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Journal articles on the topic 'Fluorescence hyperspectral imaging (fHSI)'

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1

Luthman, Anna Siri, Sebastian Dumitru, Isabel Quiros-Gonzalez, James Joseph, and Sarah E. Bohndiek. "Fluorescence hyperspectral imaging (fHSI) using a spectrally resolved detector array." Journal of Biophotonics 10, no. 6-7 (2017): 840–53. http://dx.doi.org/10.1002/jbio.201600304.

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2

Wang, Chengzhi, Xiaping Fu, Ying Zhou, and Feng Fu. "Deoxynivalenol Detection beyond the Limit in Wheat Flour Based on the Fluorescence Hyperspectral Imaging Technique." Foods 13, no. 6 (2024): 897. http://dx.doi.org/10.3390/foods13060897.

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Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. Wheat flour samples were prepared with varying DON concentrations through the addition of trace amounts of DON using the wet mixing method for fluorescence hyperspectral image collection. SG smoothing and normalization algorithms were applied for original spectra preprocessing. Feature band selection was carried out by applying the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the random frog algorithm on the fluorescence spectrum. Random forest (RF) and support vector machine (SVM) classification models were utilized to identify wheat flour samples with DON concentrations higher than 1 mg/kg. The results indicate that the SG–CARS–RF and SG–CARS–SVM models showed better performance than other models, achieving the highest recall rate of 98.95% and the highest accuracy of 97.78%, respectively. Additionally, the ROC curves demonstrated higher robustness on the RF algorithm. Deep learning algorithms were also applied to identify the samples that exceeded safety standards, and the convolutional neural network (CNN) model achieved a recognition accuracy rate of 97.78% for the test set. In conclusion, this study demonstrates the feasibility and potential of the FHSI technique in detecting DON infection in wheat flour.
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Zhan, Chunyi, Hongyi Mao, Rongsheng Fan, et al. "Detection of Apple Sucrose Concentration Based on Fluorescence Hyperspectral Image System and Machine Learning." Foods 13, no. 22 (2024): 3547. http://dx.doi.org/10.3390/foods13223547.

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China ranks first in apple production worldwide, making the assessment of apple quality a critical factor in agriculture. Sucrose concentration (SC) is a key factor influencing the flavor and ripeness of apples, serving as an important quality indicator. Nondestructive SC detection has significant practical value. Currently, SC is mainly measured using handheld refractometers, hydrometers, electronic tongues, and saccharimeter analyses, which are not only time-consuming and labor-intensive but also destructive to the sample. Therefore, a rapid nondestructive method is essential. The fluorescence hyperspectral imaging system (FHIS) is a tool for nondestructive detection. Upon excitation by the fluorescent light source, apples displayed distinct fluorescence characteristics within the 440–530 nm and 680–780 nm wavelength ranges, enabling the FHIS to detect SC. This study used FHIS combined with machine learning (ML) to predict SC at the apple’s equatorial position. Primary features were extracted using variable importance projection (VIP), the successive projection algorithm (SPA), and extreme gradient boosting (XGBoost). Secondary feature extraction was also conducted. Models like gradient boosting decision tree (GBDT), random forest (RF), and LightGBM were used to predict SC. VN-SPA + VIP-LightGBM achieved the highest accuracy, with Rp2, RMSEp, and RPD reaching 0.9074, 0.4656, and 3.2877, respectively. These results underscore the efficacy of FHIS in predicting apple SC, highlighting its potential for application in nondestructive quality assessment within the agricultural sector.
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Hu, Yan, Youli Wu, Jie Sun, Jinping Geng, Rongsheng Fan, and Zhiliang Kang. "Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics." Foods 11, no. 15 (2022): 2344. http://dx.doi.org/10.3390/foods11152344.

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Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS–DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS–DA. MSC_VIP_PLS–DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification.
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5

Beule, Pieter De, Dylan M. Owen, Hugh B. Manning, et al. "Rapid hyperspectral fluorescence lifetime imaging." Microscopy Research and Technique 70, no. 5 (2007): 481–84. http://dx.doi.org/10.1002/jemt.20434.

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6

Nie, Zhaojun, Ran An, Joseph E. Hayward, Thomas J. Farrell, and Qiyin Fang. "Hyperspectral fluorescence lifetime imaging for optical biopsy." Journal of Biomedical Optics 18, no. 9 (2013): 096001. http://dx.doi.org/10.1117/1.jbo.18.9.096001.

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7

Juntunen, Cory, Isabel M. Woller, and Yongjin Sung. "Hyperspectral Three-Dimensional Fluorescence Imaging Using Snapshot Optical Tomography." Sensors 21, no. 11 (2021): 3652. http://dx.doi.org/10.3390/s21113652.

