High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quantification of wheat nutrients with VIS-NIR (400-1700 nm) hyperspectral imaging is proposed in this study. Stepwise linear regression (SLR) was used to predict hundreds of nutrients accurately (R2 > 0.6); results improved when the hyperspectral data was processed with the first derivative. Knockout materials were also used to verify their practical application value. Various nutrients\' characteristic wavelengths were mainly concentrated in the visible regions of 400-500 nm and 900-1000 nm. Finally, we proposed an improved pix2pix conditional generative network model to visualize the nutrients distribution and showed better results compared with the original. This research highlights the potential of hyperspectral technology in high-throughput and non-destructive determination and visualization of grain nutrients with deep learning.

BACKGROUND: Gray blight (GB) is a significant disease of tea leaves, posing a severe threat to both the yield and quality. In this study, the process of leaf infection by a pathogenic isolate of the GB disease (DDZ-6) was simulated. Hyperspectral images of normal leaves, infected leaves without symptoms, and infected leaves with mild and moderate symptoms were collected. Combining convolution neural network (CNN), long short-term memory (LSTM), and support vector machine (SVM) algorithms, the early detection model of GB disease, and the rapid screening model of resistant varieties were established. The generality of this method was verified by collecting datasets under field conditions.
RESULTS: The visible red-light band demonstrated a pronounced responsiveness to GB disease, with three sensitive bands identified through rigorous screening processes utilizing uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the successive projections algorithm (SPA). The 693, 727, and 766 nm bands emerged as highly sensitive indicators of GB. Under ideal conditions, the CARS-LSTM model excelled in early detection of GB, achieving an accuracy of 92.6%. However, under field conditions, the combination of 693 and 727 nm bands integrated with a CNN provided the most effective early detection model, attaining an accuracy of 87.8%. For screening tea varieties resistant to GB, the SPA-LSTM model excelled, achieving an accuracy of 82.9%.
CONCLUSIONS: This study provides a core algorithm for a GB disease instrument with detection capabilities, which is of great importance for the early prevention of GB disease in tea plantations. © 2024 Society of Chemical Industry.

Hyperspectral detection of the change rate of organic matter content in agricultural remote sensing requires a high signal-to-noise ratio (SNR). However, due to the large number and efficiency limitation of the components, it is difficult to improve the SNR. This study uses high-efficiency convex grating with a diffraction efficiency exceeding 50% across the 360-850 nm range, a back-illuminated Complementary Metal Oxide Semiconductor (CMOS) detector with a 95% efficiency in peak wavelength, and silver-coated mirrors to develop an imaging spectrometer for detecting soil organic matter (SOM). The designed system meets the spectral resolution of 10 nm in the 360-850 nm range and achieves a swath of 100 km and a spatial resolution of 100 m at an orbital height of 648.2 km. This study also uses the basic structure of Offner with fewer components in the design and sets the mirrors of the Offner structure to have the same sphere, which can achieve the rapid adjustment of the co-standard. This study performs a theoretical analysis of the developed Offner imaging spectrometer based on the classical Rowland circular structure, with a 21.8 mm slit length; simulates its capacity for suppressing the +2nd-order diffraction stray light with the filter; and analyzes the imaging quality after meeting the tolerance requirements, which is combined with the surface shape characteristics of the high-efficiency grating. After this test, the grating has a diffraction efficiency above 50%, and the silver-coated mirrors have a reflection value above 95% on average. Finally, the laboratory tests show that the SNR over the waveband exceeds 300 and reaches 800 at 550 nm, which is higher than some current instruments in orbit for soil observation. The proposed imaging spectrometer has a spectral resolution of 10 nm, and its modulation transfer function (MTF) is greater than 0.23 at the Nyquist frequency, making it suitable for remote sensing observation of SOM change rate. The manufacture of such a high-efficiency broadband grating and the development of the proposed instrument with high energy transmission efficiency can provide a feasible technical solution for observing faint targets with a high SNR.

