Partial Least Squares Regression Algorithm Research Articles

Hyperspectral imaging combined with machine learning for high‐throughput phenotyping in winter wheat

AbstractUncrewed aerial vehicles (UAVs) are a highly successful and efficient method for phenotyping in plant breeding programs. This study explored using UAVs equipped with hyperspectral sensors to expedite breeders' decision‐making in selecting winter wheat (Triticum aestivum L.) genotypes with improved growth, biomass, leaf area, and canopy cover (CC). The hyperspectral image processing pipeline utilized for image analysis was described. The study was conducted with 2145 genotypes of winter wheat, and UAV‐based hyperspectral measurements were used to predict the phenotype. Reflectance measurements were taken at narrow wavelength intervals, spanning 400–2500 nm. For ground truthing, samples were collected from different parts of the field. These samples were used to evaluate various plant attributes, including CC, leaf area index, plant height, and dry biomass. The hyperspectral data were employed for computation of multiple vegetation indices (VIs), and to improve the prediction of plant traits, we employed partial least squares regression (PLSR) and random forest (RF) regression techniques on both the complete set of hyperspectral variables and the top 10 derived VIs. Our results show that using complete hyperspectral variables results in superior r, R2, and lower root mean square error for both models. We conclude that relying solely on linear regression models with VIs may not always result in accurate predictions of plant traits in winter wheat. However, combining these indices with the RF and PLSR algorithm significantly enhances the prediction accuracy. However, the performance of both PLSR and RF models shows minimal disparity, with only slight differences observed. This highlights the importance of utilizing appropriate machine learning algorithms for improved prediction of plant traits.

Hyperspectral imaging combined with machine learning for high‐throughput phenotyping in winter wheat

AbstractUncrewed aerial vehicles (UAVs) are a highly successful and efficient method for phenotyping in plant breeding programs. This study explored using UAVs equipped with hyperspectral sensors to expedite breeders' decision‐making in selecting winter wheat (Triticum aestivum L.) genotypes with improved growth, biomass, leaf area, and canopy cover (CC). The hyperspectral image processing pipeline utilized for image analysis was described. The study was conducted with 2145 genotypes of winter wheat, and UAV‐based hyperspectral measurements were used to predict the phenotype. Reflectance measurements were taken at narrow wavelength intervals, spanning 400–2500 nm. For ground truthing, samples were collected from different parts of the field. These samples were used to evaluate various plant attributes, including CC, leaf area index, plant height, and dry biomass. The hyperspectral data were employed for computation of multiple vegetation indices (VIs), and to improve the prediction of plant traits, we employed partial least squares regression (PLSR) and random forest (RF) regression techniques on both the complete set of hyperspectral variables and the top 10 derived VIs. Our results show that using complete hyperspectral variables results in superior r, R2, and lower root mean square error for both models. We conclude that relying solely on linear regression models with VIs may not always result in accurate predictions of plant traits in winter wheat. However, combining these indices with the RF and PLSR algorithm significantly enhances the prediction accuracy. However, the performance of both PLSR and RF models shows minimal disparity, with only slight differences observed. This highlights the importance of utilizing appropriate machine learning algorithms for improved prediction of plant traits.

Efficient Quantification of Soluble and Insoluble Oxalates in Clay Mineral Mixtures

ABSTRACT Quantification of oxalate salts in soil clay minerals is necessary to study oxalate biogeochemistry, but existing analytical techniques are expensive and time-consuming. We aim to develop an efficient attenuated total reflectance mid-infrared (MIR) spectroscopic technique to quantify oxalate salts in a clay mineral matrix. We calibrated MIR models for analysis of oxalate anion concentrations in standard solutions (0–0.01 M) by using a partial least-squares regression algorithm. MIR models were also developed for analysis of sodium oxalate (NaOx) and calcium oxalate (CaOx) content in clay mineral mixtures with composition like soils of a semi-arid region and with contrasting concentrations (0–1.0 g g−1 for both oxalate salts) to test sensitivity of analyses. Validation plots (true vs predicted values) showed excellent model fit (R2 ≥ 0.96) and accuracy (normalized root mean squared error of prediction ≤ 0.06) for CaOx, NaOx and oxalic acid components. Once predictive models are stored in analytical software, MIR spectroscopic analyses of samples are much more efficient than chemical techniques. Our MIR spectral-based models are suitable for direct quantification of oxalate salts in clay mineral mixtures for samples like those used for model calibration.