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Hyperspectral three-dimensional (3D) imaging can provide both 3D structural and functional information of a specimen. The imaging throughput is typically very low due to the requirement of scanning mechanisms for different depths and wavelengths. Here we demonstrate hyperspectral 3D imaging using Snapshot projection optical tomography (SPOT) and Fourier-transform spectroscopy (FTS). SPOT allows us to instantaneously acquire the projection images corresponding to different viewing angles, while FTS allows us to perform hyperspectral imaging at high spectral resolution. Using fluorescent beads and sunflower pollens, we demonstrate the imaging performance of the developed system.
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8

Safonov, А. I., K. V. Nikolaev, and S. N. Yakunin. "Hyperspectral X-ray imaging for nanometrology." Kristallografiâ 69, no. 4 (2024): 730–42. http://dx.doi.org/10.31857/s0023476124040207.

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A tool for X-ray hyperspectral imaging has been developed. It is based on a conventional CCD driven by an algorithm that allows resolution in both energy and position. A new algorithm has been developed that allows the real-time analysis of single photon events. The factors influencing the energy resolution, the formation of artifacts in the energy spectra, and the counting efficiency are analyzed. Furthermore, a method for achieving sub-pixel precision using the singular value decomposition is suggested. The algorithm has been tested on synthetic data and in a live experiment with the registration of X-ray fluorescence emission from a thin film structure. Applying hyperspectral imaging to grazing emission X-ray fluorescence opens up new possibilities in nanometrology.
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9

Di Benedetto, Alessia, Luìs Manuel de Almieda Nieto, Alessia Candeo, Gianluca Valentini, Daniela Comelli, and Matthias Alfeld. "Multivariate analysis on fused hyperspectral datasets within Cultural Heritage field." EPJ Web of Conferences 309 (2024): 14007. http://dx.doi.org/10.1051/epjconf/202430914007.

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This work introduces a novel method to multivariate analysis applied to fused hyperspectral datasets in the field of Cultural Heritage (CH). Hyperspectral Imaging is a well-established approach for the non-invasive examination of artworks, offering insights into their composition and conservation status. In CH field, a combination of hyperspectral techniques is usually employed to reach a comprehensive understanding of the artwork. To deal with hyperspectral data, multivariate statistical methods are essential due to the complexity of the data. The process involves factorizing the data matrix to highlight components and reduce dimensionality, with techniques such as Non-negative Matrix Factorization (NMF) gaining prominence. To maximize the synergies between multimodal datasets, the fusion of hyperspectral datasets can be coupled with multivariate analysis, with potential applications in CH. In this work, I will show examples of this approach with different combinations of datasets, including reflectance and transmittance spectral imaging, Fluorescence Lifetime Imaging and Time-Gated Hyperspectral Imaging, and Raman and fluorescence spectroscopy micro-mapping.
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10

Kong, Seong G., Matthew E. Martin, and Tuan Vo-Dinh. "Hyperspectral Fluorescence Imaging for Mouse Skin Tumor Detection." ETRI Journal 28, no. 6 (2006): 770–76. http://dx.doi.org/10.4218/etrij.06.0106.0061.

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11

Deng, Fengyuan, Changqin Ding, Jerald C. Martin, et al. "Spatial-spectral multiplexing for hyperspectral multiphoton fluorescence imaging." Optics Express 25, no. 26 (2017): 32243. http://dx.doi.org/10.1364/oe.25.032243.

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12

Wang, Youmin, Sheldon Bish, James W. Tunnell, and Xiaojing Zhang. "MEMS scanner based handheld fluorescence hyperspectral imaging system." Sensors and Actuators A: Physical 188 (December 2012): 450–55. http://dx.doi.org/10.1016/j.sna.2011.12.009.

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13

Studer, V., J. Bobin, M. Chahid, H. S. Mousavi, E. Candes, and M. Dahan. "Compressive fluorescence microscopy for biological and hyperspectral imaging." Proceedings of the National Academy of Sciences 109, no. 26 (2012): E1679—E1687. http://dx.doi.org/10.1073/pnas.1119511109.

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14

Zavattini, Guido, Stefania Vecchi, Gregory Mitchell, et al. "A hyperspectral fluorescence system for 3Din vivooptical imaging." Physics in Medicine and Biology 51, no. 8 (2006): 2029–43. http://dx.doi.org/10.1088/0031-9155/51/8/005.

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15

Kotwal, Alankar, Alfredo Sandoval, Alvin LeBlanc, Vishwanath Saragadam, Ashok Veeraraghavan, and Pablo Andres Valdes Quevedo. "1152 Lightfield Snapshot Hyperspectral Imaging for Quantitative Fluorescence-Guided Neurosurgery." Neurosurgery 71, Supplement_1 (2025): 180. https://doi.org/10.1227/neu.0000000000003360_1152.