This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLMER-ResNet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial-spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3%) and a Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and treatment adjustment, by providing physicians with insights into the underlying pathological organization of gliomas.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

The biodiversity of grasslands is important for ecosystem function and health. The protection and mana-gement of grassland biodiversity requires the collection of the information on plant diversity. Hyperspectral remote sensing, with its unique advantages of extensive coverage and high spectral resolution, offers a new solution for long-term monitoring of plant diversity. We first reviewed the development history of hyperspectral remote sensing technology, emphasized its advantages in monitoring grassland plant diversity, and further analyzed its specific applications in this field. Finally, we discussed the challenges faced by hyperspectral remote sensing technology in its applications, such as the complexity of data processing, accuracy of algorithms, and integration with ground-based remote sensing data, and proposes prospects for future research directions. With the advancement of remote sensing technology and the integrated application of multi-source data, hyperspectral remote sensing would play an increasingly important role in grassland ecological monitoring and biodiversity conservation, which could provide scientific basis and technical support for global ecological protection and sustainable development.
草原的生物多样性具有多种生态功能,而草原生物多样性的保护与管理工作需要收集草原的植物多样性信息。高光谱遥感以其独特的大范围覆盖和高光谱分辨率优势,为草原植物多样性的长期监测提供了新的解决方案。本文首先回顾了高光谱遥感技术的发展历程,强调了高光谱数据在草原植物多样性监测中的独特优势,并进一步分析了其在草原植物多样性监测中的具体应用。最后,讨论了高光谱遥感技术在当前应用中面临的挑战,如数据处理复杂性、算法精度,以及与地面遥感数据的整合问题,并对未来研究方向提出展望。随着遥感技术的不断进步和多源数据的综合应用,高光谱遥感将在草原生态监测与生物多样性保护方面发挥更加重要的作用,为全球生态保护和可持续发展提供科学依据和技术支持。.

Rapid acquisition of the data of soil moisture content (SMC) and soil organic matter (SOM) content is crucial for the improvement and utilization of saline alkali farmland soil. Based on field measurements of hyperspectral reflectance and soil properties of farmland soil in the Hetao Plain, we used a competitive adaptive reweighted sampling algorithm (CARS) to screen sensitive bands after transforming the original spectral reflectance (Ref) into a standard normal variable (SNV). Strategies Ⅰ, Ⅱ, and Ⅲ were used to model the input variables of Ref, Ref SNV, Ref-SNV+ soil covariate (SC), and digital elevation model (DEM). We constructed SMC and SOM estimation models based on random forest (RF) and light gradient boosting machine (LightGBM), and then verified and compared the accuracy of the models. The results showed that after CARS screening, the sensitive bands of SMC and SOM were compressed to below 3.3% of the entire band, which effectively optimized band selection and reduced redundant spectral information. Compared with the LightGBM model, the RF model had higher accuracy in SMC and SOM estimation, and the input variable strategy Ⅲ was better than Ⅱ and Ⅰ. The introduction of auxiliary variables effectively improved the estimation ability of the model. Based on comprehensive analysis, the coefficient of determination (Rp2), root mean square error (RMSE), and relative analysis error (RPD) of the SMC estimation model validation based on strategy Ⅲ-RF were 0.63, 3.16, and 2.01, respectively. The SOM estimation models based on strategy Ⅲ-RF had Rp2, RMSE, and RPD of 0.93, 1.15, and 3.52, respectively. The strategy Ⅲ-RF model was an effective method for estimating SMC and SOM. Our results could provide a new method for the rapid estimation of soil moisture and organic matter content in saline alkali farmland.
快速获取土壤含水率(SMC)和土壤有机质(SOM)含量对于盐碱农田土壤的改良利用至关重要。本研究基于河套平原农田土壤野外高光谱反射率和土壤属性实测数据,对原始光谱反射率(Ref)进行标准正态变量(SNV)转换后,采用竞争性自适应重加权采样算法(CARS)筛选敏感波段,然后分别以Ref、Ref-SNV和Ref-SNV+土壤协变量(SC)及数字高程模型(DEM)作为建模输入变量的策略Ⅰ、Ⅱ和Ⅲ,基于随机森林(RF)和轻梯度提升机(LightGBM)建立SMC和SOM估算模型,并对模型精度进行验证和对比。结果表明:经CARS筛选后,SMC和SOM敏感波段压缩至全波段的3.3%以下,有效优化波段选择,减少了冗余光谱信息。与LightGBM 模型相比,RF模型在SMC和SOM估算中精度更高,输入变量策略Ⅲ优于Ⅱ和Ⅰ,辅助变量的引入有效提升了模型的估算能力。综合分析,基于策略Ⅲ-RF的SMC估算模型验证决定系数(Rp2)、均方根误差(RMSE)和相对分析误差(RPD)分别为0.63、3.16和2.01,基于策略Ⅲ-RF的SOM估算模型Rp2、RMSE和RPD分别为0.93、1.15和3.52,策略Ⅲ-RF模型是估算土壤水分和土壤有机质的有效方法。研究结论可为盐碱农田土壤水分和有机质含量快速估算提供新方法。.