Strategies for the quality control of Chrysanthemi Flos: Rapid quantification and end-to-end fingerprint conversion based on FT-NIR spectroscopy.

Chrysanthemi Flos (CF) is widely used as a natural medicine or tea. Due to its diverse cultivation regions, CF exhibits varying quality. Therefore, the quality and swiftness in evaluation holds paramount significance for CF. The aim of the study was to construct a comprehensive evaluation strategy for assessing CF quality using HPLC, near-infrared (NIR) spectroscopy, and chemometrics, which included the rapid quantification analyses of chemical components and the Fourier transform (FT)-NIR to HPLC conversion of fingerprints. A total of 145 CF samples were utilised for data collection via NIR spectroscopy and HPLC. The partial least squares regression (PLSR) models were optimised using various spectral preprocessing and variable selection methods to predict the chemical composition content in CF. Both direct standardisation (DS) and PLSR algorithms were employed to establish the fingerprint conversion model from the FT-NIR spectrum to HPLC, and the model's performance was assessed through similarity and cluster analysis. The optimised PLSR quantitative models can effectively predict the content of eight chemical components in CF. Both DS and PLSR algorithms achieve the calibration conversion of CF fingerprints from FT-NIR to HPLC, and the predicted and measured HPLC fingerprints are highly similar. Notably, the best model relies on CF powder FT-NIR spectra and DS algorithm [root mean square error of prediction (RMSEP) = 2.7590, R2 = 0.8558]. A high average similarity (0.9184) prevails between predicted and measured fingerprints of test set samples, and the results of the clustering analysis exhibit a high level of consistency. This comprehensive strategy provides a novel and dependable approach for the rapid quality evaluation of CF.

High sensitivity and ultra-low concentration range photoacoustic spectroscopy based on trapezoid compound ellipsoid resonant photoacoustic cell and partial least square

A high sensitivity and ultra-low concentration range photoacoustic spectroscopy (PAS) gas detection system, which was based on a novel trapezoid compound ellipsoid resonant photoacoustic cell (TCER-PAC) and partial least square (PLS), was proposed to detect acetylene (C2H2) gas. In the concentration range of 0.5 ppm ∼ 10.0 ppm, the limit of detection (LOD) values of TCER-PAC-based PAS system without data processing was 66.4 ppb, which was lower than that of the traditional trapezoid compound cylindrical resonant photoacoustic cell (TCCR-PAC). The experimental results indicated that the TCER-PAC had higher sensitivity than of TCCR-PAC. Within the concentration range of 12.5 ppb ∼ 125.0 ppb, the LOD and limit of quantification (LOQ) of TCER-PAC-based PAS system combined with PLS regression algorithm were 1.1 ppb and 3.7 ppb, respectively. The results showed that higher detection sensitivity and lower LOD were obtained by PAS system with TCER-PAC and PLS than that of TCCR-PAC-based PAS system.

Polarization Influence on Algorithms of TFBG Sensors Data Analysis for Bending Application

In this article we deal with the influence of polarization on determining the bending radius in TFBG (tilted fiber Bragg grating) sensors. The spectrum of TFBG structures changes under the influence of many factors at the same time. In the case of bending radius measurements, additional factors may be the temperature and polarization state of the introduced light. So far, only the cladding mode envelope algorithm has been used to determine the bending radius. An interesting issue seems to be establishing the influence of cross-sensitivity of the spectrum on changes in polarization during bending measurements. In addition to the envelope algorithm, algorithms for spectral length and average deviation from the local mean were examined. As a result of the analysis of experimental data, it was found that the level of polarization’s influence on the result may be significant in determining the bending radius. Reducing the influence of polarization can be achieved by using algorithms providing wavelength parameters. Additionally, in order to reduce the influence of polarization, we proposed the use of the PLS (partial least squares regression) algorithm for the processed spectrum.

