Hyperspectral Feature Research Articles

Estimation Network for Multiple Chemical Parameters of Astragalus Leaves Based on Attention Mechanism and Multivariate Hyperspectral Features

In the context of smart agriculture, accurately estimating plant leaf chemical parameters is crucial for optimizing crop management and improving agricultural yield. Hyperspectral imaging, with its ability to capture detailed spectral information across various wavelengths, has emerged as a powerful tool in this regard. However, the complex and high-dimensional nature of hyperspectral data poses significant challenges in extracting meaningful features for precise estimation. To address this challenge, this study proposes an end-to-end estimation network for multiple chemical parameters of Astragalus leaves based on attention mechanism (AM) and multivariate hyperspectral features (AM-MHENet). We leverage HybridSN and multilayer perceptron (MLP) to extract prominent features from the hyperspectral data of Astragalus membranaceus var. mongholicus (AMM) leaves and stems, as well as the surface and deep soil surrounding AMM roots. This methodology allows us to capture the most significant characteristics present in these hyperspectral data with high precision. The AM is subsequently used to assign weights and integrate the hyperspectral features extracted from different parts of the AMM. The MLP is then employed to simultaneously estimate the chlorophyll content (CC) and nitrogen content (NC) of AMM leaves. Compared with estimation networks that utilize only hyperspectral data from AMM leaves as input, our proposed end-to-end AM-MHENet demonstrates superior estimation performance. Specifically, AM-MHENet achieves an R2 of 0.983, an RMSE of 0.73, an MAE of 0.49, and an RPD of 7.63 for the estimation of CC in AMM leaves. For NC estimation, AM-MHENet achieves an R2 value of 0.977, an RMSE of 0.27, an MAE of 0.16, and an RPD of 6.62. These results underscore AM-MHENet’s effectiveness in significantly enhancing the accuracy of both CC and NC estimation in AMM leaves. Moreover, these findings indirectly suggest a strong correlation between the development of AMM leaves and stems, as well as the surface and deep soil surrounding the roots of AMM, and directly highlight the ability of AM to effectively focus on the relevant spectral features within the hyperspectral data. The findings from this study could offer valuable insights into the simultaneous estimation of multiple chemical parameters in plants, thereby making a contribution to the existing body of research in this field.

Spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints

Hyperspectral sparse unmixing, an image processing technique, leverages a spectral library enriched with endmember spectral information as a prerequisite. It decomposes the hyperspectral data to ascertain the abundance corresponding to each endmember in the spectral library. Currently, the majority of sparse unmixing methods are inadequate in high-noise environments due to their failure to comprehensively account for hyperspectral characteristics. Addressing this challenge, this paper introduces an innovative sparse unmixing approach for hyperspectral images named spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints (SWSU-ATVLR). Initially, the sparse unmixing algorithm is introduced in detail. Subsequently, we present the our method. This method seamlessly integrates the low-rank, adaptive TV and spectral weighting characteristics of hyperspectral data. While preserving the low-rank attributes and sparsity of abundance, the adaptively adjusted abundance matrix exhibits a regularized horizontal and vertical difference ratio across various structures and fully utilizes spectral information, enhancing denoising efficiency. Subsequently, the ADMM algorithm is employed to solve the new model. To validate our proposed algorithm, SWSU-ATVLR method is compared and analysed in detailed experiments with several current state-of-the-art methods through simulated and real data experiments. Experimental results prove that our proposed method is superior to these state-of-the-art methods.

Multispectral compatible anti-reconnaissance strategy: Implementing a modulation system integrating visible light-hyperspectral-radar wave

To tackle the challenges of multi-spectral reconnaissance technology, a pioneering anti-reconnaissance device that integrates visible-light, hyperspectral, and microwave radar functionalities has been developed for the first time. By oxidative polymerization method to create a core–shell Fe3O4@polyaniline composite material for the counter electrode. This design optimized impedance matching, achieving wideband radar wave absorption up to 10.5 GHz (reflection loss < -10 dB). The core–shell structure enhanced specific surface area, improving charge exchange and electrochemical performance to meet stringent counter electrode requirements. And, constructed a frequency-selective metal electrode based on metamaterial principles, complementing the radar wave absorption capabilities of the counter electrode material. Additionally, synthesized an asymmetric viologen molecule as the working electrode, mimicking vegetation color transitions from green to yellow while preserving hyperspectral characteristics and radar wave absorption properties. To enhance hyperspectral compatibility without compromising radar wave absorption or color dynamics, we developed a cross-linked polyvinyl alcohol/hydroxyethyl cellulose film based on biomimetic principles. The device can switch between yellow and green under voltages of 0 or −1.2 V, with both states exhibiting a spectral similarity to foliage exceeding 0.95 and an effective bandwidth reaching 13.44 GHz. The integration of these three functionalities allows the device to operate independently without interference.