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INTRODUCTION: Fluorescence-guided surgery with protoporphyrin (PpIX) has shown improved rates of gross total resection. However, visual fluorescence assessments have diagnostic accuracies as low as <70% for detecting residual tumor due light attenuation and scattering. Hyperspectral imaging (HSI) techniques have been developed to measure absolute PpIX concentrations. However, these methods are slow (seconds to minutes/image) due to existing HSI spectral- or spatial-scanning technologies. METHODS: We built a custom HSI fluorescence system for visible to near infrared imaging. We validated an empirical model for correcting tissue optical properties on the measured fluorescence to estimate tissue PpIX concentrations, requiring both reflectance and fluorescence measurements. RESULTS: We used our novel HSI system to image fluorescence and reflectance of tissue mimicking phantoms of known absorption, scattering, and PpIX concentrations . Our empirical correction model showed high linearity (R2 = 0.95) to correct for optical properties unlike the raw fluorescence with known PpIX concentrations . We then validated our HSI system in a rat glioma model to accurately measure PpIX concentrations, where tumor areas that are invisible with conventional raw fluorescence are visualized with our HSI system with estimated absolute concentrations. CONCLUSIONS: Here we demonstrate a novel HSI system for real time quantitative fluorescence image-guided surgery. We optimized and validated our system in pre-clinical phantom and rat glioma studies demonstrating its ability to quantify PpIX and detect PpIX concentrations otherwise ‘invisible’ to conventional fluorescence, which provide support for translating our results to a clinical system.
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16

Pian, Qi, Ruoyang Yao, Nattawut Sinsuebphon, and Xavier Intes. "Compressive hyperspectral time-resolved wide-field fluorescence lifetime imaging." Nature Photonics 11, no. 7 (2017): 411–14. http://dx.doi.org/10.1038/nphoton.2017.82.

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17

Leavesley, Silas J., Mikayla Walters, Carmen Lopez, et al. "Hyperspectral imaging fluorescence excitation scanning for colon cancer detection." Journal of Biomedical Optics 21, no. 10 (2016): 104003. http://dx.doi.org/10.1117/1.jbo.21.10.104003.

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18

Mustafic, Adnan, Yu Jiang, and Changying Li. "Cotton contamination detection and classification using hyperspectral fluorescence imaging." Textile Research Journal 86, no. 15 (2016): 1574–84. http://dx.doi.org/10.1177/0040517515590416.

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19

Kim, Min-Jee, Jongguk Lim, Sung Won Kwon, et al. "Geographical Origin Discrimination of White Rice Based on Image Pixel Size Using Hyperspectral Fluorescence Imaging Analysis." Applied Sciences 10, no. 17 (2020): 5794. http://dx.doi.org/10.3390/app10175794.

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Geographical origin discrimination of white rice is an important endeavor in preventing illegal distribution of white rice and regulating and standardizing food safety and quality assurance. The aim of this study was to develop a method for geographical origin discrimination between South Korean and Chinese rice using a hyperspectral fluorescence imaging technique and multivariate analysis. Hyperspectral fluorescence images of South Korean and Chinese rice samples were obtained in the wavelength range of 420 nm to 780 nm with intervals of 4.8 nm using 365 nm wavelength ultraviolet-A excitation light. Partial least squares discriminant analysis models were developed and applied to the acquired image to determine the geographical origins of the rice samples. In addition, various pre-processing techniques were applied to improve the discrimination accuracy. Accordingly, the pixel size of the hyperspectral image was determined. The results revealed that the optimum pixel size of the hyperspectral image that was above 7 mm × 7 mm showed a high discrimination accuracy. Moreover, the geographical origin discrimination model that applied the first-order derivative achieved a high discrimination accuracy of 98.89%. The results of this study showed that hyperspectral fluorescence imaging technology can be used to quickly and accurately discriminate the geographical origins of white rice.
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Lee, Ahyeong, Saetbyeol Park, Jinyoung Yoo, et al. "Detecting Bacterial Biofilms Using Fluorescence Hyperspectral Imaging and Various Discriminant Analyses." Sensors 21, no. 6 (2021): 2213. http://dx.doi.org/10.3390/s21062213.

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Biofilms formed on the surface of agro-food processing facilities can cause food poisoning by providing an environment in which bacteria can be cultured. Therefore, hygiene management through initial detection is important. This study aimed to assess the feasibility of detecting Escherichia coli (E. coli) and Salmonella typhimurium (S. typhimurium) on the surface of food processing facilities by using fluorescence hyperspectral imaging. E. coli and S. typhimurium were cultured on high-density polyethylene and stainless steel coupons, which are the main materials used in food processing facilities. We obtained fluorescence hyperspectral images for the range of 420–730 nm by emitting UV light from a 365 nm UV light source. The images were used to perform discriminant analyses (linear discriminant analysis, k-nearest neighbor analysis, and partial-least squares discriminant analysis) to identify and classify coupons on which bacteria could be cultured. The discriminant performances of specificity and sensitivity for E. coli (1–4 log CFU·cm−2) and S. typhimurium (1–6 log CFU·cm−2) were over 90% for most machine learning models used, and the highest performances were generally obtained from the k-nearest neighbor (k-NN) model. The application of the learning model to the hyperspectral image confirmed that the biofilm detection was well performed. This result indicates the possibility of rapidly inspecting biofilms using fluorescence hyperspectral images.
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Jiang, Wentao, Jingwei Li, Xinli Yao, Erik Forsberg, and Sailing He. "Fluorescence Hyperspectral Imaging of Oil Samples and Its Quantitative Applications in Component Analysis and Thickness Estimation." Sensors 18, no. 12 (2018): 4415. http://dx.doi.org/10.3390/s18124415.

