obtained indicates the robustness of the data set and also of the applicability of the PLS regression analysis. Wavelengths recognized by the PLS regression model were utilized for the development of vegetation indices for estimating chlorophyll and LAI. Predictive performances of the developed simple ratios (SRs) were evaluated using the cross-validation method. SR 743/692 gave the best results for the prediction of chlorophyll with the leave-one-out cross-validation (LOO-CV) method (R 2 = 0.73, RMSE = 0.28 for teak and R 2 = 0.71, RMSE = 0.15 for bamboo). The normalized difference ratio (ND 1457/1084) gave the best results for the prediction of LAI with LOO-CV (R 2 = 0.66, RMSE = 0.57). Ratios developed here can be tested for teak and bamboo cover spread in tropical regions with similar environmental conditions.

Total viable count (TVC) is often used as an important indicator for chicken freshness evaluation. In this study, 112 fresh chicken flesh samples were acquired after slaughtered and their hyperspectral images were collected in the LW-NIR (900-1700 nm) range. The full LW-NIR spectra (486 wavebands) within the images were extracted and applied to related to reference TVC values measured in different storage period, using partial least squares regression (PLSR) algorithm, resulting in high correlation coefficients (R) and low root mean square errors (RMSE), for either raw spectra or pretreatment spectra. By using regression coefficients (RC) method, 20, 18, 17 and 20 optimal wavebands were respectively selected from raw spectra, baseline correction (BC) spectra, Savitzky-Golay convolution smoothing (SGCS) spectra and standard normal variate (SNV) spectra and applied for the optimization of original full waveband PLSR model. By comparison, RC-PLSR model based on the SGCS spectra showed a better performance in TVC prediction with RC of 0.98 and RMSEC of 0.35 log10 CFU/g in calibration set, and RP of 0.98 and RMSEP of 0.44 log10 CFU/g in prediction set. At last, by transferring the best RC-PLSR model, the dynamic TVC change during the storage was visualized by color maps to indicate the TVC spoilage degree. The overall study revealed that LW-NIR hyperspectral imaging combined with PLSR could be used to predict the freshness of chicken flesh. Keywords: hyperspectral imaging, chicken, TVC, partial least square regression (PLSR) DOI: 10.25165/j.ijabe.20191203.4444 Citation: H Wang, H J He, H J Ma, F S Chen, Z L Kang, M M Zhu, et al. LW-NIR hyperspectral imaging for rapid prediction of TVC in chicken flesh. Int J Agric & Biol Eng, 2019; 12(3): 180–186.

Digestibility profiles serve as vital indicators of feedstuffs' quality, determining how effectively livestock utilise feed nutrients for growth and production. This study develops a rapid and non-destructive model for the digestibility quality of unconventional feedstuffs, which integrates feed NIR spectra with chemometric techniques. A total of 30 samples (citronella residues) obtained from the fermentation process were used, and their NIR spectra (1000~2500 nm) were acquired using a BUCHI NIRFlex N-500 spectrometer. Reference digestibility profiles, including dry matter (IVDMD), organic matter (OM), and pH of rumen fluid, were measured using the two-stage digestion method. Partial least squares regression (PLSR), ridge regression (Ridge), adaptive boosting (AdaBoost), and support vector machine regression (SVMR) models were then constructed to assess digestibility quality. Model performance was assessed using the coefficient of determination (R 2 ), root-mean-square error (RMSE), and residual predictive deviation (RPD). The results show that the performance of the digestibility profile prediction model meets the accepted standards for NIRS calibration. Notably, the SVMR model exhibits exceptional stability and performance, achieving an R 2 value of ≥ 0.97 and a low RMSE, outperforming the AdaBoost, Ridge, and PLSR models. These findings confirm that the calibration model has significant potential for further independent validation to serve as a rapid-quality assessment tool in analyzing the digestibility profiles of feedstuffs.