Mining sensitive hyperspectral feature to non-destructively monitor biomass and nitrogen accumulation status of tea plant throughout the whole year

Rapid and non-destructive estimation of tea plant growth and nitrogen (N) nutrition status using hyperspectral remote sensing is crucial for precise management of tea gardens. This study aimed to mine and fuse sensitive hyperspectral features to achieve an accurate estimation of tea plant growth parameters (biomass and N accumulation) throughout the whole year. An ASD Handheld 2 sensor was used to collect canopy hyperspectral reflectance of tea plants across four periods (Period 1–4) within a year, with tea plant biomass and N accumulation indicators acquired synchronously. The measured spectral reflectance and its first derivative, and wavelet feature were extracted and used to establish quantitative relationships with tea plant growth parameters. Random forest and LASSO algorithms were employed to combine sensitive hyperspectral features and construct the biomass and N accumulation monitoring models. The results showed that wavelet features (R2 = 0.35–0.58) had a stronger correlation with tea plant biomass and N accumulation parameters compared with the measured reflectance or first derivative spectral features. Similarly, the hyperspectral indices (R2 = 0.51–0.69) derived from sensitive wavelet features performed an accurate estimation of tea plant growth parameters. Furthermore, the combination of sensitive hyperspectral indices derived from measured reflectance, first derivative, and wavelet feature using random forest (R2 = 0.67–0.76) and LASSO (R2 = 0.61–0.72) algorithms achieved the greatest accuracy for monitoring tea plant biomass and N accumulation compared with individual hyperspectral feature. Additionally, the above estimation models obtained higher accuracy in period 4 compared to periods 1–3. This study provides valuable remote sensing technical support for predicting biomass and N accumulation status of tea plant throughout the whole year.

Comprehensive analysis of hyperspectral features for monitoring canopy maize leaf spot disease

Accurate quantification of hyperspectral features altered by plant disease infection is pivotal for effective disease management. However, the sensitivity of hyperspectral features to plant disease progression remains elusive, primarily because these features are often influenced by plant growth and environmental factors in addition to the specific disease. This study explores the sensitivity of biophysical and spectral features as indicators for maize adaptation to leaf spot disease. Using high-resolution UAV hyperspectral imaging, we captured maize adaptation dynamics over 30 days post-infection. We evaluated the sensitivity and importance of hyperspectral features for disease monitoring, including biophysical parameters retrieved by the PROSAIL model, and spectral features, including spectral reflectance, vegetation indices (VIs), and wavelet features (WFs). Our findings reveal that WFs first indicate disease response as early as 6 days after infection (DAI), followed by VIs at DAI 8, and variations in chlorophyll content (Cab) become apparent by DAI 10. The Cab, plant senescence reflectance index (PSRI), and normalized photosynthetic reflectance index (NPRI) are determined to be important features at the early stage of the disease. Our experimental results show that the different feature sets are complementary at the early and severe stages of the disease. Our classification models integrating Cab, VIs, and WFs showed higher overall accuracy than models using only spectral features or VIs, with a maximum improvement of 9.36 %. However, these feature sets are redundant in the mild and initial severe disease stages, where models using only spectral features achieve the highest overall accuracy of 86.21 %. This study underscores the novel insights by offering an understanding of plant responses to disease infection and enhancing early detection strategies.

Integrating Hyperspectral Reflectance and Physiological Parameters to Detect Urban Tree Stress: A Study of Drought and Simulated Acid Rain