In recent years, increasing demand for inland river water quality precision management has heightened the necessity for real-time, rapid, and continuous monitoring of water conditions. By analyzing the optical properties of water bodies remotely, unmanned aerial vehicle (UAV) hyperspectral imaging technology can assess water quality without direct contact, presenting a novel method for monitoring river conditions. However, there are currently some challenges to this technology that limit the promotion application of this technology, such as underdeveloped sensor calibration, atmospheric correction algorithms, and limitations in modeling non-water color parameters. This article evaluates the advantages and disadvantages of traditional sensor calibration methods and considers factors like sensor aging and adverse weather conditions that impact calibration accuracy. It suggests that future improvements should target hardware enhancements, refining models, and mitigating external interferences to ensure precise spectral data acquisition. Furthermore, the article summarizes the limitations of various traditional atmospheric correction methods, such as complex computational requirements and the need for multiple atmospheric parameters. It discusses the evolving trends in this technology and proposes streamlining atmospheric correction processes by simplifying input parameters and establishing adaptable correction algorithms. Simplifying these processes could significantly enhance the accuracy and feasibility of atmospheric correction. To address issues with the transferability of water quality inversion models regarding non-water color parameters and varying hydrological conditions, the article recommends exploring the physical relationships between spectral irradiance, solar zenith angle, and interactions with water constituents. By understanding these relationships, more accurate and transferable inversion models can be developed, improving the overall effectiveness of water quality assessment. By leveraging the sensitivity and versatility of hyperspectral sensors and integrating interdisciplinary approaches, a comprehensive database for water quality assessment can be established. This database enables rapid, real-time monitoring of non-water color parameters which offers valuable insights for the precision management of inland river water quality.

Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the \"trilateral\" parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0-2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the \"trilateral\" parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0-2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.

Face recognition has been well studied under visible light and infrared (IR) in both intra-spectral and cross-spectral cases. However, how to fuse different light bands for face recognition, i.e., hyperspectral face recognition, is still an open research problem, which has the advantages of richer information retention and all-weather functionality over single-band face recognition. Thus, in this research, we revisit the hyperspectral recognition problem and provide a deep learning-based approach. A new fusion model (named HyperFace) is proposed to address this problem. The proposed model features a pre-fusion scheme, a Siamese encoder with bi-scope residual dense learning, a feedback-style decoder, and a recognition-oriented composite loss function. Experiments demonstrate that our method yields a much higher recognition rate than face recognition using only visible light or IR data. Moreover, our fusion model is shown to be superior to other general-purpose image fusion methods that are either traditional or deep learning-based, including state-of-the-art methods, in terms of both image quality and recognition performance.