<p>Soil properties could be assessed with reflectance spectroscopy (soil spectroscopy, SS) in vis-NIR region (400-2500 nm) through absorption features found in soil spectra. A high spectral resolution (up to 1 nm) drives to high dimensional and multicollinear data. This issue is usually addressed prior to modelling with feature extraction methods such as Principal Component Analysis, or embedded methods such as Partial Least Squares Regression (PLSR). Feature Selection (FS) wrapper methods are promising dimensionality reduction approaches barely used in SS. The objective of this study was two-fold: i) evaluate the performance of FS wrapper methods built from Random Forest (RF) algorithm to predict soil organic matter (SOM), clay and carbonates using laboratory spectroscopy, ii) test the performance of FS methods for dimensionality reduction in SS. The reflectance of 100 soil samples from Sierra de las Nieves National Park (Spain), was measured under laboratory conditions using an ASD FieldSpec Pro JR. A spectral preprocessing method, Continumm Removal (CR), was applied to raw spectra. The RF wrapper considered two different feature searching approaches: Sequential Forward Selection (SFS) and Sequential Flotant Forward Selection (SFFS). The performance of RF with FS (RF-FS) was compared to that of Partial Least Squares Regression (PLSR) and RF (without FS). Models were evaluated with R-squared, root mean squared error (RMSE) and ratio of prediction to deviation (RPD).</p><p>RF-FS models outperformed PLSR and RF models for the three SAP. RF-FS best models had a RPD of 2.19 for SOM, 1.64 for carbonates and 1.52 for clay, whereas PLSR models had RPD values of 1.59, 1.22 and 1.3, and RF 1.38, 1.23 and 1.23 for SOM, carbonates, and clay, respectively. Therefore, FS was useful in obtaining models with improved accuracy by reducing redundant features and avoiding multicollinearity (Hughes effect). The application of FS wrapper methods reduced the number of features in the RF-FS models to less than 1% of the starting features. Features were selected across all spectra from SOM and clay, and around 900, 1900 and 2350 nm for carbonates. This research, thus, shows an alternative to different feature extraction approaches for modelling soil properties based on FS methods and machine learning.</p>

Inversion of soil heavy metals in metal tailings area based on different spectral transformation and modeling methods

The objective of this study was to compare the predictions of the protein contents and hardness values by partial least squares regression (PLSR) and principal components regression (PCR) models for bulk samples of Canadian wheat, which were obtained from different locations and crop years. Wheat samples of Canada Western Red Spring (CWRS), Canada Western Hard White Spring (CWHWS), Canada Western Soft White Spring (CWSWS), and Canada Prairie Spring Red (CPSR) classes were obtained from nearby agricultural farms in the main wheat growing locations in the Provinces of Alberta, Saskatchewan, and Manitoba from 2007, 2008, and 2009 crop years. Wheat samples were conditioned to moisture levels of 13, 16, and 19 % (wet basis) and pooled together for developing the regression models. A database of the near-infrared (NIR) hyperspectral image cubes of bulk samples of wheat classes was created in the wavelength region of 960–1,700 nm with 10 nm intervals. Reference protein contents and hardness values were determined using the Dumatherm method and single kernel characterization system (SKCS), respectively. A tenfold cross-validation was used for the ten-factor partial least squares regression (PLSR) and principal components regression (PCR) models for prediction purposes. Prediction performances of regression models were assessed by calculating the estimated mean square errors of prediction (MSEP), standard error of cross-validation (SECV), and correlation coefficient (r). Using the full data set in the protein prediction study, the ten-component PLSR model gave 1.76, 1.33, and 0.68 for the estimated MSEP, SECV, and r, respectively, which were better than the results for the ten-component PCR model (2.02, 1.42, and 0.62, respectively). For the hardness prediction, the estimated MSEP, SECV, and r values were 147.7, 12.15, and 0.82, respectively, for the ten-component PLSR model using the full data set. The PLSR models prediction performances outperformed the PCR models for predicting protein contents and hardness of wheat.

