{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:10:20Z","timestamp":1790698220986,"version":"4.1.0"},"reference-count":64,"publisher":"American Chemical Society (ACS)","issue":"38","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015799","name":"Universidade Estadual de Maring?","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015799","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004612","name":"Funda??o Arauc?ria","doi-asserted-by":"publisher","award":["CP 19\/2022Jovens Doutores"],"award-info":[{"award-number":["CP 19\/2022Jovens Doutores"]}],"id":[{"id":"10.13039\/501100004612","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico","doi-asserted-by":"publisher","award":["CNPq 168180\/20227"],"award-info":[{"award-number":["CNPq 168180\/20227"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Non-destructive classification of ornamental plant material could improve greenhouse quality control, cultivar screening, and spectral phenotyping; however, most routine decisions still rely on visual inspection. We evaluated proximal VIS\u2013NIR\u2013SWIR spectroradiometry (400\u20132400 nm) for 900 balanced plant-level leaf or bract spectra representing nine ornamental classes from pothos, poinsettia, geranium, and hibiscus. The spectra formed a highly structured low-dimensional dataset, with the first three principal components explaining 97.11% of the total variance. Full-spectrum and edge-trimmed representations preserved high performance (best macro-F1 = 82.56% and 81.99%, respectively), whereas a ReliefF-selected 16-band green window (547\u2013562 nm) reduced performance to 60.77% macro-F1. Among the full-spectrum deep models, MLP_Deep achieved 80.99% F1. These results show that proximal reflectance enables effective and interpretable plant-level ornamental phenotype classification and discrimination within the present benchmark, whereas compact green-band selection alone cannot replace broader VIS\u2013NIR\u2013SWIR information for closely related foliage classes.<\/jats:p>","DOI":"10.1021\/acsomega.6c05120","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T07:01:32Z","timestamp":1789369292000},"page":"57197-57212","source":"Crossref","is-referenced-by-count":0,"title":["VIS\u2013NIR\u2013SWIR Proximal Spectroradiometry\nCoupled with Machine Learning and Deep Learning for Ornamental Plant\nIdentification and Classification"],"prefix":"10.1021","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2343-5045","authenticated-orcid":true,"given":"Renan","family":"Falcioni","sequence":"first","affiliation":[{"name":"State University of Maringa\u0301 , , Av. Colombo, 5790 , , ,","place":["Maringa\u0301, Parana\u0301, Brazil, 87020\u2212900"]},{"name":"University of Sa\u0303o Paulo , , Av. Pa\u0301dua Dias, 11 , , ,","place":["Piracicaba, Sa\u0303o Paulo, Brazil, 13418\u2212900"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5328-0323","authenticated-orcid":true,"given":"Jose\u0301 Alexandre M.","family":"Dematte\u0302","sequence":"additional","affiliation":[{"name":"University of Sa\u0303o Paulo , , Av. Pa\u0301dua Dias, 11 , , ,","place":["Piracicaba, Sa\u0303o Paulo, Brazil, 13418\u2212900"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-2661","authenticated-orcid":true,"given":"Marcos","family":"Rafael Nanni","sequence":"additional","affiliation":[{"name":"State University of Maringa\u0301 , , Av. Colombo, 5790 , , ,","place":["Maringa\u0301, Parana\u0301, Brazil, 87020\u2212900"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"2026092911110724000_cit1","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.tplants.2021.12.003","article-title":"Applications\nof hyperspectral imaging in plant phenotyping","volume":"27","author":"Saric\u0301","year":"2022","journal-title":"Trends Plant Sci."},{"issue":"7","key":"2026092911110724000_cit2","doi-asserted-by":"publisher","DOI":"10.3390\/su13073707","article-title":"Precision agriculture digital technologies\nfor sustainable fungal\ndisease management of ornamental plants","volume":"13","author":"Traversari","year":"2021","journal-title":"Sustainability"},{"key":"2026092911110724000_cit3","doi-asserted-by":"publisher","DOI":"10.1016\/j.scienta.2021.110546","article-title":"Hyperspectral imaging for high-throughput\nvitality monitoring in ornamental plant production","volume":"291","author":"Ruett","year":"2022","journal-title":"Sci. Hortic."},{"key":"2026092911110724000_cit4","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1146\/annurev-arplant-050312-120137","article-title":"Future scenarios for\nplant phenotyping","volume":"64","author":"Fiorani","year":"2013","journal-title":"Annu. Rev. Plant Biol."},{"key":"2026092911110724000_cit5","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.tplants.2013.09.008","article-title":"Field high-throughput\nphenotyping: the new crop breeding\nfrontier","volume":"19","author":"Araus","year":"2014","journal-title":"Trends Plant Sci."