With urbanization and climate change worsening, urban trees are constantly exposed to environmental stress. To enhance the functionality and health of trees, it is crucial to rapidly and non-destructively detect and respond to tree stress. Research utilizing hyperspectral characteristics for detecting various stresses has recently been actively pursued. This study conducted comparative analysis using various leaf physiological parameters (chlorophyll content, chlorophyll fluorescence, leaf water, and gas exchange status) and hyperspectral data (VIS: visible ray; SWIR: short-wave infrared) to diagnose stress in Prunus yedoensis, commonly grown urban trees, by subjecting them simultaneously to different stresses (drought and simulated acid rain). The findings suggest that hyperspectral reflectance proved more responsive in identifying stress compared to the physiological parameters. Initially, VIS was more effective in detecting two stress responses than SWIR through a classification model (PLS-DA: partial least squares-discriminant analysis). Although SWIR initially faced challenges in simulated acid rain stress detection, spectral preprocessing (SNV: standard normal variate, + S.G 2nd: Savitzky–Golay 2nd derivative) enhanced its stress classification accuracy. Over time, the SWIR bands (1437 nm, 1667 nm, and 1949 nm) exhibited characteristics (such as moisture detection) more closely aligned with stress responses compared to VIS, as determined through PCA (principal component analysis). Hyperspectral reflectance also revealed the potential to measure chlorophyll fluorescence (Fo: minimum fluorescence). Building upon the foundational data of this study, the future potential of diagnosing urban tree stress using portable spectrometers is strong.

A model for inversion of hyperspectral characteristics of phosphate content in mural plaster based on fractional-order differential algorithm

The Dunhuang murals, a significant part of Chinese heritage, have suffered deterioration primarily due to environmental and chemical factors, notably salt damage. This study proposes a sophisticated method that synergizes Fractional Order Differentiation (FOD) and Partial Least Squares Regression (PLSR) to accurately invert the phosphate content in the mural plaster of the Dunhuang paintings. The focal points of the research include: (1) To address the issue of information loss and reduced modeling precision caused by integer-order differentiation algorithms, the FOD method is employed for preprocessing spectral data. This approach ensures the fine spectral differences in the phosphate content of the mural plaster are precisely captured, (2) Utilizing PLSR, the study models the spectral bands identified at a significance level of 0.01 with measured conductivity values, thereby enabling the precise prediction of the phosphate content in the murals. The research outcomes reveal: (1) The FOD method can elucidate the nonlinear characteristics and variation patterns of the mural samples in the spectral data. As the order increases from zero to two, the number of spectral bands meeting the 0.01 significance test initially decreases and then increases. The highest absolute value of the positive correlation coefficient is observed at the 1.9 order, corresponding to the 2077 nm band, (2) For predicting the phosphate content in the murals, the model at the 1.9 order is most suitable for inversion. This model, after cross-validation, achieves a maximum R2 value of 0.861. This study created an efficient FOD-based model for estimating phosphate in mural plaster.

Hyperspectral Characteristics and SPAD Estimation of Wheat Leaves under CO2 Microleakage Stress.

To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model's accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a "red shift" of the green peak and a "blue shift" of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops.

Predicting Grapevine Physiological Parameters Using Hyperspectral Remote Sensing Integrated with Hybrid Convolutional Neural Network and Ensemble Stacked Regression

Grapevine rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhance scion physiology. Direct leaf-level physiological parameters like net assimilation rate, stomatal conductance to water vapor, quantum yield of PSII, and transpiration can illuminate the rootstock effect on scion physiology. However, these measures are time-consuming and limited to leaf-level analysis. This study used different rootstocks to investigate the potential application of aerial hyperspectral imagery in the estimation of canopy level measurements. A statistical framework was developed as an ensemble stacked regression (REGST) that aggregated five different individual machine learning algorithms: Least absolute shrinkage and selection operator (Lasso), Partial least squares regression (PLSR), Ridge regression (RR), Elastic net (ENET), and Principal component regression (PCR) to optimize high-throughput assessment of vine physiology. In addition, a Convolutional Neural Network (CNN) algorithm was integrated into an existing REGST, forming a hybrid CNN-REGST model with the aim of capturing patterns from the hyperspectral signal. Based on the findings, the performance of individual base models exhibited variable prediction accuracies. In most cases, Ridge Regression (RR) demonstrated the lowest test Root Mean Squared Error (RMSE). The ensemble stacked regression model (REGST) outperformed the individual machine learning algorithms with an increase in R2 by (0.03 to 0.1). The performances of CNN-REGST and REGST were similar in estimating the four different traits. Overall, these models were able to explain approximately 55–67% of the variation in the actual ground-truth data. This study suggests that hyperspectral features integrated with powerful AI approaches show great potential in tracing functional traits in grapevines.

Study on the Determination of Flavor Value of Rice Based on Grid Iterative Search Swarm Optimization Support Vector Machine Model and Hyperspectral Imaging.