Predicting Models for Plant Metabolites Based on PLSR, AdaBoost, XGBoost, and LightGBM Algorithms Using Hyperspectral Imaging of Brassica juncea

The integration of hyperspectral imaging with machine learning algorithms has presented a promising strategy for the non-invasive and rapid detection of plant metabolites. For this study, we developed prediction models using partial least squares regression (PLSR) and boosting algo-rithms (such as AdaBoost, XGBoost, and LightGBM) for five metabolites in Brassica juncea leaves: total chlorophyll, phenolics, flavonoids, glucosinolates, and anthocyanins. To enhance the model performance, we employed several spectral data preprocessing methods and feature-selection al-gorithms. Our results showed that the boosting algorithms generally outperformed the PLSR models in terms of prediction accuracy. In particular, the LightGBM model for chlorophyll and the AdaBoost model for flavonoids improved the prediction performance, with R2p = 0.71–0.74, com-pared to the PLSR models (R2p = 0.53–0.58). The final models for the glucosinolates and anthocya-nins performed sufficiently for practical uses such as screening, with R2p = 0.82–0.85 and RPD = 2.4–2.6. Our findings indicate that the application of a single preprocessing method is more effective than utilizing multiple techniques. Additionally, the boosting algorithms with feature selection ex-hibited superior performance compared to the PLSR models in the majority of cases. These results highlight the potential of hyperspectral imaging and machine learning algorithms for the non-destructive and rapid detection of plant metabolites, which could have significant implications for the field of smart agriculture.

Sparse wavelengths data in mid-infrared spectroscopy: Modelling approaches and channel sampling.

Infrared instruments with smaller and cost-effective components such as bandpass filters, single channel detectors, and laser-based light sources are being developed to provide cheaper and faster analysis of biological samples. Such instruments often provide measurements in form of sparse data, which include a collection of single-frequency channels or a collection of channels covering very narrow spectral ranges, called here multi-frequency channels. To keep costs low, the number of channels needs to be kept at a minimum. However, modelling and preprocessing of sparse data needs enough channels to perform the task. The aim of this study therefore was to understand the effect of channels sampling on data modelling results and find optimal modelling algorithm for different type of sparse data. The sparse data was simulated using Fourier Transform Infrared spectra of milk and fungi. Regression models were established to predict fatty acid composition by partial least squares regression (PLSR), multiple linear regression (MLR) and random forest (RF) methods. We observe that PLSR algorithm is very well suited for sparse data such as multi-frequency channels: excellent calibration models were obtained with only three channels comprising three wavenumbers each. The results were comparable to results obtained with full spectra. MLR and RF in turn provided similarly good results using data with single-frequency channels requiring nine channels in total.

Estimation of key potentially toxic elements in arid agricultural soils using Vis-NIR spectroscopy with variable selection and PLSR algorithms

Potentially toxic elements (PTEs) pose a significant threat to soil and the environment. Therefore, the fast quantification of PTEs is crucial for better management of contaminated sites. Versatile technique such as Visible near-infrared spectroscopy (Vis–NIRS) (350–2,500 nm) has attracted tremendous attention for assessing PTEs and has achieved promising results combined with successful multivariate analysis. This research investigated the potential of Vis–NIRS combined with partial least squares regression (PLSR) and variable selection methods to assess key PTEs (Cd, Co, Cu, Cr, Pb, and Zn) in agricultural soils under arid conditions. The soil samples (80) were collected from a polluted area around Al-Moheet drainage, Minya Governorate–upper Egypt. The samples were scanned using an ASD FieldSpec-4 spectroradiometer. Simulated annealing (SA) and uninformative variable elimination (UVE) were used to select the effective wavelengths in predicting PTEs. PLSR was used to develop the spectral models using the full range (FR-PLS) and feature-selected spectra techniques SA (SA-PLS) and UVE (UVE-PLS). The results indicated that UVE-PLS models performed better than FR-PLS and SA-PLS models in predicting the key PTEs. The obtained coefficient of determination (R2) and the ratio of performance to deviation (RPD) were 0.74 and 2.48 (Cr), 0.72 and 2.03 (Pb), 0.62 and 1.86 (Cd), 0.59 and 1.78 (Cu), 0.52 and 1.68 (Co), and 0.46 and 1.41 (Zn), respectively. The results suggested that the UVE-PLS spectral model is promising for predicting Cr, Pb, and Cd, and can be improved for predicting Cu, Co, and Zn elements in agricultural soils.

Rapid quality assessment of Gentianae Macrophyllae Radix based on near infrared spectroscopy and capillary electrophoresis.