This study aimed to compare stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector machine regression (SVMR) for estimating soil total nitrogen (TN) contents with laboratory visible/near-infrared reflectance (Vis/NIR) of selected coarse and heterogeneous soils. Moreover, the effects of the first (1st) vs. second (2nd) derivative of spectral reflectance and the importance wavelengths were explored. The TN contents and the Vis/NIR were measured in the laboratory. Several methods were employed for Vis/NIR data pre-processing. The SMLR, PLSR and SVMR models were calibrated and validated using independent datasets. Results showed that the SVMR and the PLSR models had similar performances, and better performances than the SMLR. The spectral bands near 1450, 1850, 2250, 2330 and 2430 nm in the PLSR model were important wavelengths. In addition, the 1st derivative was more appropriate than the 2nd derivative for spectral data pre-processing. PLSR was the most suitable method for estimating TN contents in this study. SVMR may be a promising technique, and its potential needs to be further explored. Moreover, the future studies using outdoor and airborne/satellite hyperspectral data for estimating TN content are necessary for testing the findings.

Carbohydrate levels are important regulators of the growth and yield of tree crops. Current methods for measuring foliar carbohydrate concentrations are time consuming and laborious, but rapid imaging technologies have emerged with the potential to improve the effectiveness of tree nutrient management. Carbohydrate concentrations were predicted using hyperspectral imaging (400–1000 nm) of leaves of the evergreen tree crops, avocado, and macadamia. Models were developed using partial least squares regression (PLSR) and artificial neural network (ANN) algorithms to predict carbohydrate concentrations. PLSR models had R2 values of 0.51, 0.82, 0.86, and 0.85, and ANN models had R2 values of 0.83, 0.83, 0.78, and 0.86, in predicting starch, sucrose, glucose, and fructose concentrations, respectively, in avocado leaves. PLSR models had R2 values of 0.60, 0.64, 0.91, and 0.95, and ANN models had R2 values of 0.67, 0.82, 0.98, and 0.98, in predicting the same concentrations, respectively, in macadamia leaves. ANN only outperformed PLSR when predicting starch concentrations in avocado leaves and sucrose concentrations in macadamia leaves. Performance differences were possibly associated with nonlinear relationships between carbohydrate concentrations and reflectance values. This study demonstrates that PLSR and ANN models perform well in predicting carbohydrate concentrations in evergreen tree-crop leaves.

This study aimed to test the ability of a nondestructive and fast method of Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy quantifying acetamiprid levels in apple and to compare with a universal chemical method. Non-pesticide applied apple were entirely homogenized with 0.00, 0.156, 0.31, 0.625, 1.25, 2.50, 5.00, 10.00, 20.00, 40.00, 80.00 and 160.00 ppm acetamiprid. Universal chemical method produced acceptable performance: accuracy of 90% and precision of 22% in acetamiprid quantification. In contrast, partial least square regression (PLSR) model 1 using raw spectra data and PLSR model 2 using its second derivate have higher accuracy of 108% and 101% and precision of 9.33% and 3.67%, respectively, and the model 2 being more powerful. Moreover, ATR-FTIR combined PLSR models produced lower root mean square error (RMSE) of quantification (0.41–0.87) than that of universal method (3.46). In conclusion, acetamiprid levels up to 80-folds of legally permitted level (2 ppm) in fresh apple can accurately be detectable by ATR-FTIR spectroscopic method.

Fusion of Gamma-rays and portable X-ray fluorescence spectral data to measure extractable potassium in soils

Motivated by recent research on two-phase flow void fraction measurement, this paper presents a study on void fraction measurements of a lubricant air-oil two-phase flow using a near infrared (NIR) optical-fiber spectrometer associated with partial least squares (PLS) regression models. To overcome the measurement inaccuracy due to interface scattering, this study used PLS regression models to analyse the spectrum of air-oil two-phase flow. First, an NIR optical-fiber spectrometer experimental system was developed. Second, a flow regime analysis was conducted for the bubble flow transition inside the experimental system. Finally, the PLS regression model validations, the void fraction measurement accuracies, and the uncertainties of the experimental system were evaluated and discussed, and the different PLS regression models were compared in detail. The results indicate that the NIR optical-fiber spectrometer combined with the PLS regression model can achieve void fraction measurements of lubricants in air-oil two-phase flow, with a maximum squared correlation coefficient (R 2) of 0.981, a minimum Type A uncertainty of 0.039% and a minimum expanded uncertainty of 0.077%.