In the field of rice processing and cultivation, it is crucial to adopt efficient, rapid and user-friendly techniques to detect the flavor values of various rice varieties. The conventional methods for flavor value assessment mainly rely on chemical analysis and technical evaluation, which not only deplete the rice resources but also incur significant time and labor costs. In this study, hyperspectral imaging technology was utilized in combination with an improved Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithm, i.e., the Grid Iterative Search Particle Swarm Optimization Support Vector Machine (GISPSO-SVM) algorithm, introducing a new non-destructive technique to determine the flavor value of rice. The method captures the hyperspectral feature data of different rice varieties through image acquisition, preprocessing and feature extraction, and then uses these features to train a model using an optimized machine learning algorithm. The results show that the introduction of GIS algorithms in a PSO-optimized SVM is very effective and can improve the parameter finding ability. In terms of flavor value prediction accuracy, the Principal Component Analysis (PCA) combined with the GISPSO-SVM algorithm achieved 96% accuracy, which was higher than the 93% of the Competitive Adaptive Weighted Sampling (CARS) algorithm. And the introduction of the GIS algorithm in different feature selection can improve the accuracy to different degrees. This novel approach helps to evaluate the flavor values of new rice varieties non-destructively and provides a new perspective for future rice flavor value detection methods.

Rapid species discrimination of similar insects using hyperspectral imaging and lightweight edge artificial intelligence.

Species discrimination of insects is an important aspect of ecology and biodiversity research. The traditional methods based on human visual experience and biochemical analysis cannot strike a balance between accuracy and timeliness. Morphological identification using computer vision and machine learning is expected to solve this problem, but image features have poor accuracy for very similar species and usually require complicated networks that are unfriendly to portable edge devices. In this work, we propose a fast and accurate species discrimination method of similar insects using hyperspectral features and lightweight machine learning algorithm. Feature regions selection, feature spectra selection and model quantification are used for the optimization of discriminating network. The experimental results of six similar butterfly species in the genus of Graphium show that, compared with morphological recognition with machine vision, our work achieves a higher accuracy of 92.36 ± 3.04% and a shorter inference time of 0.6 ms, with the tiny-size convolutional neural network deployed on a neural network chip. This study provides a rapid and high-accuracy species discrimination method for insects with high appearance similarity and paves the way for field discriminations using intelligent micro-spectrometer based on on-chip microstructure and artificial intelligence chip.

Research on nondestructive detection of sweet-waxy corn seed varieties and mildew based on stacked ensemble learning and hyperspectral feature fusion technology

The variety and quality of corn seeds are crucial factors affecting crop yield and farmers’ economic benefits. This study adopts an innovative method based on a hyperspectral imaging system combined with stacked ensemble learning, aiming to achieve varieties classification and mildew detection of sweet-waxy corn seeds. First, data interference is eliminated by extracting the spectral and texture information of each corn sample and preprocessing the data. Secondly, a stacked ensemble learning model (Stack) was constructed by stacking base models and meta-models. Its results were compared with those of the base models, including Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF).Finally, the overall performance of the model is improved through the information fusion strategy of hyperspectral data and texture information. The research results indicate that the GBDT-Stack model, which integrates spectral and texture data, demonstrated optimal performance in the comprehensive classification of both corn seed varieties and mold detection. On the test set, the model achieved an average prediction accuracy of 97.01%. Specifically, the model achieved a test set accuracy ranging from 94.49% to 97.58% for different corn seed varieties and a test set accuracy of 98.89% for mildew detection. This model not only classifies corn seed varieties but also accurately detects mildew, demonstrating its wide applicability. The method has huge potential and is of great significance for improving crop yield and quality.

SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits

Key messageHyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes.Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.

Research on fertilization decision method for rice tillering stage based on the coupling of UAV hyperspectral remote sensing and WOFOST.

The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current large-scale rice production process. WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice. The research results indicate that the training set R2 and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R2 and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme. This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization.

Imaging Hyperspectral Feature Fusion for Estimation of Rapeseed Pod’s Water Content and Recognition of Pod’s Maturity Level

Imaging hyperspectral technology is becoming popular in agriculture to provide detailed information on crop growth. In this work, we propose an estimation of rapeseed pod’s water content model and identification of maturity levels (green, yellow, and full) model by using this technology. Four types of hyperspectral features are extracted—color, texture, spectral three-edge parameters, and spectral indices. By integrating these features, satisfactory results are achieved: the optimal feature combination is from spectral indices and three-edge parameters, with low RRMSE and RE for yellow maturity. Incorporating spectral indices significantly improved the pod’s water content estimation, reducing RRMSE by up to 43.30% and 30.11% in the green and full maturity stages. Random forest and support vector machine with kernel method (SVM-KM) algorithms outperformed other statistical models, with SVM-KM achieving up to 96.90% accuracy in identifying maturity levels. These findings provide valuable insights for managing rapeseed production during the pod stage.