The aim of this study was to establish a rapid quality assessment method for Gentianae Macrophyllae Radix (RGM) using near infrared (NIR) spectra combined with chemometric analysis. The NIR spectra were acquired using an integrating sphere diffuse reflectance module, using air as the reference. Capillary electrophoresis (CE) analyses were performed on a model P/ACE MDQ Plus system. Partial least squares-discriminant analysis qualitative model was developed to distinguish different species of RGM samples, and the prediction accuracy for all samples was 91%. The CE response values at each retention time were predicted by building a partial least squares regression (PLSR) calibration model with the CE data set as the Y matrix and the NIR spectra data set as the X matrix. The converted CE fingerprints basically match the real ones, and the six main peaks can be accurately predicted. Transforming NIR spectra fingerprints into the form of CE fingerprints increases its interpretability and more intuitively demonstrates the components that cause diversity among samples of different species and origins. Loganic acid, gentiopicroside and roburic acid were considered as quality indicators of RGM and calibration models were built using PLSR algorithm. The developed models gave root mean square error of prediction of 0.2592% for loganic acid, 0.5341% for gentiopicroside and 0.0846% for roburic acid. The overall results demonstrate that the rapid quality assessment system can be used for quality control of RGM. This article is protected by copyright. All rights reserved.

Towards Optimal Variable Selection Methods for Soil Property Prediction Using a Regional Soil Vis-NIR Spectral Library

Soil visible and near-infrared (Vis-NIR, 350–2500 nm) spectroscopy has been proven as an alternative to conventional laboratory analysis due to its advantages being rapid, cost-effective, non-destructive and environmentally friendly. Different variable selection methods have been used to deal with the high redundancy, heavy computation, and model complexity of using full spectra in spectral modelling. However, most previous studies used a linear algorithm in the variable selection, and the application of a non-linear algorithm remains poorly explored. To address the current knowledge gap, based on a regional soil Vis-NIR spectral library (1430 soil samples), we evaluated seven variable selection algorithms together with three predictive algorithms in predicting seven soil properties. Our results showed that Cubist overperformed partial least squares regression (PLSR) and random forests (RF) in most soil properties (R2 > 0.75 for soil organic matter, total nitrogen and pH) when using the full spectra. Most of variable selection can greatly reduce the number of spectral bands and therefore simplified predictive models without losing accuracy. The results also showed that there was no silver bullet for the optimal variable selection algorithm among different predictive algorithms: (1) competitive adaptive reweighted sampling (CARS) always performed best for the PLSR algorithm, followed by forward recursive feature selection (FRFS); (2) recursive feature elimination (RFE) and genetic algorithm (GA) generally had better accuracy than others for the Cubist algorithm; and (3) FRFS had the best model performance for the RF algorithm. In addition, the performance was generally better when the algorithm used in the variable selection matched the predictive algorithm. The outcome of this study provides a valuable reference for predicting soil information using spectroscopic techniques together with variable selection algorithms.

A Vis-NIRs Calibration Model for the Prediction of Myristicin and Alpha-Pinene on Nutmeg: A Comparison Study of PLSR Algorithm and Machine Learning Algorithm

Determination of the myristicin and alpha-pinene content of nutmeg is still constrained by the extended testing time in the laboratory, which is expensive and is carried out destructively. In addition, non-destructive testing using spectroscopy often faces problems in building models that only rely on algorithms that perform linearly, such as PCR and PLSR. Therefore, the present study studied Vis-NIR (381-1065 nm) as a fast, inexpensive, and non-destructive mechanism to determine the myristicin and alpha-pinene of nutmeg fruits from Aceh, Indonesia. Two algorithms commonly used in spectral data processing, partial least squares regression (PLSR) and machine learning represented by a support vector machine (SVM), were employed and compared to predict myristicin and alpha-pinene in nutmeg fruits. The chemical reference parameters (myristicin and alpha-pinene) were measured using gas chromatography mass spectrometry (GC-MS). Standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing were involved as spectra enhancement before the prediction models outcome. The results show that the kernel of the radial basis function (RBF) kernel n-SVM algorithm is better than PLSR for myristicin prediction with gamma (g), c, and nu (n) of 0.1, 1.0, and 0.99, respectively. Also, the e-SVM algorithm by RBF kernel is better than PLSR for the prediction of alpha-pinene in nutmeg fruits with gamma (g), c, and epsilon (e) compositions of 0.01, 10, 0.1, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp2), the root means square error of calibration (RMSEC) and prediction (RMSEP), and the ratio (RPD) for the prediction of myristicin were 0.992, 0.986, 0.941%, 1.325% and 8.348, respectively. The coefficient correlation of calibration (rc) and coefficient determination of prediction (Rp2), the root mean square error of calibration (RMSEC) and prediction (RMSEP), and the ratio of prediction to deviation ratio (RPD) for the prediction of alpha-pinene were 0.976, 0.979, 0.305%, 0.317% and 6.826, respectively. In general, the results satisfactorily indicate that Vis-NIRS, with the appropriate algorithm, has promising results in determining myristicin and alpha-pinene on nutmeg from Aceh, Indonesia, as nondestructive measurement.

