Full Band Research Articles

Dirac points, new photonic band gaps, and effect of magnetically induced transparency in dichroic cholesteric liquid crystals with wavelength-dependent magneto-optical activity parameter.

We investigated the properties of cholesteric liquid crystals (CLCs) being in an external static magnetic field directed along the helix axis. We considered a dichroic CLC, that is, CLC with parameters ReΔ=Reɛ_{1}-Reɛ_{2}/2=0 and ImΔ=Imɛ_{1}-Imɛ_{2}/2≠0, where ɛ_{1,2} are the principal values of the local dielectric permittivity tensor. We have shown that in the case of the wavelength dependence of the magneto-optic activity parameter, new features appear in the optics of dichroic CLCs, in particular, in this case new Dirac points appear. Dirac points are points where there is an intersection of any two wave vector curves (they degenerate) and a linear law of the wave vector dependence on the frequency near these points. And moreover, at some Dirac points photonic band gaps (PBGs) appear; at others, lines of magnetically induced transparency (MIT), that is, a full transmission band appears, in an absorbing medium. In this case a polarization-sensitive transmission band appears too. At certain values of the helix pitch of the CLC and of the magnitude of the external magnetic field, three PBGs of different nature appear: a transmittance band, two narrow lines of MIT, and at others a broadband MIT, etc. This system is nonreciprocal, and the nonreciprocity changes over a wide range. It is observed both for reflection and transmittance and for absorption. The soft-matter nature of CLCs and their response to external influences lead to easily tunable multifunctional devices that can find a variety of applications. They can be applied as tunable narrow-band or broadband filters and mirrors, a highly tunable broad/narrow-band coherent perfect absorber, transmitter, ideal optical diode, and in other devices.

Band Nonlinearity-Enabled Manipulation of Dirac Nodes, Weyl Cones, and Valleytronics with Intense Linearly Polarized Light.

We study low-frequency linearly polarized laser-dressing in materials with valley (graphene and hexagonal-Boron-Nitride) and topological (Dirac- and Weyl-semimetals) properties. In Dirac-like linearly dispersing bands, the laser substantially moves the Dirac nodes away from their original position, and the movement direction can be fully controlled by rotating the laser polarization. We prove that this effect originates from band nonlinearities away from the Dirac nodes. We further demonstrate that this physical mechanism is widely applicable and can move the positions of the valley minima in hexagonal materials to tune valley selectivity, split and move Weyl cones in higher-order Weyl semimetals, and merge Dirac nodes in three-dimensional Dirac semimetals. The model results are validated with ab initio calculations. Our results directly affect efforts for exploring light-dressed electronic structure, suggesting that one can benefit from band nonlinearity for tailoring material properties, and highlight the importance of the full band structure in nonlinear optical phenomena in solids.

Abnormal interaction between cortical regions of obstructive sleep apnea hypopnea syndrome children.

Obstructive sleep apnea hypopnea syndrome negatively affects the cognitive function of children. This study aims to find potential biomarkers for obstructive sleep apnea hypopnea syndrome in children by investigating the patterns of sleep electroencephalography networks. The participants included 16 mild obstructive sleep apnea hypopnea syndrome children, 12 severe obstructive sleep apnea hypopnea syndrome children, and 13 healthy controls. Effective brain networks were constructed using symbolic transfer entropy to assess cortical information interaction. The information flow pattern in the participants was evaluated using the parameters cross-within variation and the ratio of posterior-anterior information flow. Obstructive sleep apnea hypopnea syndrome children had a considerably higher symbolic transfer entropy in the full frequency band of N1, N2, and rapid eye movement (REM) stages (P < 0.05), and a significantly lower symbolic transfer entropy in full frequency band of N3 stage (P < 0.005), in comparison with the healthy controls. In addition, the cross-within variation of the β frequency band across all sleep stages were significantly lower in the obstructive sleep apnea hypopnea syndrome group than in the healthy controls (P < 0.05). What is more, the posterior-anterior information flowin the β frequency band of REM stage was significantly higher in mild obstructive sleep apnea hypopnea syndrome children than in the healthy controls (P < 0.05). These findings may serve as potential biomarkers for obstructive sleep apnea hypopnea syndrome in children and provide new insights into the pathophysiological mechanisms.