Hyperspectral remote sensing of forage stoichiometric ratios in the senescent stage of alpine grasslands

Senescence is an important phenological stage in the life cycle of grassland plants, and estimating grass senescence in alpine grasslands is particularly crucial for animal husbandry and grazing the utilization of grassland resources for grazing. However, the application of proximal sensing technologies to capture the variations in forage growth parameters in the senescent stage, especially regarding some key stoichiometric ratios, still faces challenges associated with indiscernible spectral properties and indeterminate absorption and reflectance features. The present study aims to demonstrate the potential and effectiveness of applying hyperspectral data to assess the forage carbon-nitrogen (C:N) ratio and calcium-phosphorus (Ca:P) ratio during the senescent stage (September to November). A progressive variable dimensionality reduction strategy based on a genetic algorithm (GA) and hybrid optimization method (ant optimization colony (ACO) and extreme learning machine (ELM)) coupled with multiple linear and nonlinear statistical methods is applied to develop C:N ratio and Ca:P ratio estimation models via combination with data from 205 sample sites collected from six field campaigns (2016–2019) on the eastern Tibetan Plateau. The results show that the important spectral bands favorable for forage C:N ratio and Ca:P ratio retrieval are generally in the red and shortwave infrared (SWIR) regions, and the proposed model presents satisfactory performance in the estimation of the forage C:N ratio (V-R2 = 0.81, V-RMSE = 6.44) and Ca:P ratio (V-R2 = 0.64, V-RMSE = 2.54) during senescence. Moreover, the model can effectively overcome the spatial differences among sampling areas and perform optimally in capturing the variations in the forage C:N ratio and Ca:P ratio in the early and middle stages of senescence. Overall, our study demonstrates that it is feasible and promising to estimate these stoichiometric ratios using hyperspectral feature bands during grass senescence at the canopy level, with potential applications that may further enhance the monitoring of forage quality and quantity.

Establishing a Hyperspectral Model for the Chlorophyll and Crude Protein Content in Alpine Meadows Using a Backward Feature Elimination Method

(1) Background: The effective selection of hyperspectral feature bands is pivotal in monitoring the nutritional status of intricate alpine grasslands on the Qinghai–Tibet Plateau. The traditional methods often employ hierarchical screening of multiple feature indicators, but their universal applicability suffers due to the use of a consistent methodology across diverse environmental contexts. To remedy this, a backward feature elimination (BFE) selection method has been proposed to assess indicator importance and stability. (2) Methods: As research indicators, the crude protein (CP) and chlorophyll (Chl) contents in degraded grasslands on the Qinghai–Tibet Plateau were selected. The BFE method was integrated with partial least squares regression (PLS), random forest (RF) regression, and tree-based regression (TBR) to develop CP and Chl inversion models. The study delved into the significance and consistency of the forage quality indicator bands. Subsequently, a path analysis framework (PLS-PM) was constructed to analyze the influence of grassland community indicators on SpecChl and SpecCP. (3) Results: The implementation of the BFE method notably enhanced the prediction accuracy, with ΔR2RF-Chl = 56% and ΔR2RF-CP = 57%. Notably, spectral bands at 535 nm and 2091 nm emerged as pivotal for CP prediction, while vegetation indices like the PRI and mNDVI were critical for Chl estimation. The goodness of fit for the PLS-PM stood at 0.70, indicating the positive impact of environmental factors such as grassland cover on SpecChl and SpecCP prediction (rChl = 0.73, rCP = 0.39). SpecChl reflected information pertaining to photosynthetic nitrogen associated with photosynthesis (r = 0.80). (4) Disscusion: Among the applied model methods, the BFE+RF method is excellent in periodically discarding variables with the smallest absolute coefficient values. This variable screening method not only significantly reduces data dimensionality, but also gives the best balance between model accuracy and variables, making it possible to significantly improve model prediction accuracy. In the PLS-PM analysis, it was shown that different coverage and different community structures and functions affect the estimation of SpecCP and SpecChl. In addition, SpecChl has a positive effect on the estimation of SpecCP (r = 0.80), indicating that chlorophyll does reflect photosynthetic nitrogen information related to photosynthesis, but it is still difficult to obtain non-photosynthetic and compound nitrogen information. (5) Conclusions: The application of the BFE + RF method to monitoring the nutritional status of complex alpine grasslands demonstrates feasibility. The BFE filtration process, focusing on importance and stability, bolsters the system’s generalizability, resilience, and versatility. A key research avenue for enhancing the precision of CP monitoring lies in extracting non-photosynthetic nitrogen information.