Monitoring of crops during their growth period is essential to increase the profitability of production and reduce costs. Unmanned aerial vehicles (UAVs) have been introduced as an appropriate tool in precision farming, due to their affordability and high-spatial resolution data. The potential of plant height and vegetation indices derived from UAV-based RGB imagery at 12-leaf growth stage (V12) is investigated for prediction of corn forage yield. Using the structure from motion techniques, the crop height model (CHM) was created by subtracting crop surface model from digital elevation model. Next, by evaluating the CHM using ground measurements of plant height, it was shown that UAV imagery provides high capability in modeling the plant height ( R 2 = 0.85). Pearson’s correlation coefficient ( R ) between variables showed that the plant height had the highest correlation with forage yield ( R = 0.84). Furthermore, among the vegetation indices, visible atmospherically resistant index with R = 0.77, normalized difference index with R = 0.75, and excess red with R = 0.74 were better than other indices. Finally, the partial least squares regression (PLSR) model was constructed using plant height and vegetation indices to predict forage yield. The beta regression coefficients of the PLSR model were used to identify the most relevant predictors, and the final PLSR model was rebuilt with relevant predictors. The PLSR analysis confirmed that the plant height was the most important predictor, although integrating the vegetation indices in the PLSR model has slightly improved the prediction model ( R 2 = 0.74, RMSE = 0.316 kg / m2, and relative RMSE = 12.39 % ), compared to the model that used only plant height ( R 2 = 0.702, RMSE = 0.339 kg / m2, and relative RMSE = 13.29 % ). Overall, these results indicate that the UAV-based images are efficient and cost-effective data to predict corn forage yield as well as plant height modeling.

Estimating the available potassium (AK) in soil can help improve field management and crop production. Fourier-transform infrared (FTIR) spectroscopy is one of the most promising techniques for the fast and real-time analysis of soil AK content. However, the successful estimation of soil AK content by FTIR depends on the proper selection of appropriate spectral dimensionality reduction techniques. To magnify the subtle spectral signals concerning AK content and improve the understanding of the characteristic FTIR wavelengths of AK content, a total of 145 soil samples were collected in an agricultural site located in the southwest part of Sichuan, China, and three typical spectral dimensionality reduction methods—the successive projections algorithm (SPA), simulated annealing algorithm (SA) and competitive adaptive reweighted sampling (CARS)—were adopted to select the appropriate spectral variable. Then, partial least squares regression (PLSR) was utilized to establish AK inversion models by incorporating the optimal set of spectral variables extracted by different dimensionality reduction algorithms. The accuracy of each inversion model was tested based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute value error (MAE), and the contribution of the inversion model variables was explored. The results show that: (1) The application of spectral dimensionality reduction is a useful technique for isolating specific components of multicomponent spectra, and as such is a powerful tool to improve and expand the predicted potential of the spectroscopy of soil AK content. Compared with the SA and CARS algorithms, the SPA was more suitable for soil AK content inversion. (2) The inversion model results showed that the characteristic wavelengths were mainly around 777 nm, 1315 nm, 1375 nm, 1635 nm, 1730 nm and 3568–3990 nm. (3) Comparing the performances of different inversion models, the SPA–PLSR model (R2= 0.49, RMSE = 22.80, MAE = 16.82) was superior to the SA–PLSR and CARS–PLSR models, which has certain guiding significance for the rapid detection of soil AK content.