A composite acoustic black hole for ultra-low-frequency and ultra-broad-band sound wave control

Achieving ultra-low and ultra-broad-band sound absorption and full-band sound insulation is a major challenge. Here, we propose a composite structure of a multilayer micro-perforated plate and acoustic black holes to achieve this purpose. Combining the stable sound absorption effect of the multilayer micro-perforated plate in the full frequency band and the sound insulation effect of the acoustic black hole in the low frequency and the excellent sound absorption effect in the high frequency, the excellent sound control effect of 600–3150 Hz absorption coefficient greater than 0.8 and 100–3150 Hz sound transmission loss greater than 50 dB is achieved. The acoustic properties of different components and different acoustic black hole outlet were evaluated by finite element method, and the principles of sound absorption and insulation of the composite structure were elaborated. Finally, the results of finite element method are verified by impedance tube experiments. This work can make further progress in elucidating the acoustic properties of the ABH and open up new avenues in the control of ultra-low and ultra-wide frequency acoustic waves.

Wideband optical edge detection based on dielectric metasurface

Optical image edge detection manifests itself as the advantages of fast speed, low energy consumption, parallel operation, and large information capacity, playing a crucial role in object and image recognition in the realms of biology, medicine, and artificial intelligence. In this paper, a dielectric metasurface that can achieve object edge detection in the full visible band is proposed. By combining two optical differential methods referring to spatial spectrum-dependent transmission function and 4f system-based spatial spectrum phase shift, a tetratomic macropixel metasurface that enables polarization-correlated interference based on dual geometric phase modulation is designed, which can achieve object edge detection under 473, 530, and 633 nm wavelengths, and even under white light conditions. This coherent filtering metasurface operator provides a flexible approach for achieving wideband two-dimensional edge detection.

The Structural Characterisation and DFT-Aided Interpretation of Vibrational Spectra for Cyclo(l-Cys-d-Cys) Cyclic Dipeptide in a Solid State.

Cyclic dipeptides with two intramolecular peptide bonds forming a six-membered 2,5-diketopiperazine ring are gaining significant attention due to their biological and chemical properties. Small changes in the local geometry of such molecules (from cis to trans) can lead to significant structural differences. This work presents the results of a study of cyclo(l-Cys-d-Cys), a dipeptide comprising two cysteine molecules in opposite chiral configurations, with the functional groups situated at both sides of the diketopiperazine ring. X-ray diffraction (XRD) experiment revealed that the molecule crystallises in the P-1 space group, which includes the centre of inversion. The IR and Raman vibrational spectra of the molecule were acquired and interpreted in terms of the potential energy distribution (PED) according to the results of density functional theory (DFT) calculations. The DFT-assisted analysis of energy frameworks for the hydrogen bond network within molecular crystals was performed to support the interpretation of X-ray structural data. The optimisation of the computational model based on three-molecule geometry sections from the crystallographic structure, selected to appropriately reflect the intermolecular interactions responsible for the formation of 1D molecular tapes in cyclo(l-Cys-d-Cys) crystal, allowed for better correspondence between theoretical and experimental vibrational spectra. This work can be considered the first complete structural characterisation of cyclo(l-Cys-d-Cys), complemented via vibrational spectroscopy results with full band assignment aided with the use of the DFT method.

Research on the Dynamic Model of Fireball Thermal Dose Based on the Effective Band Integral Method.

In the brief combustion process of an explosive fireball, the fireball can release considerable radiant energy. Aiming at the problem that the Stephen-Boltzmann formula calculates the fireball surface radiant energy (full band), it does not match the working bands of most infrared thermal imagers. So, in this paper, we obtain dynamic parameters such as the temperature, diameter, and height of the fireball from the infrared thermal image of the thermobaric explosive fireball, achieve on-site atmospheric transmittance by the temperature calibration target, and integrate within the effective wavelength band of the infrared thermal imager, and a precise dynamic model of the fireball's thermal radiation dose was finally established. According to the fireball test data of the infrared thermal imaging camera in the 2-5 μm band, the heat dose of the fireball at different distances is calculated, which is about 1/2.5 of the calculation result of the Stephen-Boltzmann full-band integral formula. The calculations in this paper are more accurate than measurements from existing static models and provide a better assessment of the thermal damage performance of various types of munitions.

