{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T02:52:00Z","timestamp":1775875920353,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Agriculture, Forestry, and Fisheries Research Council","award":["19191026"],"award-info":[{"award-number":["19191026"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Quantifying carotenoid content in agriculture is essential for assessing crop nutritional value, improving crop quality, promoting human health, understanding plant stress responses, and facilitating breeding and genetic improvement efforts. Hyperspectral reflectance imaging is a nondestructive and rapid tool for estimating the carotenoid content. In spectrometer reflectance measurements, there are various sources of noise that can compromise the accuracy of carotenoid content estimations. Recently, various machine learning algorithms have been identified as robust against various types of noise, eliminating the need for denoising processes. Specifically, Cubist and the one-dimensional convolutional neural network (1D-CNN) have been used in evaluating vegetation properties based on reflectance data. We used regression models based on Cubist and 1D-CNN to estimate carotenoid content from reflectance data (the spectral resolution was resampled in 5 nm bands across the entire wavelength domain from 400 to 850 nm) with various degrees of Gaussian and spike noise added. The Cubist-based model was the most robust for this purpose: it achieved a ratio of performance to deviation of 1.41, a root mean square error of 1.11 \u00b5g\/cm2, and a coefficient of determination (R2) of 0.496 when applied to reflectance data with a combination of Gaussian (mean: 0; variance: 0.04) and spike noise (density: 0.05; amplitude: 0.05).<\/jats:p>","DOI":"10.3390\/rs15174303","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T11:41:18Z","timestamp":1693482078000},"page":"4303","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Carotenoid Content Estimation in Tea Leaves Using Noisy Reflectance Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8330-3730","authenticated-orcid":false,"given":"Rei","family":"Sonobe","sequence":"first","affiliation":[{"name":"Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan"},{"name":"Institute for Tea Science, Shizuoka University, Shizuoka 422-8529, Japan"}]},{"given":"Yuhei","family":"Hirono","sequence":"additional","affiliation":[{"name":"Institute for Tea Science, Shizuoka University, Shizuoka 422-8529, Japan"},{"name":"Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization, Shimada 428-8501, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1016\/S0891-5849(98)00266-4","article-title":"Antioxidant potentials of vitamin A and carotenoids and their relevance to heart disease","volume":"26","author":"Palace","year":"1999","journal-title":"Free. Radic. Biol. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11418-019-01364-x","article-title":"Carotenoids as natural functional pigments","volume":"74","author":"Maoka","year":"2020","journal-title":"J. Nat. Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Crupi, P., Faienza, M.F., Naeem, M.Y., Corbo, F., Clodoveo, M.L., and Muraglia, M. (2023). Overview of the Potential Beneficial Effects of Carotenoids on Consumer Health and Well-Being. Antioxidants, 12.","DOI":"10.3390\/antiox12051069"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Elvira-Torales, L.I., Garc\u00eda-Alonso, J., and Periago-Cast\u00f3n, M.J. (2019). Nutritional Importance of Carotenoids and Their Effect on Liver Health: A Review. Antioxidants, 8.","DOI":"10.3390\/antiox8070229"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1096\/fasebj.10.4.8647339","article-title":"Carotenoids 3: In Vivo Function of Carotenoids in Higher Plants","volume":"10","author":"Gilmore","year":"1996","journal-title":"FASEB J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S1011-1344(97)00092-4","article-title":"The carotenoids as anti-oxidants\u2014A review","volume":"41","author":"Edge","year":"1997","journal-title":"J. Photochem. Photobiol. B Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110164","DOI":"10.1016\/j.scienta.2021.110164","article-title":"Low temperature effects on carotenoids biosynthesis in the leaves of green and albino tea plant (Camellia sinensis (L.) O. Kuntze)","volume":"285","author":"Yang","year":"2021","journal-title":"Sci. Hortic."