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The fast response and analysis of oil spill accidents is important but remains challenging. Here, a compact fluorescence hyperspectral system based on a grating-prism structure able to perform component analysis of oil as well as make a quantitative estimation of oil film thickness is developed. The spectrometer spectral range is 366–814 nm with a spectral resolution of 1 nm. The feasibility of the spectrometer system is demonstrated by determining the composition of three types of crude oil and various mixtures of them. The relationship between the oil film thickness and the fluorescent hyperspectral intensity is furthermore investigated and found to be linear, which demonstrates the feasibility of using the fluorescence data to quantitatively measure oil film thickness. Capable of oil identification, distribution analysis, and oil film thickness detection, the fluorescence hyperspectral imaging system presented is promising for use during oil spill accidents by mounting it on, e.g., an unmanned aerial vehicle.
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22

Gao, Jiayu, Xuhui Yang, Simo Liu, Yufeng Liu, and Xiaofeng Ning. "Detection of Tomato Leaf Pesticide Residues Based on Fluorescence Spectrum and Hyper-Spectrum." Horticulturae 11, no. 2 (2025): 121. https://doi.org/10.3390/horticulturae11020121.

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In order to rapidly and nondestructively detect pesticide residues on tomato leaves, fluorescence spectroscopy and hyperspectral techniques were used to study the nondestructive detection of three different concentrations of benzyl-pyrazolyl esters on the surface of tomato leaves, respectively. In this study, fluorescence spectrum acquisition and hyperspectral imaging processing of tomato leaf samples with and without pesticides were conducted, and spectral data from regions of interest of hyperspectral images were extracted. The data in the spectral raw bands were optimized using convolutional smoothing (S-G), standard normal variable transformation (SNV), multiplicative scatter correction (MSC), and baseline calibration (baseline) algorithms, respectively. In order to improve the operating rate of discrimination, a continuous projection algorithm (SPA) was used to extract the characteristic wavelengths of the fluorescence spectra and hyperspectral data of pesticide residues, and algorithms such as the least-squares support vector machine (LSSVM) algorithm and least partial squares regression (PLSR) were used to build a quantitative model, while algorithms such as the convolutional neural network (BPNN) algorithm and decision tree algorithm (CART) were used to build a qualitative model. According to the results, R2 of the model of hyperspectral data after SG-SNV preprocessing and PLSR modeling reached 0.9974, RMSEC reached 0.0221, and RMSEP reached 0.0565. R2 of the model of fluorescence spectral data after SG-MSC preprocessing and SVM modeling reached 0.9986, RMSEC reached 0.2496, and RMSEP reached 0.4193. Qualitative analysis was established based on the characteristic wavelengths of hyper-spectrum and fluorescence spectrum extracted by the SPA algorithm, and the accuracy of the training sets of the optimal qualitative model reached 94.9% and 95.7%, respectively, and the accuracy of the test sets both reached 100%. After comparison, the quantitative model of data based on fluorescence spectrum for pesticide residue detection in tomato leaves proved to have a better effect, and the qualitative model showed higher accuracy in discrimination. Therefore, the fluorescence spectral and hyperspectral imaging techniques applied to tomato leaf pesticide detection enjoy a promising application prospect.
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23

Wirth, Dennis, Brook Byrd, Boyu Meng, Rendall R. Strawbridge, Kimberley S. Samkoe, and Scott C. Davis. "Hyperspectral imaging and spectral unmixing for improving whole-body fluorescence cryo-imaging." Biomedical Optics Express 12, no. 1 (2020): 395. http://dx.doi.org/10.1364/boe.410810.

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24

Wang, Jiaxian, Li He, Zhengwei He, Yun Hou, Chaowei Wang, and Chengjiang Zhang. "Regularities of element migration based on rock spectral features: a case study of the Liwu copper deposit." Analytical Methods 13, no. 14 (2021): 1720–30. http://dx.doi.org/10.1039/d1ay00004g.

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25

Gruber, Florian, Philipp Wollmann, Wulf Grählert, and Stefan Kaskel. "Hyperspectral Imaging Using Laser Excitation for Fast Raman and Fluorescence Hyperspectral Imaging for Sorting and Quality Control Applications." Journal of Imaging 4, no. 10 (2018): 110. http://dx.doi.org/10.3390/jimaging4100110.