Wind loss model for the thick canopies of orchard trees based on accurate variable spraying.

Variable application by wind is an efficient application technology recommended by the Food and Agriculture Organization (FAO) of the United Nations that can effectively improve the deposition effect of liquid medicine in a canopy and reduce droplet drift. In view of the difficulty of modelling wind forces in orchard tree canopies and the lack of a wind control model, the wind loss model for a canopy was studied. First, a three-dimensional wind measurement test platform was built for an orchard tree canopy. The orchard tree was located in three-dimensional space, and the inner leaf areas of the orchard tree canopy and the wind force in different areas were measured. Second, light detection and ranging (LiDAR) point cloud data of the orchard tree canopy were obtained by LiDAR scanning. Finally, classic regression, partial least squares regression (PLSR), and back propagation (BP) neural network algorithms were used to build wind loss models in the canopy. The research showed that the BP neural network algorithm can significantly improve the fitting accuracy of the model. Under different fan speeds of 1,381 r/min, 1,502 r/min, and 1,676 r/min, the coefficient of determination (R2) of the model were 81.78, 72.85, and 69.20%, respectively, which were 19.38, 7.55, and 12.3% higher than those of the PLSR algorithm and 21.48, 22.25, and 24.3% higher than those of multiple regression analysis. The comparison showed that the BP neural network algorithm obtains the highest model accuracy, but because the model is not intuitive, PLSR has the advantages of intuitive and simple models in the three algorithms. In practical applications, the wind loss model based on a BP neural network or PLSR can be selected according to the operational requirements and software and hardware conditions. This study can provide a basis for wind control in precise variable spraying and promote the development of wind control technologies.

Innovative Fusion-Based Strategy for Crop Residue Modeling

The purpose of this study was to present a new strategy based on fusion at the decision level for modeling the crop residue. To this end, a set of satellite imagery and field data, including the Residue Cover Fraction (RCF) of corn, wheat and soybean was used. Firstly, the efficiency of Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Partial-Least-Squares Regression (PLSR) in RCF modeling was evaluated. Furthermore, to increase the accuracy of RCF modeling, different algorithms results were combined based on their modeling error, which is called the decision-based fusion strategy. The R2 (RMSE) between the actual and modeled RCF based on ANN, RFR, SVR and PLSR algorithms for corn were 0.83 (3.89), 0.86 (3.25), 0.76 (4.56) and 0.75 (4.81%), respectively. These values were 0.81 (4.86), 0.85 (4.22), 0.78 (5.45) and 0.74 (6.20%) for wheat and 0.81 (3.96), 0.83 (3.38), 0.76 (5.01) and 0.72 (5.65%) for soybean, respectively. The error of corn, wheat and soybean RCF estimating decision-based fusion strategy was reduced by 0.90, 0.96 and 0.99%, respectively. The results showed that by implementing the decision-based fusion strategy, the accuracy of the RCF modeling was significantly improved.

Performance Evaluation of Pre-Processing and Pre-Treatment Algorithm for Near-Infrared Spectroscopy Signals: Case Study pH of Intact Mango “Arumanis”

pH is one of the important physical parameters to characterize mango damage because it can indicate changes in the structure and chemical content of the fruit. Thus, the present work evaluated the possibility of NIRs as a rapid and non-destructive tool for measuring the pH properties of intact mango from the cultivar "Arumanis" (Mangifera indica L.) using several algorithms for pre-processing, pre-treatment, and prediction. Three different algorithm predictions, namely principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR), were used and compared to predict the pH of mangos. A total of 16 pre-processing and pre-treatment algorithms are used to support algorithm prediction, and the results are also compared with the raw data spectra. The NIR spectral data used range from 1000 to 2500 nm. Algorithm performance will be evaluated using RMSE, error differences and concluded using RPD. The results show that the prediction of the PLSR algorithm can be performed with an RPD of 8.17, which is more significant than the PCR and SVMR algorithms, which are 1.04, and 1.90, respectively. To support this, pre-processing and pretreatment of the second derivative Savitzky–Golay is the best algorithm that can be used to predict the pH of the whole mango cultivar "Arumanis".