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Niu, S., Deng, X., Song, Q., He, L., Bai, D., and He, Y. (2023). Genome-wide association study of leaf-related traits in tea plant in Guizhou based on genotyping-by-sequencing. BMC Plant Biol., 23.","DOI":"10.1186\/s12870-023-04192-0"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.tifs.2004.04.0094","article-title":"The Role of Carotenoids in Consumer Choice and the Likely Bene Wts from Their Inclusion into Products for Human Consumption","volume":"15","author":"Baker","year":"2004","journal-title":"Trends Food Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/0731-7085(84)80062-X","article-title":"High-Performance Liquid Chromatography\u2014Advances and Perspectives","volume":"2","author":"Smith","year":"1984","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0176-1617(11)81192-2","article-title":"The Spectral Determination of Chlorophylls a and b, as well as Total Carotenoids, Using Various Solvents with Spectrophotometers of Different Resolution","volume":"144","author":"Wellburn","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.optmat.2005.10.002","article-title":"Ultraviolet and visible spectroscopic studies of phthalocyanine and its complexes thin films","volume":"29","author":"Seoudi","year":"2006","journal-title":"Opt. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1080\/10106049.2014.985743","article-title":"Developing a spectral library of mangrove species of Indian east coast using field spectroscopy","volume":"30","author":"Prasad","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Crist\u00f3bal, J., Graham, P., Prakash, A., Buchhorn, M., Gens, R., Guldager, N., and Bertram, M. (2021). Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping. Remote Sens., 13.","DOI":"10.3390\/rs13061178"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qamar, F., Sharma, M.S., and Dobler, G. (2022). The Impacts of Air Quality on Vegetation Health in Dense Urban Environments: A Ground-Based Hyperspectral Imaging Approach. Remote Sens., 14.","DOI":"10.3390\/rs14163854"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shaik, R.U., Periasamy, S., and Zeng, W. (2023). Potential Assessment of PRISMA Hyperspectral Imagery for Remote Sensing Applications. Remote Sens., 15.","DOI":"10.3390\/rs15051378"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sonobe, R., Miura, Y., Sano, T., and Horie, H. (2018). Monitoring Photosynthetic Pigments of Shade-Grown Tea from Hyperspectral Reflectance. Can. J. Remote Sens., 44.","DOI":"10.1080\/07038992.2018.1461555"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.biosystemseng.2018.09.018","article-title":"Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments","volume":"175","author":"Sonobe","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sonobe, R., Hirono, Y., and Oi, A. (2020). Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms. Plants, 9.","DOI":"10.3390\/plants9030368"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1080\/2150704X.2020.1795294","article-title":"Quantifying chlorophyll-a and b content in tea leaves using hyperspectral reflectance and deep learning","volume":"11","author":"Sonobe","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2005.09.002","article-title":"Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy","volume":"99","author":"Miller","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.isprsjprs.2019.06.016","article-title":"Lever-arm and boresight correction, and field of view determination of a spectroradiometer mounted on an unmanned aircraft system","volume":"155","author":"Gautam","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sonobe, R., and Hirono, Y. (2023). Applying Variable Selection Methods and Preprocessing Techniques to Hyperspectral Reflectance Data to Estimate Tea Cultivar Chlorophyll Content. Remote Sens., 15.","DOI":"10.3390\/rs15010019"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5363","DOI":"10.1080\/10106049.2021.1914747","article-title":"Use of spectral reflectance from a compact spectrometer to assess chlorophyll content in Zizania latifolia","volume":"37","author":"Sonobe","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1080\/10942910801992991","article-title":"Near-Infrared Spectroscopy for Classification of Oranges and Prediction of the Sugar Content","volume":"12","author":"Shao","year":"2009","journal-title":"Int. J. Food Prop."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1366\/0003702894202201","article-title":"Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra","volume":"43","author":"Barnes","year":"1989","journal-title":"Appl. Spectrosc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.compag.2012.03.008","article-title":"Reflectance Vis\/NIR spectroscopy for nondestructive taste characterization of Valencia oranges","volume":"85","author":"Jamshidi","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1016\/j.snb.2018.11.034","article-title":"Automatic de-noising of close-range hyperspectral images with a wavelength-specific shearlet-based image noise reduction method","volume":"281","author":"Mishra","year":"2019","journal-title":"Sens. Actuators B Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5054","DOI":"10.1109\/TGRS.2015.2417098","article-title":"Band-Specific Shearlet-Based Hyperspectral Image Noise Reduction","volume":"53","author":"Karami","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1049\/iet-ipr.2017.0391","article-title":"Multiscale