Multi-Scale Dense Graph Attention Network for Hyperspectral Classification

In recent years, numerous deep learning-based methods have gained increasing attention in hyperspectral classification, particularly the Graph Neural Network, which exhibits superior capabilities in structural description. However, a single graph structure is not suitable for hyperspectral feature representation. Therefore, we propose a novel Multiple-Scale graph network structure, known as the Multi-Scale Dense Graph Attention network for hyperspectral classification. Firstly, semi-supervised local Fisher discriminant analysis and superpixel segmentation were employed for dimensionality reduction and multi-scale graph construction, respectively. Secondly, Spectral-Spatial convolution is applied to extract shallow features from the image. Subsequently, an improved graph self-attention network is sequentially applied to each scale graph, and the different scale graphs are densely connected through spatial feature alignment modules, designed using twice matrix multiplication. Finally, the combined pixel-level feature map from multiple graph spaces is derived, and Spectral-Spatial convolution is employed to fuse the abundant feature maps for hyperspectral classification. Experimental results on various hyperspectral datasets demonstrate the superiority of our MSDesGATnet over many state-of-the-art methods. The code is available at https://github.com/l7170/MSDesGAT.git.

A novel visible and near-infrared hyperspectral imaging platform for automated breast-cancer detection

Background: Breast cancer is a leading cause of cancer-related deaths among women worldwide. Early and accurate detection is crucial for improving patient outcomes. Our study utilizes Visible and Near-Infrared Hyperspectral Imaging (VIS-NIR HSI), a promising non-invasive technique, to detect cancerous regions in ex-vivo breast specimens based on their hyperspectral response.Methods: In this paper, we present a novel HSI platform integrated with fuzzy c-means clustering for automated breast cancer detection. We acquire hyperspectral data from breast tissue samples, and preprocess it to reduce noise and enhance hyperspectral features. Fuzzy c-means clustering is then applied to segment cancerous regions based on their spectral characteristics.Results: Our approach demonstrates promising results. We evaluated the quality of the clustering using metrics like Silhouette Index (SI), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The clustering metrics results revealed an optimal number of 6 clusters for breast tissue classification, and the SI values ranged from 0.68 to 0.72, indicating well-separated clusters. Moreover, the CHI values showed that the clusters were well-defined, and the DBI values demonstrated low cluster dispersion. Additionally, the sensitivity, specificity, and accuracy of our system were evaluated on a dataset of breast tissue samples. We achieved an average sensitivity of 96.83%, specificity of 93.39%, and accuracy of 95.12%. These results indicate the effectiveness of our HSI-based approach in distinguishing cancerous and non-cancerous regions.Conclusions: The paper introduces a robust hyperspectral imaging platform coupled with fuzzy c-means clustering for automated breast cancer detection. The clustering metrics results support the reliability of our approach in effectively segmenting breast tissue samples. In addition, the system shows high sensitivity and specificity, making it a valuable tool for early-stage breast cancer diagnosis. This innovative approach holds great potential for improving breast cancer screening and, thereby, enhancing our understanding of the disease and its detection patterns.

Highlighting Water Stress in Apple Seedlings Using HSI Texture with Machine Learning Technique

Apples are known for their nutrition and economic value. Accurate and rapid diagnosis of water status in apple seedlings on an individual rootstock basis is a prerequisite for precision water management. This study presents a rapid and non-destructive approach for estimating water content in apple seedlings at leaf levels. A PIKA L system collects hyperspectral images (400-1000nm) of apple leaves. Our research extracts spatial information, gray-level co-occurrence matrix (GLCM), from feature wavelength images of hypercubes. Machine learning methods are applied to these spatial feature matrixs to identify apple leaves under different water stresses. In addition, differences in spectral responses were analysed using machine learning techniques for sorting apple seedlings with varying water treatments (dry, normal, and overwatering). Also, we measure chlorophyll to determine the relationship between hyperspectral characteristics and physiological changes. The achievements of the research indicate that the fusion of texture and hyperspectral imaging coupled with machine learning techniques is promising and presents a powerful potential to determine the water stress in the leaves of apple seedlings.