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A hyperspectral measurement system for the fast and large area measurement of Raman and fluorescence signals was developed, characterized and tested. This laser hyperspectral imaging system (Laser-HSI) can be used for sorting tasks and for continuous quality monitoring. The system uses a 532 nm Nd:YAG laser and a standard pushbroom HSI camera. Depending on the lens selected, it is possible to cover large areas (e.g., field of view (FOV) = 386 mm) or to achieve high spatial resolutions (e.g., 0.02 mm). The developed Laser-HSI was used for four exemplary experiments: (a) the measurement and classification of a mixture of sulphur and naphthalene; (b) the measurement of carotenoid distribution in a carrot slice; (c) the classification of black polymer particles; and, (d) the localization of impurities on a lead zirconate titanate (PZT) piezoelectric actuator. It could be shown that the measurement data obtained were in good agreement with reference measurements taken with a high-resolution Raman microscope. Furthermore, the suitability of the measurements for classification using machine learning algorithms was also demonstrated. The developed Laser-HSI could be used in the future for complex quality control or sorting tasks where conventional HSI systems fail.
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Hornberger, Christoph, Bert H. Herrmann, Georg Daeschlein, et al. "Detecting Bacteria on Wounds with Hyperspectral Imaging in Fluorescence Mode." Current Directions in Biomedical Engineering 6, no. 3 (2020): 264–67. http://dx.doi.org/10.1515/cdbme-2020-3067.

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AbstractChronic non-healing wounds represent an increasing problem. In order to enable physicians and nurses to make evidence based decisions on wound treatment, the professional societies call for supporting tools to be offered to physicians. Oxygen supply, bacteria colonization and other parameters influence the healing process. So far, these parameters cannot be monitored in an objective and routinely manner. Existing methods like the microbiological analysis of wound swabs, mean a great deal of effort and partly a long delay. In this paper 42 fluorescence images from 42 patients with diabetic foot ulcer, recorded with a hyperspectral imaging system (TIVITA®), converted for fluorescence imaging, were analysed. Beside the fluorescence images, information about the bacterial colonization is available from microbiological analysis of wound swabs. After preprocessing, principal component analysis, PCA, is used for data analysis with a 405 nm excitation wavelength, the emission wavelength range 510 - 745 nm is used for analysis. After dividing the data into a training and a test dataset it could be shown, that bacteria are detectable in the wound area. A quantification in bacterial colonization counts (BCC) was not in the focus of the research in this study stage.
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Mo, Changyeun, Moon S. Kim, Giyoung Kim, Eun Ju Cheong, Jinyoung Yang, and Jongguk Lim. "Detecting Drought Stress in Soybean Plants Using Hyperspectral Fluorescence Imaging." Journal of Biosystems Engineering 40, no. 4 (2015): 335–44. http://dx.doi.org/10.5307/jbe.2015.40.4.335.

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28

Noh, Hyun Kwon, and Renfu Lu. "Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality." Postharvest Biology and Technology 43, no. 2 (2007): 193–201. http://dx.doi.org/10.1016/j.postharvbio.2006.09.006.

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29

Haaland, David M., Howland D. T. Jones, Mark H. Van Benthem, et al. "Hyperspectral Confocal Fluorescence Imaging: Exploring Alternative Multivariate Curve Resolution Approaches." Applied Spectroscopy 63, no. 3 (2009): 271–79. http://dx.doi.org/10.1366/000370209787598843.

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30

St-Georges-Robillard, Amélie, Mathieu Masse, Maxime Cahuzac, et al. "Fluorescence hyperspectral imaging for live monitoring of multiple spheroids in microfluidic chips." Analyst 143, no. 16 (2018): 3829–40. http://dx.doi.org/10.1039/c8an00536b.

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31

Gouisset, E., G. Rioland, F. Bourcier, D. Faye, P. Walter, and F. Infante. "Detection and characterization of contamination with fluorescence spectroscopy." IOP Conference Series: Materials Science and Engineering 1287, no. 1 (2023): 012025. http://dx.doi.org/10.1088/1757-899x/1287/1/012025.

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Abstract In the field of failure analysis and in particular molecular and particulate contamination, being able to detect any trace of contaminants during the integration of an orbital spacecraft is crucial. In this context, fluorescence allows not only to detect but also to discriminate contaminants. We studied the fluorescence response of two epoxy adhesives, typical sources of spacecraft contamination in orbit with a portable broadband hyperspectral instrument (UV-Vis-NIR) developed in collaboration with the CNES and Intraspec Technologies, but also with a commercial spectrofluorometer. These measurements had two objectives, evaluate the performance of our hyperspectral instrument in order to identify prospect of improvement, but as well study the pertinence of fluorescence signature study in the contamination field. The first goal brings out that the hyperspectral instrument is capable of imaging the scene and allows us to extract fluorescence spectra from the image, but it still needs development, especially in term of sensitivity in UV range. The second goal shows promising results. Fluorescence studies with the spectrofluorometer emphasize that fluorescence spectra are specific to the chemical nature of the contaminant, which allows us to clearly discriminate them.
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32

Lefcourt, Alan, Ross Kistler, S. Gadsden, and Moon Kim. "Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities." Applied Sciences 7, no. 1 (2016): 3. http://dx.doi.org/10.3390/app7010003.