Broadband Millimeter-Wave Hybrid Circulators

A new broadband hybrid circulator has been developed for use at millimeter-wave (MMW) frequencies. The hybrid circulator comprises a Faraday rotator and an orthomode transducer (OMT) which work together to produce the circulator function. The initial hybrid circulator prototypes employ an asymmetric OMT and are designed to have 20 dB isolation over 24% fractional bandwidths. However, the architecture can be modified to cover full rectangular waveguide bands by using a symmetric OMT. The hybrid circulator bandwidth does not degrade at the higher MMW frequencies like the Y-junction. For example, a well-tuned Y-junction circulator designed to operate at 60 GHz has a 20 dB fractional bandwidth near 10%. But the bandwidth drops to 2% for a Y-junction circulator operating at 160 GHz. Test data are presented for two hybrid circulator prototypes covering the bands 53–68 GHz in WR-15 and 150–190 GHz in WR-5. The measured insertion loss of the hybrid circulator is typically less than 0.8 dB over the 53–68 GHz band and near 2 dB over the 150–190 GHz band. The measured VSWR is less than 1.4:1 in both bands. The hybrid circulator can be scaled to at least 400 GHz.

Absence of Topological Protection of the Interface States in Z_{2} Photonic Crystals.

Inspired from electronic systems, topological photonics aims to engineer new optical devices with robust properties. In many cases, the ideas from topological phases protected by internal symmetries in fermionic systems are extended to those protected by crystalline symmetries. One such popular photonic crystal model was proposed by Wu and Hu in 2015 for realizing a bosonic Z_{2} topological crystalline insulator with robust topological edge states, which led to intense theoretical and experimental studies. However, a rigorous relationship between the bulk topology and edge properties for this model, which is central to evaluating its advantage over traditional photonic designs, has never been established. In this Letter, we revisit the expanded and shrunken honeycomb lattice structures proposed by Wu and Hu and show that they are topologically trivial in the sense that symmetric, localized Wannier functions can be constructed. We show that the Z and Z_{2} type classifications of the Wu-Hu model are equivalent to the C_{2}T protected Euler class and the second Stiefel-Whitney class, respectively, with the latter characterizing the full valence bands of the Wu-Hu model, indicating only a higher order topological insulator. Additionally, we show that the Wu-Hu interface states can be gapped by a uniform topology preserving C_{6} and T symmetric perturbation, which demonstrates the trivial nature of the interface. Our result reveals that topology is not a necessary condition for the reported helical edge states in many photonics systems and opens new possibilities for interface engineering that may not be constrained by topological considerations.

Detecting Surface Defects of Achacha Fruit (Garcinia humilis) with Hyperspectral Images

Hyperspectral imaging data within the wavelength range of 400–1000 nm were used to classify the common skin conditions (i.e., normal, scar, decay, and insect bite) of achacha fruits. The band ratio (BR) and spectral angle mapper (SAM) algorithms were used in a binary classification. Furthermore, SAM, support vector machine (SVM), and artificial neural network (ANN) models were used in a multiclass classification. The performances of the binary and multiclass classification models were assessed. For the binary-classification approach, the three defective classes were merged into one, and the accuracies of the BR (990 nm/600 nm) and SAM were 78.70% and 75.02%, respectively. Furthermore, the SAM, SVM, and ANN accuracies in the four class problems were 58.36%, 83.59%, and 99.88%, respectively. A principal component analysis (PCA) was used for the data reduction. Nine characteristic wavelengths were extracted from the weighting-coefficient curves of the first four principal components. Using only the nine selected bands, the accuracies of the SAM, SVM, and ANN models were 51.49%, 80.76%, and 96.85%, respectively. Compared with the models using full bands, the classification accuracies of the models using only nine characteristic bands decreased slightly; however, the gain in classification speed and the potential data-acquisition speed can expedite the classification of achacha fruits.