Accurate forest biomass estimation serves as the foundation of forest management and holds critical significance for a comprehensive understanding of forest carbon storage and balance. This study aimed to integrate Landsat 8 OLI and Sentinel-1A SAR satellite image data and selected a portion of the Shanxia Experimental Forest in Jiangxi Province as the study area to establish a biomass estimation model by screening influencing factors. Firstly, we extracted spectral information, vegetation indices, principal component features, and texture features within 3 × 3-pixel neighborhoods from Landsat 8 OLI. Moreover, we incorporated Sentinel-1’s VV (vertical transmit–vertical receive) and VH (vertical transmit–horizontal receive) polarizations. We proposed an ensemble AGB (aboveground biomass) model based on a neural network. In addition to the neural network model, namely the tent mapping atom search optimized BP neural network (Tent_ASO_BP) model, partial least squares regression (PLSR), support vector machine (SVR), and random forest (RF) regression prediction techniques were also employed to establish the relationship between multisource remote sensing data and forest biomass. Optical variables (Landsat 8 OLI), SAR variables (Sentinel-1A), and their combinations were input into the four prediction models. The results indicate that Tent_ ASO_ BP model can better estimate forest biomass. Compared to pure optical or single microwave data, the Tent_ASO_BP model with the optimal combination of optical and microwave input features achieved the highest accuracy. Its R2 was 0.74, root mean square error (RMSE) was 11.54 Mg/ha, and mean absolute error (MAE) was 9.06 Mg/ha. Following this, the RF model (R2 = 0.54, RMSE = 21.33 Mg/ha, MAE = 17.35 Mg/ha), SVR (R2 = 0.52, RMSE = 17.66 Mg/ha, MAE = 15.11 Mg/ha), and PLSR (R2 = 0.50, RMSE = 16.52 Mg/ha, MAE = 12.15 Mg/ha) models were employed. In conclusion, the BP neural network model improved by tent mapping atom search optimization algorithm significantly enhanced the accuracy of AGB estimation in biomass studies. This will provide a new avenue for large-scale forest resource surveys.

Cotton aphid is the most common and harmful insect in the process of cotton growth and development, and it is the most serious pest that restricts the high yield and high quality of cotton in China and even in the world. The field-scale experiments were conducted for two years to estimate the population of cotton aphids model on cotton leaves using remote sensing. Hyperspectral data from single leaves and aphid populations were obtained at different growth stages of various cotton varieties. Preprocessing techniques including savitzky-golay (SG), first derivative (FD), second derivative (SD), logarithm transformation (LG), and reciprocal transformation (RG) were applied to the original spectral data. The successive projections algorithm (SPA) combined with the Pearson correlation coefficient (Pearson) method were used to construct three types of hyperspectral monitoring models: linear regression (LR), Extreme gradient boosting (XGBoost), and partial least squares regression (PLSR). Results indicated that LG significantly improved model accuracy, while SPA effectively reduced the number of bands required for analysis. Among the three models constructed using selected feature bands, the XGBoost model outperforms LR and PLSR models in terms of prediction accuracy. LG-Person-XGBoost identified ten feature bands with a coefficient of determination (R2), root mean square error (RMSE), and relative percent deviation (RPD) values reaching 0.76, 65.74 heads/leaf, and 0.91 respectively for the modeling set; whereas predicted R2, RMSE, and RPD values were found to be 0.36, 57.62 heads/leaf, and 0.99 respectively. LG-SPA-XGBoost achieved superior prediction accuracy by selecting eight feature bands with R2, RMSE, and RPD values reaching 0.87, 46.7 heads/leaf, and 1.43 respectively for the modeling set; whereas predicted R2, RMSE, and RPD values were found to be 0.77, 42.66 heads/leaf, and 1.27 respectively. This indicates that the hyperspectral remote sensing model can be used to estimate the population of cotton aphids on cotton leaves based on the selection of feature bands providing are reference for the nondestructive monitoring of cotton aphids in cotton fields and offering a significant supplement to the traditional methods of crop pest quantity monitoring.