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Kim, M. S., Yud-Ren Chen, Byoung-Kwan Cho, A. M. Lefcourt, Kuanglin Chao, and Chun-Chieh Yang. "ONLINE HYPERSPECTRAL LINE-SCAN FLUORESCENCE IMAGING FOR SAFETY INSPECTION OF APPLES." Acta Horticulturae, no. 768 (May 2008): 385–90. http://dx.doi.org/10.17660/actahortic.2008.768.50.

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I. Kim, M. S. Kim, Y. R. Chen, and S. G. Kong. "DETECTION OF SKIN TUMORS ON CHICKEN CARCASSES USING HYPERSPECTRAL FLUORESCENCE IMAGING." Transactions of the ASAE 47, no. 5 (2004): 1785–92. http://dx.doi.org/10.13031/2013.17595.

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35

Liu, Zhiyi, Suihua Ma, Yanhong Ji, Le Liu, Jihua Guo, and Yonghong He. "Parallel scan hyperspectral fluorescence imaging system and biomedical application for microarrays." Journal of Physics: Conference Series 277 (January 1, 2011): 012023. http://dx.doi.org/10.1088/1742-6596/277/1/012023.

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36

Dwight, Jason G., and Tomasz S. Tkaczyk. "Lenslet array tunable snapshot imaging spectrometer (LATIS) for hyperspectral fluorescence microscopy." Biomedical Optics Express 8, no. 3 (2017): 1950. http://dx.doi.org/10.1364/boe.8.001950.

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37

H. K. Noh, Y. Peng, and R. Lu. "Integration of Hyperspectral Reflectance and Fluorescence Imaging for Assessing Apple Maturity." Transactions of the ASABE 50, no. 3 (2007): 963–71. http://dx.doi.org/10.13031/2013.23119.

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Abdel Gawad, Ahmed L., Yasser El-Sharkawy, H. S. Ayoub, Ashraf F. El-Sherif, and Mahmoud F. Hassan. "Classification of dental diseases using hyperspectral imaging and laser induced fluorescence." Photodiagnosis and Photodynamic Therapy 25 (March 2019): 128–35. http://dx.doi.org/10.1016/j.pdpdt.2018.11.017.

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Davis, Ryan W., Howland D. T. Jones, Aaron M. Collins, et al. "Label-free measurement of algal triacylglyceride production using fluorescence hyperspectral imaging." Algal Research 5 (July 2014): 181–89. http://dx.doi.org/10.1016/j.algal.2013.11.010.

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40

Jun, Won, Moon S. Kim, Kangjin Lee, Patricia Millner, and Kuanglin Chao. "Assessment of bacterial biofilm on stainless steel by hyperspectral fluorescence imaging." Sensing and Instrumentation for Food Quality and Safety 3, no. 1 (2009): 41–48. http://dx.doi.org/10.1007/s11694-009-9069-1.

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41

Faqeerzada, Mohammad Akbar, Eunsoo Park, Taehyun Kim, et al. "Fluorescence Hyperspectral Imaging for Early Diagnosis of Heat-Stressed Ginseng Plants." Applied Sciences 13, no. 1 (2022): 31. http://dx.doi.org/10.3390/app13010031.

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Ginseng is a perennial herbaceous plant that has been widely consumed for medicinal and dietary purposes since ancient times. Ginseng plants require shade and cool temperatures for better growth; climate warming and rising heat waves have a negative impact on the plants’ productivity and yield quality. Since South Korea’s temperature is increasing beyond normal expectations and is seriously threatening ginseng plants, an early-stage non-destructive diagnosis of stressed ginseng plants is essential before symptomatic manifestation to produce high-quality ginseng roots. This study demonstrated the potential of fluorescence hyperspectral imaging to achieve the early high-throughput detection and prediction of chlorophyll composition in four varieties of heat-stressed ginseng plants: Chunpoong, Jakyeong, Sunil, and Sunmyoung. Hyperspectral imaging data of 80 plants from these four varieties (temperature-sensitive and temperature-resistant) were acquired before and after exposing the plants to heat stress. Additionally, a SPAD-502 meter was used for the non-destructive measurement of the greenness level. In accordance, the mean spectral data of each leaf were extracted from the region of interest (ROI). Analysis of variance (ANOVA) was applied for the discrimination of heat-stressed plants, which was performed with 96% accuracy. Accordingly, the extracted spectral data were used to develop a partial least squares regression (PLSR) model combined with multiple preprocessing techniques for predicting greenness composition in ginseng plants that significantly correlates with chlorophyll concentration. The results obtained from PLSR analysis demonstrated higher determination coefficients of R2val = 0.90, and a root mean square error (RMSE) of 3.59%. Furthermore, five proposed bands (683 nm, 688 nm, 703 nm, 731 nm, and 745 nm) by stepwise regression (SR) were developed into a PLSR model, and the model coefficients were used to create a greenness-level concentration in images that showed differences between the control and heat-stressed plants for all varieties.
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Zhou, Ju, Feiyi Li, Xinwu Wang, et al. "Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll." Plants 13, no. 9 (2024): 1270. http://dx.doi.org/10.3390/plants13091270.