Intelligent identification on cotton verticillium wilt based on spectral and image feature fusion

BackgroundVerticillium wilt is the major disease of cotton, which would cause serious yield reduction and economic losses, and the identification of cotton verticillium wilt is of great significance to cotton research. However, the traditional method is still manual, which is subjective, inefficient, and labor-intensive, and therefore, this study has proposed a novel method for cotton verticillium wilt identification based on spectral and image feature fusion. The cotton hyper-spectral images have been collected, while the regions of interest (ROI) have been extracted as samples including 499 healthy leaves and 498 diseased leaves, and the average spectral information and RGB image of each sample were obtained. In spectral feature processing, the preprocessing methods including Savitzky-Golay smoothing (SG), multiplicative scatter correction (MSC), de-trending (DT) and mean normalization (MN) algorithms have been adopted, while the feature band extraction methods have adopted principal component analysis (PCA) and successive projections algorithm (SPA). In RGB image feature processing, the EfficientNet was applied to build classification model and 16 image features have been extracted from the last convolutional layer. And then, the obtained spectral and image features were fused, while the classification model was established by support vector machine (SVM) and back propagation neural network (BPNN). Additionally, the spectral full bands and feature bands were used as comparison for SVM and BPNN classification respectively.ResultThe results showed that the average accuracy of EfficientNet for cotton verticillium wilt identification was 93.00%. By spectral full bands, SG-MSC-BPNN model obtained the better performance with classification accuracy of 93.78%. By feature bands, SG-MN-SPA-BPNN model obtained the better performance with classification accuracy of 93.78%. By spectral and image fused features, SG-MN-SPA-FF-BPNN model obtained the best performance with classification accuracy of 98.99%.ConclusionsThe study demonstrated that it was feasible and effective to use fused spectral and image features based on hyper-spectral imaging to improve identification accuracy of cotton verticillium wilt. The study provided theoretical basis and methods for non-destructive and accurate identification of cotton verticillium wilt.

Attenuation Characteristics of Vibrationin a Locally Resonant Phononic Crystal Frame Structure

In this paper, a frame structure based on the locally resonant (LR) mechanism of phononic crystals (PCs) is designed on account of the wide application of frame structures in high-rise buildings, and the band structures, displacement fields of eigenmodes, and transmission power spectrums of corresponding finite structure are calculated by finite element (FE) method. Numerical results and further analysis demonstrate that a full band gap with low starting frequency can be opened by the frame structure formed by periodically combining soft and hard materials, and the starting frequency can be further lowered with the adjustment of corresponding geometric parameters, which provides a theoretical basis for the studies on vibration insulation and noise reduction of high-rise buildings.

Rethinking Representation Learning-Based Hyperspectral Target Detection: A Hierarchical Representation Residual Feature-Based Method

Representation learning-based hyperspectral target detection (HTD) methods generally follow a learning paradigm of single-layer or one-step representation residual learning and the target detection on original full spectral bands, which, in some cases, cannot accurately distinguish the target pixels from variable background pixels via one-round of the detection process. To alleviate the problem and make full use of the latent discriminate characteristics in different spectral bands and the representation residual, this paper proposes a level-wise band-partition-based hierarchical representation residual feature (LBHRF) learning method for HTD with a parallel and cascaded hybrid structure. Specifically, the LBHRR method proposes to partition and fuse different levels of sub-band spectra combinations, and take full advantages of the discriminate information in representation residuals from different levels of band-partition. The highlights of this work include three aspects. First, the original full spectral bands are partitioned in a parallel level-wise manner to obtain the augmented representation residual feature through level-wise band-partition-based representation residual learning, such that the global spectral integrity and contextual information of local adjacent sub-bands are flexibly fused. Second, the SoftMax transformation, pooling operation, and augmented representation residual feature reuse among different layers are equipped in cascade to enhance the learning ability of the nonlinear and discriminant hierarchical representation residual features of the method. Third, a hierarchical representation residual feature-based HTD method is developed in an efficient stepwise learning manner instead of back-propagation optimization. Experimental results on several HSI datasets demonstrate that the proposed model can yield promising detection performance in comparison to some state-of-the-art counterparts.