adaptive regularisation Savitzky\u2013Golay method for speckle noise reduction in ultrasound images","volume":"12","author":"Vorasayan","year":"2018","journal-title":"IET Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1366\/12-06723","article-title":"Optimizing Savitzky-Golay Parameters for Improving Spectral Resolution and Quantification in Infrared Spectroscopy","volume":"67","author":"Zimmermann","year":"2013","journal-title":"Appl. Spectrosc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1562\/0031-8655(2002)075<0272:ACCIPL>2.0.CO;2","article-title":"Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy","volume":"75","author":"Gitelson","year":"2002","journal-title":"Photochem. Photobiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(92)90059-S","article-title":"A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency","volume":"41","author":"Gamon","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_34","first-page":"221","article-title":"Semi-Empirical Indices to Assess Carotenoids\/Chlorophyll a Ratio from Leaf Spectral Re-flectance","volume":"31","author":"Penuelas","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.rse.2017.03.004","article-title":"PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle","volume":"193","author":"Gitelson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1080\/01431161.2017.1407050","article-title":"Estimating leaf carotenoid contents of shade-grown tea using hyperspectral indices and PROSPECT\u2013D inversion","volume":"39","author":"Sonobe","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neunet.2018.12.010","article-title":"An extensive experimental survey of regression methods","volume":"111","author":"Sirsat","year":"2019","journal-title":"Neural Netw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-020-0286-0","article-title":"A comparative dimensionality reduction study in telecom customer segmentation using deep learning and PCA","volume":"7","author":"Alkhayrat","year":"2020","journal-title":"J. Big Data"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sakurada, M., and Yairi, T. (2014, January 8\u201313). Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction. Proceedings of the ACM International Conference Proceeding Series, Montreal, QC, Canada.","DOI":"10.1145\/2689746.2689747"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.geoderma.2019.06.016","article-title":"Convolutional neural network for simultaneous prediction of several soil properties using visible\/near-infrared, mid-infrared, and their combined spectra","volume":"352","author":"Ng","year":"2019","journal-title":"Geoderma"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"112353","DOI":"10.1016\/j.rse.2021.112353","article-title":"Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network","volume":"257","author":"Pullanagari","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_42","unstructured":"Malvern Panalytical (2023, July 19). ASD Plant Probe. Available online: https:\/\/www.azom.com\/equipment-details.aspx?EquipID=5412."},{"key":"ref_43","unstructured":"R Core Team (2023, July 19). R: A Language and Environment for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1198\/jasa.2004.s339","article-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","volume":"99","author":"Ruppert","year":"2004","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_45","unstructured":"Kuhn, M., Weston, S., Keefer, C., Coulter, N., and Quinlan, R. (2023, July 19). Rulequest Research Pty Ltd. Package \u2018Cubist\u2019. Available online: https:\/\/cran.r-project.org\/web\/packages\/Cubist\/Cubist.pdf."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kawamura, K., Nishigaki, T., Andriamananjara, A., Rakotonindrina, H., Tsujimoto, Y., Moritsuka, N., Rabenarivo, M., and Razafimbelo, T. (2021). Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar. Remote Sens., 13.","DOI":"10.3390\/rs13081519"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bisong, E. (2019). Building Machine Learning and Deep Learning Models on Google Cloud Platform, Apress.","DOI":"10.1007\/978-1-4842-4470-8"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.trac.2010.05.006","article-title":"Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy","volume":"29","author":"Palagos","year":"2010","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_49","first-page":"803","article-title":"Near-Infrared Technology in the Agriculture and Food Industries. Herausgegeben von P. Williams und K. Norris. 330 Seiten, zahlr. Abb. und Tab. American Association of Cereal Chemists, Inc., St. Paul, Minnesota, USA, 1987. Preis: 175,90 $ (USA 169,00 $)","volume":"32","author":"Linow","year":"1988","journal-title":"Food Nahr."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"480","DOI":"10.2136\/sssaj2001.652480x","article-title":"Near-Infrared Reflectance Spectroscopy-Principal Components Regression Analyses of Soil Properties","volume":"65","author":"Chang","year":"2001","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Clim. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e623","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1007\/s11947-016-1710-5","article-title":"Predict Compositions and Mechanical Properties of Sugar Beet Using Hyperspectral Scattering","volume":"9","author":"Pan","year":"2016","journal-title":"Food Bioprocess Technol."},{"key":"ref_54","first-page":"102719","article-title":"A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance","volume":"108","author":"Shi","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lamsal, K., Malenovsk\u00fd, Z., Woodgate, W., Waterman, M., Brodribb, T.J., and Aryal, J. (2022). Spectral Retrieval of Eucalypt Leaf Biochemical Traits by Inversion of the Fluspect-Cx Model. Remote Sens., 14.","DOI":"10.3390\/rs14030567"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sonobe, R., Yamashita, H., Mihara, H., Morita, A., and Ikka, T. (2020). Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance. Remote Sens., 12.","DOI":"10.3390\/rs12193265"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1111\/1754-9485.13261","article-title":"A review of medical image data augmentation techniques for deep learning applications","volume":"65","author":"Chlap","year":"2021","journal-title":"J. Med. Imaging Radiat. Oncol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1109\/JSTARS.2014.2298752","article-title":"Toward a Semiautomatic Machine Learning Retrieval of Biophysical Parameters","volume":"7","author":"Caicedo","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_59","unstructured":"DeVries, T., and Taylor, G.W. (2017). Improved Regularization of Convolutional Neural Networks with Cutout. arXiv."},{"key":"ref_60","unstructured":"Brendel, W., Rauber, J., and Bethge, M. (May, January 30). Decision-Based Adversarial Attacks: Reliable Attacks against Black-Box Machine Learning Models. Proceedings of the 6th International Conference on Learning Representations, ICLR 2018\u2014Conference Track Proceedings, Vancouver, BC, Canada."},{"key":"ref_61","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (May, January 30). Towards Deep Learning Models Resistant to Adversarial Attacks. Proceedings of the 6th International Conference on Learning Representations, ICLR 2018\u2014Conference Track Proceedings, Vancouver, BC, Canada."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"945769","DOI":"10.3389\/fenrg.2022.945769","article-title":"Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant","volume":"10","author":"Khan","year":"2022","journal-title":"Front. Energy Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"108518","DOI":"10.1016\/j.measurement.2020.108518","article-title":"Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network","volume":"173","author":"Wang","year":"2021","journal-title":"Measurement"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1080\/01431161.2020.1826065","article-title":"Hyperspectral reflectance sensing for quantifying leaf chlorophyll content in wasabi leaves using spectral pre-processing techniques and machine learning algorithms","volume":"42","author":"Sonobe","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/978-3-319-39126-7_4","article-title":"Carotenoids and Photosynthesis","volume":"79","author":"Hashimoto","year":"2016","journal-title":"Subcell Biochem."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1039\/c3pp25406b","article-title":"The Orange Carotenoid Protein: A blue-green light photoactive protein","volume":"12","author":"Kirilovsky","year":"2013","journal-title":"Photochem. Photobiol. Sci."},{"key":"ref_67","unstructured":"Hamamatsu Photonics (2023, July 19). Mini-Spectrometer. Available online: http:\/\/www.farnell.com\/datasheets\/2822646.pdf."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Udelhoven, T., Schlerf, M., Segl, K., Mallick, K., Bossung, C., Retzlaff, R., Rock, G., Fischer, P., M\u00fcller, A., and Storch, T. (2017). A Satellite-Based Imaging Instrumentation Concept for Hyperspectral Thermal Remote Sensing. Sensors, 17.","DOI":"10.3390\/s17071542"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Zhang, N., Yang, G., Pan, Y., Yang, X., Chen, L., and Zhao, C. (2020). A Review of Advanced Technologies and Development for Hy-perspectral-Based Plant Disease Detection in the Past Three Decades. Remote Sens., 12.","DOI":"10.3390\/rs12193188"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4303\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:44:11Z","timestamp":1760129051000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4303"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,31]]},"references-count":69,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15174303"],"URL":"https:\/\/doi.org\/10.3390\/rs15174303","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,31]]}}}