Innovative Leaf Area Detection Models for Orchard Tree Thick Canopy Based on LiDAR Point Cloud Data

Orchard spraying can effectively control pests and diseases. Over-spraying commonly results in excessive pesticide residues on agricultural products and environmental pollution. To avoid these problems, variable spraying technology uses target canopy detection to evaluate the leaf area in a canopy and adjust the application rate accordingly. In this study, a mobile LiDAR detection platform was set up to automatically measure point cloud data for a thick canopy in an apple orchard. A test platform was built, and manual measurements of the canopy leaf area were taken. Then, polynomial regression, back propagation (BP) neural network regression, and partial least squares regression (PLSR) algorithms were used to study the relationship between the orchard tree canopy point clouds and leaf areas. The BP neural network algorithm (86.1% and 73.6% accuracies for the test and verification data, respectively) and the PLSR algorithm (78.46% and 60.3%, respectively) performed better than the Fourier function of the polynomial regression (59.73% accuracy). The leaf area model obtained using PLSR was intuitive and simple, while the BP neural network algorithm was more accurate and could meet the requirements for high-precision variable spraying.

A near‐infrared spectroscopy method for the detection of texture profile analysis of Litopeneo vannamei based on partial least squares regression

AbstractAmong aquatic products freshness testing methods, spectral detection method is the most commonly used rapid nondestructive testing method, and the quality structure index can be used as the judgment index of aquatic products freshness, so studying the near‐infrared (NIR) spectroscopy detection method of aquatic products based on texture index is helpful to improve the detection accuracy of freshness of products. In order to find a method for NIR spectroscopy based on texture indicators, we took shrimp as the research target, collected total 216 NIR spectral data of 680–2600 nm and texture index of sample for 8 consecutive days, and established a quantitative prediction model. In this study, we compared the effects of six spectral pretreatments on partial least squares regression (PLSR) models, including multiple scattering correction, standard normal variate transformation, mean centering, Savitzky–Golay (S‐G) smoothing, moving average method, and normalization. The results show that the PLSR model based on S‐G smoothing has the best model accuracy in prediction after the S‐G smoothing algorithm has preprocessed the spectral data. In addition, principal component analysis and the Mahalanobis distance algorithm were used in the study to remove abnormal samples to improve the accuracy of the model. Finally, the quantitative prediction model for qualitative construct indicators constructed based on the S‐G smoothing algorithm and the PLSR algorithm had better prediction results than the other models algorithm we considered, with a correlation coefficient (R) of 0.868 for the cohesive validation set and 0.781 for the elastic validation set of qualitative construct indicators. Finally, based on the above study, this study developed a prediction graphical user interface system for qualitative metrics of texture.Practical ApplicationsMost of the existing aquatic meat freshness detection methods are offline detection based on physical and chemical tests, and some use spectral technology to detect the chemical parameters of aquatic meat freshness. This project innovatively applies NIR spectroscopy technology to aquatic products. The physical parameters of meat freshness can be quickly detected online. By preprocessing the data samples and selecting the response characteristic wavelengths, a NIR spectroscopy prediction model of texture indicators with self‐learning ability and high precision is constructed, and the texture is established based on this. Indicator NIR spectroscopy prediction system. This whole process provides new research methods and analytical tools for rapid nondestructive testing of aquatic product freshness.