Semi-Empirical Pseudopotential Method for Graphene and Graphene Nanoribbons.

We implemented a semi-empirical pseudopotential (SEP) method for calculating the band structures of graphene and graphene nanoribbons. The basis functions adopted are two-dimensional plane waves multiplied by several B-spline functions along the perpendicular direction. The SEP includes both local and non-local terms, which were parametrized to fit relevant quantities obtained from the first-principles calculations based on the density-functional theory (DFT). With only a handful of parameters, we were able to reproduce the full band structure of graphene obtained by DFT with a negligible difference. Our method is simple to use and much more efficient than the DFT calculation. We then applied this SEP method to calculate the band structures of graphene nanoribbons. By adding a simple correction term to the local pseudopotentials on the edges of the nanoribbon (which mimics the effect caused by edge creation), we again obtained band structures of the armchair nanoribbon fairly close to the results obtained by DFT. Our approach allows the simulation of optical and transport properties of realistic nanodevices made of graphene nanoribbons with very little computation effort.

A highly robust RF 65 nm CMOS power amplifier design using Quasi-Newton control algorithm for wireless system

This article reports a novel robust approach towards CMOS power amplifier (PA) using Quasi-newton (QN) control algorithm in 65 nm CMOS process which provides best performance parameters over redundant wide bandwidth ranging from 2.4 to 16.4 GHz frequency band. Each stage are designed and optimized using QN algorithm to get desired goals such as high linearity, small group delay variations and high PAE across the entire frequency band of interest. Moreover, pole-zeros compensation technique is adopted and derived to get better stability of the proposed PA. The simulation and measurement results of PA achieved a small signal power gain of 10.5–16.8 dB with input return loss of better than 10 dB over the frequency band of 2.4 GHz to 16.4 GHz. A small group delay variation of ±58 ps over full frequency band of operation is achieved by optimizing the design parametric analysis. It is also observed that within the frequency of 6.5 to 14.6 GHz, an excellent small group delay variation of only ±11 ps is achieved and this is due to stage-2 tuning compensation technique. It also demonstrates the achieved input power in 1 dB compression points are −3.1 to 4.3 dBm, leading to maximum power added efficiency of 36.3%, respectively. The proposed PA consumes a lower DC power of 20.5 mW under supply voltage of 1.5. In addition, Process, voltage and temperature (PVT) analysis is executed at different conditions in order to achieve a robustness of the proposed PA over the entire band of operation.

Multi‐channel response reconstruction using transformer based generative adversarial network

AbstractAccurate measurement data are a basic prerequisite for effective structural health monitoring (SHM). However, data loss are inevitable in the long‐term monitoring of large‐scale structures. To solve this problem, this research proposes a transformer‐based generative adversarial network (GAN) to reconstruct lost measurements from observed measurements. The generator of GAN is an encoder‐decoder structure using transformer as the backbone combined with discrete wavelet transform. Skip connections are used between the encoder part and decoder part to promote multi‐scale information flow. A novel discriminator is designed to assess the reality of wavelet spectra of reconstructed samples. To deceive the discriminator, the generator must generate samples that are accurate over the full frequency band. The developed model is used to reconstruct linear responses of a footbridge under pedestrian excitations and nonlinear responses of a suspension bridge under typhoon events. Experimental results demonstrate that lost responses can be reconstructed accurately, even when a large proportion of data are lost. The effectiveness of the proposed method is further verified by comparing the reconstruction accuracy of the proposed model with those of other three state‐of‐the‐art models. The results demonstrate that an improved performance of applying the proposed approach for dynamic structural response reconstruction is achieved and validated with in‐field testing data under ambient and extreme excitation conditions.

Differential benefits of unmasking extended high-frequency content of target or background speech.