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Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization–Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.
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Wei, Lifei, Yangxi Zhang, Ziran Yuan, Zhengxiang Wang, Feng Yin, and Liqin Cao. "Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil." Applied Sciences 10, no. 8 (2020): 2941. http://dx.doi.org/10.3390/app10082941.

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Soil total arsenic (TAs) contamination caused by human activities—such as mining, smelting, and agriculture—is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate total arsenic (TAs) concentration in soil. VNIR with hyperspectral imaging has the potential to predict TAs concentration in soil. In this study, 59 soil samples were collected from the Daye City mining area of China, and hyperspectral imaging of the soil samples was undertaken using a visible/near-infrared hyperspectral imaging system (wavelength range 470–900 nm). Spectral preprocessing included standard normal variate (SNV) transformation, multivariate scatter correction (MSC), first derivative (FD) preprocessing, and second derivative (SD) preprocessing. Characteristic bands were then identified based on Spearman’s rank correlation coefficients. Four regression models were used for the modeling prediction: partial least squares regression (PLSR) (R2 = 0.71, RMSE = 0.48), support vector machine regression (SVMR) (R2 = 0.78, RMSE = 0.42), random forest (RF) (R2 = 0.78, RMSE = 0.42), and extremely randomized trees regression (ETR) (R2 = 0.81, RMSE = 0.38). The prediction results were compared with the results of atomic fluorescence spectrometry methods. In the prediction results of the models, the accuracy of ETR using FD preprocessing was the highest. The results confirmed that hyperspectral imaging combined with Spearman’s rank correlation with machine learning models can be used to estimate soil TAs content.
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Kleynhans, Tania, David W. Messinger, Roger L. Easton, and John K. Delaney. "Low-Cost Multispectral System Design for Pigment Analysis in Works of Art." Sensors 21, no. 15 (2021): 5138. http://dx.doi.org/10.3390/s21155138.

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To better understand and preserve works of art, knowledge is needed about the pigments used to create the artwork. Various noninvasive techniques have been used previously to create pigment maps, such as combining X-ray fluorescence and hyperspectral imaging data. Unfortunately, most museums have limited funding for the expense of specialized research equipment, such as hyperspectral reflectance imaging systems. However, many museums have hand-held point X-ray fluorescence systems attached to motorized easels for scanning artwork. To assist museums in acquiring data that can produce similar results to that of HSI systems, while minimizing equipment costs, this study designed and modeled a prototype system to demonstrate the expected performance of a low-cost multispectral system that can be attached to existing motorized easels. We show that multispectral systems with a well-chosen set of spectral bands can often produce classification maps with value on par with hyperspectral systems. This study analyzed the potential for capturing data with a point scanning system through predefined filters. By applying the system and noise modeling parameters to HSI data captured from a 14th-Century illumination, the study reveals that the proposed multispectral imaging system is a viable option for this need.
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Simko, Ivan, Jose A. Jimenez-Berni, and Robert T. Furbank. "Detection of decay in fresh-cut lettuce using hyperspectral imaging and chlorophyll fluorescence imaging." Postharvest Biology and Technology 106 (August 2015): 44–52. http://dx.doi.org/10.1016/j.postharvbio.2015.04.007.

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46

Castro-Díaz, Miguel, Mohamed Osmani, Sergio Cavalaro, et al. "Hyperspectral Imaging Sorting of Refurbishment Plasterboard Waste." Applied Sciences 13, no. 4 (2023): 2413. http://dx.doi.org/10.3390/app13042413.

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Post-consumer plasterboard waste sorting is carried out manually by operators, which is time-consuming and costly. In this work, a laboratory-scale hyperspectral imaging (HSI) system was evaluated for automatic refurbishment plasterboard waste sorting. The HSI system was trained to differentiate between plasterboard (gypsum core between two lining papers) and contaminants (e.g., wood, plastics, mortar or ceramics). Segregated plasterboard samples were crushed and sieved to obtain gypsum particles of less than 250 microns, which were characterized through X-ray fluorescence to determine their chemical purity levels. Refurbishment plasterboard waste particles <10 mm in size were not processed with the HSI-based sorting system because the manual processing of these particles at a laboratory scale would have been very time-consuming. Gypsum from refurbishment plasterboard waste particles <10 mm in size contained very small amounts of undesirable chemical impurities for plasterboard manufacturing (chloride, magnesium, sodium, potassium and phosphorus salts), and its chemical purity was similar to that of the gypsum from HSI-sorted plasterboard (96 wt%). The combination of unprocessed refurbishment plasterboard waste <10 mm with HSI-sorted plasterboard ≥10 mm in size led to a plasterboard recovery yield >98 wt%. These findings underpin the potential implementation of an industrial-scale HSI system for plasterboard waste sorting.
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LIN, Xiu, Faduman HA, Lulu MA, et al. "A cotton leaf nitrogen monitoring model based on spectral-fluorescence data fusion." Notulae Botanicae Horti Agrobotanici Cluj-Napoca 51, no. 1 (2023): 13059. http://dx.doi.org/10.15835/nbha51113059.