Angular effect of algorithms for monitoring leaf nitrogen concentration of wheat using multi-angle remote sensing data

Leaf nitrogen concentration (LNC) is a key indicator of crops’ growth status, and the timely and accurate large-scale area monitoring of LNC is crucial for guiding field management. Here, we collected LNC data and hyperspectral data at the canopy scale, from 13 view zenith angles (VZAs) and two locations (Zhengzhou and Shangshui, in China) across 2 years (2012–2014). Four data processing methods—vegetation indices (VIs), back-propagation neural network (BPNN), eXtreme gradient boost (XGBoost), and partial least squares regression (PLSR)—were applied and compared for their ability to estimate LNC under 13 VZAs. These results revealed consistent trends in the performance of the four algorithms at 13 VZAs: the nadir direction is better than the extreme angle, and the best performance is obtained for the range of –30° to 0° (R2 ≥ 0.83; RMSE ≤ 0.41%). The number of bands is a critical factor affecting the accuracy of LNC monitoring: including red edge bands can alleviate angular effect to some extent. The accuracy of PLSR for monitoring LNC is not only superior near the nadir direction (R2 = 0.91), but also better than that provided VIs (16%–17%), BPNN (15%–16%), or XGBoost (29%–58%) at ± 60°. Therefore, it is strongly recommended the PLSR algorithm be used to process multi-angle remote sensing data to estimate the LNC of field crops. This work provides a timely reference basis for selecting the appropriate flight angle and the number and positioning of bands for unmanned aerial vehicle (UAV) and satellite applications in the future.

Application of PCR and PLS Tools for the Simultaneous Quantification of Praziquantel and Ivermectin Binary Mixtures in Veterinary Tablets

Two chemometric calibration methods, principal component regression (PCR) and partial least squares regression (PLS) were proposed for the simultaneous spectrophotometric determination of praziquantel (PRA) and ivermectin (IVE) in a marketed veterinary formulation without using preliminary separation step. UV spectra of calibration set and samples including PRA and IVE were recorded in the spectral region of 225-315 nm. PCR and PLS algorithms were applied to absorbance data matrix and concentration set including PRA and IVE in the linear concentration range of 20.0-160.0 µg/mL for PRA and 2.0-44.0 µg/mL for IVE. The capability of the PCR and PLS methods were validated by analyzing validation samples. Assay results showed that both PCR and PLS approaches provided an opportunity for quantifying PRA and IVE in veterinary tablet formulation.

Method for detecting soil total nitrogen contents based on pyrolysis and artificial olfaction

Soil nitrogen is an essential nutrient element for crop growth and development, and an important indicator of soil fertility characteristics. This study proposed a method based on pyrolysis and artificial olfaction to quickly and accurately determine the soil total nitrogen (STN) content. A muffle furnace was used to pyrolyze the soil samples, and ten different types of oxide semiconductor gas sensors were used to construct a sensor array to detect the soil samples’ pyrolysis gas. The response curves of the sensors were tested at pyrolysis temperatures of 200°C, 300°C, 400°C, and 500°C and at pyrolysis times of 1 min, 3 min, 5 min, and 10 min to obtain the optimal pyrolysis state of the soil samples. The optimal pyrolysis temperature was 400°C, and the pyrolysis time was 3 min. The response area, maximum value, average differential coefficient, variance value, maximum gradient value, average value, and 8th-second transient value of the sensor response curve were extracted to construct an artificial olfactory feature space of 121×10×7 (121 soil samples, ten sensor numbers, seven extracted eigenvalues). Back-propagation neural network algorithm (BPNN), partial least squares regression algorithm (PLSR), and partial least squares regression combined with back-propagation neural network algorithm (PLSR-BPNN) were used to establish a prediction model of artificial olfactory feature space and STN content. Moreover, coefficient of determination (R2), root mean square error (RMSE), and the ratio of performance to deviation (RPD) were used as the performance indicators of the prediction results. The test results showed that the R2 of the PLSR, BPNN, and PLSR-BPNN models were 0.89033, 0.81185, and 0.92186, and the RMSE values were 0.24297, 0.37370, and 0.21781, and the RPD were 2.9964, 1.9482, and 3.3426, respectively. The model established by the PLSR-BPNN algorithm has the highest R2 and RPD and the smallest RMSE, can achieve the accurate prediction of STN content, and therefore the model is rated as “excellent”. The detection method in this study achieves a low-cost, rapid, and accurate determination of STN content, and provides a new reference for the measurement of STN. Keywords: pyrolysis, artificial olfactory system, soil total nitrogen, gas sensor array, prediction methods DOI: 10.25165/j.ijabe.20221503.7086 Citation: Li M W, Zhu Q H, Liu H, Xia X M, Huang D Y. Method for detecting soil total nitrogen contents based on pyrolysis and artificial olfaction. Int J Agric & Biol Eng, 2022; 15(3): 167–176.