Current evidence supports the contribution of extended high frequencies (EHFs; >8 kHz) to speech recognition, especially for speech-in-speech scenarios. However, it is unclear whether the benefit of EHFs is due to phonetic information in the EHF band, EHF cues to access phonetic information at lower frequencies, talker segregation cues, or some other mechanism. This study investigated the mechanisms of benefit derived from a mismatch in EHF content between target and masker talkers for speech-in-speech recognition. EHF mismatches were generated using full band (FB) speech and speech low-pass filtered at 8 kHz. Four filtering combinations with independently filtered target and masker speech were used to create two EHF-matched and two EHF-mismatched conditions for one- and two-talker maskers. Performance was best with the FB target and the low-pass masker in both one- and two-talker masker conditions, but the effect was larger for the two-talker masker. No benefit of an EHF mismatch was observed for the low-pass filtered target. A word-by-word analysis indicated higher recognition odds with increasing EHF energy level in the target word. These findings suggest that the audibility of target EHFs provides target phonetic information or target segregation and selective attention cues, but that the audibility of masker EHFs does not confer any segregation benefit.

Identification of Gentian-Related Species Based on Two-Dimensional Correlation Spectroscopy (2D-COS) Combined with Residual Neural Network (ResNet).

Gentian is a traditional Chinese herb with heat-clearing, damp-drying, inflammation-alleviating and digestion-promoting effects, which is widely used in clinical practice. However, there are many species of gentian. According to the pharmacopoeia, Gentiana manshurica Kitag, Gentiana scabra Bge, Gentiana triflora Pall and Gentianarigescens Franch are included. Therefore, accurately identifying the species of gentian is important in clinical use. In recent years, with the advantages of low cost, convenience, fast analysis and high sensitivity, infrared spectroscopy (IR) has been extensively used in herbal identification. Unlike one-dimensional spectroscopy, a two-dimensional correlation spectrum (2D-COS) can improve the resolution of the spectrum and better highlight the details that are difficult to detect. In addition, the residual neural network (ResNet) is an important breakthrough in convolutional neural networks (CNNs) for significant advantages related to image recognition. Herein, we propose a new method for identifying gentian-related species using 2D-COS combined with ResNet. A total of 173 gentian samples from seven different species are collected in this study. In order to eliminate a large amount of redundant information and improve the efficiency of machine learning, the extracted feature band method was used to optimize the model. Four feature bands were selected from the infrared spectrum, namely 3500-3000 cm-1, 3000-2750 cm-1, 1750-1100 cm-1 and 1100-400 cm-1, respectively. The one-dimensional spectral data were converted into synchronous 2D-COS images, asynchronous 2D-COS images, and integrative 2D-COS images using Matlab (R2022a). The identification strategy for these three 2D-COS images was based on ResNet, which analyzes 2D-COS images based on single feature bands and full bands as well as fused feature bands. According to the results, (1) compared with the other two 2D-COS images, synchronous 2D-COS images are more suitable for the ResNet model, and (2) after extracting a single feature band 1750-1100 cm-1 to optimize ResNet, the model has the best convergence performance, the accuracy of training, test and external validation is 1 and the loss value is only 0.155. In summary, 2D-COS combined with ResNet is an effective and accurate method to identify gentian-related species.

A method for fast calculating the electronic states in 2D quantum structures based on AIIIBV nitrides

The paper presents a method for fast calculating the electronic states in two-dimensional quantum structures based on AIIIBV nitrides. The method is based on the representation of electronic states in the form of a linear combination of bulk wave functions of materials, from which quantum structures are made. The parameters and criteria for the selection of bulk wave functions that provides fast convergence of the numerical procedures for calculating the eigenvalues of the quantum Hamiltonian have been considered. The results of the calculations have been given both for one polar InGaN/GaN quantum well and for a system of several quantum wells. Being based on the full band structure of AIIIBV nitrides with a wurtzite-type crystal lattice, the proposed approach takes into account the states far from the center of the Brillouin zone, while preserving the computational efficiency of traditional methods of envelope function in approximating the effective mass.

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops.

Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R2 values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C3 and C4 plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, β-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.