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In the present study, hyperspectral imaging and remote sensing of fluorescence were integrated to monitor the nitrogen content in leaves of drip-irrigated cotton at different growth periods in northern Xinjiang, China. Based on the spectrum and chlorophyll fluorescence parameters of nitrogen content in cotton leaves of different growth periods obtained through the shuffled frog-leaping algorithm (SFLA), the successive projection algorithm (SPA), grey relational analysis (GRA), and competitive adaptive reweighted sampling (CARS), a monitoring model of nitrogen content in cotton leaves was established via on hyperspectral imaging, chlorophyll fluorescence parameters, and spectral-fluorescence data fusion. The results showed that: (1) there were significant positive correlations between the chlorophyll fluorescence parameters Fv'/Fm', Fv/Fm, Yield, Fm, NPQ, and the nitrogen content at each growth period. (2) The effectiveness of chlorophyll fluorescence parameters in inversion of nitrogen content was the highest at the budding period and the blooming period, and the coefficients of determination (R2) of the validation sets were 0.745 and 0.709, respectively. (3) In the monitoring model for cotton leaf nitrogen in the blooming period that was established based on the decision-level algorithm and spectral-fluorescence data fusion, the R2 value of the training set reached 0.961, and that of the validation set was 0.828. In conclusion, the findings of this study suggest that the feature-level fusion and decision-level fusion algorithms of spectral-fluorescence data can effectively improve the accuracy and reliability of cotton leaf nitrogen monitoring.
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48

Seo, Youngwook, Ahyeong Lee, Balgeum Kim, and Jongguk Lim. "Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods." Applied Sciences 10, no. 19 (2020): 6724. http://dx.doi.org/10.3390/app10196724.

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(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed at verifying the feasibility of detecting and isolating grain flours by using hyperspectral imaging technology and developing a classification model of grain flours. (2) Methods: Multiple hyperspectral images were acquired through line scanning methods from reflectance of visible and near-infrared wavelength (400–1000 nm), reflectance of shortwave infrared wavelength (900–1700 nm), and fluorescence (400–700 nm) by 365 nm ultraviolet (UV) excitation. Eight varieties of grain flours were prepared (rice: 4, starch: 4), and the particle size and starch damage content were measured. To develop the classification model, four multivariate analysis methods (linear discriminant analysis (LDA), partial least-square discriminant analysis, support vector machine, and classification and regression tree) were implemented with several pre-processing methods, and their classification results were compared with respect to accuracy and Cohen’s kappa coefficient obtained from confusion matrices. (3) Results: The highest accuracy was achieved as 97.43% through short-wavelength infrared with normalization in the spectral domain. The submission of the developed classification model to the hyperspectral images showed that the fluorescence method achieves the highest accuracy of 81% using LDA. (4) Conclusions: In this study, the potential of non-destructive classification of rice and starch flours using multiple hyperspectral modalities and chemometric methods were demonstrated.
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Falcioni, Renan, Roney Berti de Oliveira, Marcelo Luiz Chicati, Werner Camargos Antunes, José Alexandre M. Demattê, and Marcos Rafael Nanni. "Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants." Sensors 24, no. 19 (2024): 6490. http://dx.doi.org/10.3390/s24196490.

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The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.
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50

Min, Hyun Jung, Jianwei Qin, Pappu Kumar Yadav, et al. "Classification of Citrus Leaf Diseases Using Hyperspectral Reflectance and Fluorescence Imaging and Machine Learning Techniques." Horticulturae 10, no. 11 (2024): 1124. http://dx.doi.org/10.3390/horticulturae10111124.

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Citrus diseases are significant threats to citrus groves, causing financial losses through reduced fruit size, blemishes, premature fruit drop, and tree death. The detection of citrus diseases via leaf inspection can improve grove management and mitigation efforts. This study explores the potential of a portable reflectance and fluorescence hyperspectral imaging (HSI) system for detecting and classifying a control group and citrus leaf diseases, including canker, Huanglongbing (HLB), greasy spot, melanose, scab, and zinc deficiency. The HSI system was used to simultaneously collect reflectance and fluorescence images from the front and back sides of the leaves. Nine machine learning classifiers were trained using full spectra and spectral bands selected through principal component analysis (PCA) from the HSI with pixel-based and leaf-based spectra. A support vector machine (SVM) classifier achieved the highest overall classification accuracy of 90.7% when employing the full spectra of combined reflectance and fluorescence data and pixel-based analysis from the back side of the leaves, whereas a discriminant analysis classifier yielded the best accuracy of 94.5% with the full spectra of combined reflectance and fluorescence data and leaf-based analysis. Among the diseases, control, scab, and melanose were classified most accurately, each with over 90% accuracy. Therefore, the integration of the reflectance and fluorescence HSI with advanced machine learning techniques demonstrated the capability to accurately detect and classify these citrus leaf diseases with high precision.
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