},{"issue":"2","key":"2026092911110724000_cit6","doi-asserted-by":"publisher","DOI":"10.3390\/rs17020285","article-title":"Using hyperspectral\nimaging and principal component analysis to detect and monitor water\nstress in ornamental plants","volume":"17","author":"Patiluna","year":"2025","journal-title":"Remote Sens."},{"key":"2026092911110724000_cit7","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.rse.2017.03.004","article-title":"PROSPECT-D:\ntowards modeling leaf optical properties through a complete lifecycle","volume":"193","author":"Fe\u0301ret","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"2026092911110724000_cit8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2020.112173","article-title":"PROSPECT-PRO\nfor estimating content of nitrogen-containing leaf proteins and other\ncarbon-based constituents","volume":"252","author":"Fe\u0301ret","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"2026092911110724000_cit9","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1016\/j.jphotobiol.2017.11.023","article-title":"Non-invasive\nquantification of foliar pigments: possibilities\nand limitations of reflectance- and absorbance-based approaches","volume":"178","author":"Gitelson","year":"2018","journal-title":"J. Photochem. Photobiol., B"},{"key":"2026092911110724000_cit10","doi-asserted-by":"publisher","DOI":"10.1016\/j.flora.2025.152815","article-title":"The secrets\nof variegated leaves: molecular to physiological and ecological insights","volume":"331","author":"Bhardwaj","year":"2025","journal-title":"Flora"},{"key":"2026092911110724000_cit11","doi-asserted-by":"publisher","DOI":"10.1093\/hr\/uhac246","article-title":"High-throughput discovery\nof plastid genes causing\nalbino phenotypes in ornamental chimeric plants","volume":"10","author":"Park","year":"2023","journal-title":"Hortic. Res."},{"issue":"14","key":"2026092911110724000_cit12","doi-asserted-by":"publisher","DOI":"10.3390\/plants14142155","article-title":"Advances in bract coloration: diversity,\npigment synthesis, and regulatory mechanisms in ornamental plants","volume":"14","author":"Li","year":"2025","journal-title":"Plants"},{"key":"2026092911110724000_cit13","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1111\/nph.18713","article-title":"Predicting leaf traits across functional\ngroups using reflectance spectroscopy","volume":"238","author":"Kothari","year":"2023","journal-title":"New Phytol."},{"key":"2026092911110724000_cit14","doi-asserted-by":"publisher","first-page":"4050","DOI":"10.1093\/jxb\/erad129","article-title":"A leaf-level spectral\nlibrary to support high-throughput plant phenotyping:\npredictive accuracy and model transfer","volume":"74","author":"Wijewardane","year":"2023","journal-title":"J. Exp.\nBot."},{"issue":"4","key":"2026092911110724000_cit15","doi-asserted-by":"publisher","DOI":"10.1016\/j.xplc.2021.100209","article-title":"Hyperspectral\nreflectance-based phenotyping for quantitative genetics in crops:\nprogress and challenges","volume":"2","author":"Grzybowski","year":"2021","journal-title":"Plant Commun."},{"key":"2026092911110724000_cit16","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1111\/nph.16771","article-title":"Leaf reflectance spectra capture the evolutionary history\nof seed plants","volume":"228","author":"Meireles","year":"2020","journal-title":"New Phytol."},{"issue":"1","key":"2026092911110724000_cit17","doi-asserted-by":"publisher","DOI":"10.1186\/s13007-023-01089-9","article-title":"Evaluating potential of leaf reflectance spectra to\nmonitor plant genetic variation","volume":"19","author":"Li","year":"2023","journal-title":"Plant Methods"},{"issue":"1","key":"2026092911110724000_cit18","doi-asserted-by":"publisher","DOI":"10.1002\/ppj2.20106","article-title":"Data driven discovery and quantification\nof hyperspectral leaf reflectance phenotypes across a maize diversity\npanel","volume":"7","author":"Tross","year":"2024","journal-title":"Plant Phenome J."},{"key":"2026092911110724000_cit19","doi-asserted-by":"publisher","first-page":"6175","DOI":"10.1093\/jxb\/erab295","article-title":"A best-practice\nguide to predicting plant traits from\nleaf-level hyperspectral data using partial least squares regression","volume":"72","author":"Burnett","year":"2021","journal-title":"J. Exp. Bot."},{"key":"2026092911110724000_cit20","doi-asserted-by":"publisher","DOI":"10.1016\/j.agrformet.2023.109458","article-title":"Crop yield prediction\nvia explainable AI and interpretable machine learning: dangers of\nblack box models for evaluating climate change impacts on crop yield","volume":"336","author":"Hu","year":"2023","journal-title":"Agric. For. Meteorol."},{"issue":"7","key":"2026092911110724000_cit21","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2025.112759","article-title":"A review on\nspectral data preprocessing techniques for machine learning and quantitative\nanalysis","volume":"28","author":"Yan","year":"2025","journal-title":"iScience"},{"key":"2026092911110724000_cit22","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach.\nLearn."},{"key":"2026092911110724000_cit23","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern\nclassification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE\nTrans. Inf. Theory"},{"key":"2026092911110724000_cit24","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"2026092911110724000_cit25","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1023\/A:1025667309714","article-title":"Theoretical\nand empirical analysis of ReliefF and RReliefF","volume":"53","author":"Robnik-S\u030cikonja","year":"2003","journal-title":"Mach. Learn."},{"key":"2026092911110724000_cit26","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/BF02551274","article-title":"Approximation\nby superpositions of a sigmoidal function","volume":"2","author":"Cybenko","year":"1989","journal-title":"Math.\nControl Signals Syst."},{"key":"2026092911110724000_cit27","doi-asserted-by":"publisher","DOI":"10.1016\/j.aca.2025.343766","article-title":"A comparative analysis\nof deep learning and chemometric approaches for spectral data modeling","volume":"1347","author":"Gariso","year":"2025","journal-title":"Anal. Chim. Acta"},{"issue":"4","key":"2026092911110724000_cit28","doi-asserted-by":"publisher","first-page":"532","DOI":"10.21273\/HORTTECH04866-21","article-title":"Using advanced optical\nsensing to\nquantify phytotoxicity in ornamental plants","volume":"31","author":"Nansen","year":"2021","journal-title":"HortTechnology"},{"key":"2026092911110724000_cit29","doi-asserted-by":"publisher","first-page":"3064","DOI":"10.1109\/TGRS.2018.2880193","article-title":"Selection of informative spectral bands for PLS models\nto estimate\nfoliar chlorophyll content using hyperspectral reflectance","volume":"57","author":"Jin","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"2026092911110724000_cit30","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1109\/MGRS.2019.2911100","article-title":"Hyperspectral band\nselection: a review","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE\nGeosci. Remote Sens. Mag."},{"key":"2026092911110724000_cit31","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-83118-8","article-title":"A new band selection framework for\nhyperspectral remote\nsensing image classification","volume":"14","author":"Kumar","year":"2024","journal-title":"Sci. Rep."},{"issue":"6","key":"2026092911110724000_cit32","doi-asserted-by":"publisher","DOI":"10.3390\/plants15060933","article-title":"VIS-NIR-SWIR\nhyperspectral imaging and advanced machine and deep learning algorithms\nfor a controlled benchmark of bean seed identification and classification","volume":"15","author":"Falcioni","year":"2026","journal-title":"Plants"},{"key":"2026092911110724000_cit33","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and differentiation of data by simplified\nleast squares procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"2026092911110724000_cit34","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component\nanalysis: a review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A"},{"key":"2026092911110724000_cit35","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-56706-x","article-title":"Evaluation metrics and statistical\ntests for machine learning","volume":"14","author":"Rainio","year":"2024","journal-title":"Sci. Rep."},{"key":"2026092911110724000_cit36","doi-asserted-by":"publisher","first-page":"22","DOI":"10.18845\/tm.v37i7.7293","article-title":"Improving\nbalanced accuracy for minority plant species\nunder data imbalance","volume":"37","author":"Gonzalez-Villanueva","year":"2024","journal-title":"Tecnol. Marcha"},{"issue":"3","key":"2026092911110724000_cit37","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall\nplot is more informative than\nthe ROC plot when evaluating binary classifiers on imbalanced datasets","volume":"10","author":"Saito","year":"2015","journal-title":"PLoS One"},{"issue":"3","key":"2026092911110724000_cit38","doi-asserted-by":"publisher","DOI":"10.3390\/rs16030533","article-title":"Selecting\nand interpreting multiclass\nloss and accuracy assessment metrics for classifications with class\nimbalance: guidance and best practices","volume":"16","author":"Farhadpour","year":"2024","journal-title":"Remote\nSens."},{"key":"2026092911110724000_cit39","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1214\/ss\/1177013815","article-title":"Bootstrap methods for standard errors, confidence intervals,\nand other measures of statistical accuracy","volume":"1","author":"Efron","year":"1986","journal-title":"Stat. Sci."},{"key":"2026092911110724000_cit40","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate\nstatistical tests for comparing supervised classification learning\nalgorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput."},{"key":"2026092911110724000_cit41","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/S0003-2670(02)01422-8","article-title":"Comparison of supervised pattern\nrecognition methods with McNemar\u2019s statistical test: Application\nto qualitative analysis of sugar beet by near-infrared spectroscopy","volume":"477","author":"Roggo","year":"2003","journal-title":"Anal. Chim. Acta"},{"key":"2026092911110724000_cit42","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations\nby back-propagating\nerrors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"issue":"7","key":"2026092911110724000_cit43","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy15071585","article-title":"Maize seed variety\nclassification based on hyperspectral imaging\nand a CNN-LSTM learning framework","volume":"15","author":"Zhu","year":"2025","journal-title":"Agronomy"},{"key":"2026092911110724000_cit44","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2024.1376915","article-title":"Efficient residual network using\nhyperspectral images for corn variety identification","volume":"15","author":"Li","year":"2024","journal-title":"Front. Plant Sci."},{"key":"2026092911110724000_cit45","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1007\/s10618-020-00710-y","article-title":"InceptionTime:\nfinding AlexNet for time series classification","volume":"34","author":"Ismail Fawaz","year":"2020","journal-title":"Data Min. Knowl. Discovery"},{"key":"2026092911110724000_cit46","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.108034","article-title":"Deep learning\nmodels\nbased on hyperspectral data and time-series phenotypes for predicting\nquality attributes in lettuces under water stress","volume":"211","author":"Yu","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"2026092911110724000_cit47","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-97052-w","article-title":"CMTNet: a\nhybrid CNN-transformer\nnetwork for UAV-based hyperspectral crop classification in precision\nagriculture","volume":"15","author":"Guo","year":"2025","journal-title":"Sci. Rep."},{"key":"2026092911110724000_cit48","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2022.104520","article-title":"A tutorial on automatic\nhyperparameter tuning of deep\nspectral modelling for regression and classification tasks","volume":"223","author":"Passos","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"2026092911110724000_cit49","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2024.1499875","article-title":"Deep learning-enabled\nexploration of global spectral features for\nphotosynthetic capacity estimation","volume":"15","author":"Deng","year":"2025","journal-title":"Front. Plant\nSci."},{"issue":"1","key":"2026092911110724000_cit50","doi-asserted-by":"publisher","DOI":"10.1186\/s13007-021-00806-6","article-title":"Wheat physiology\npredictor: predicting physiological traits in wheat from hyperspectral\nreflectance measurements using deep learning","volume":"17","author":"Furbank","year":"2021","journal-title":"Plant Methods"},{"key":"2026092911110724000_cit51","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"issue":"17","key":"2026092911110724000_cit52","doi-asserted-by":"publisher","first-page":"10019","DOI":"10.1021\/acs.jafc.4c11492","article-title":"Hyperspectral imaging\nand deep learning for quality and safety inspection\nof fruits and vegetables: a review","volume":"73","author":"Yang","year":"2025","journal-title":"J. Agric.\nFood Chem."},{"issue":"3","key":"2026092911110724000_cit53","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib:\na 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"issue":"60","key":"2026092911110724000_cit54","doi-asserted-by":"publisher","DOI":"10.21105\/joss.03021","article-title":"seaborn:\nstatistical data visualization","volume":"6","author":"Waskom","year":"2021","journal-title":"J. Open Source\nSoftware"},{"key":"2026092911110724000_cit55","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental\nalgorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat. Methods"},{"key":"2026092911110724000_cit56","first-page":"2825","article-title":"Scikit-learn: machine\nlearning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"2026092911110724000_cit57","doi-asserted-by":"publisher","DOI":"10.3390\/ijms24065288","article-title":"Contrasting metabolisms in green and white leaf sectors\nof variegated Pelargonium zonale: an\nintegrative transcriptomic and metabolomic study","volume":"24","author":"Milic\u0301","year":"2023","journal-title":"Int. J. Mol. Sci."},{"issue":"19","key":"2026092911110724000_cit58","doi-asserted-by":"publisher","first-page":"28909","DOI":"10.1021\/acsomega.6c01933","article-title":"Pigment-structure\nleaf syndromes in poinsettia (Euphorbia pulcherrima Willd. ex Klotzsch) alter chloroplast ultrastructure, biochemical\nprofiling, and photosynthetic quantum yields","volume":"11","author":"Falcioni","year":"2026","journal-title":"ACS Omega"},{"key":"2026092911110724000_cit59","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1104\/pp.20.00577","article-title":"Spectral phenotyping of physiological and anatomical\nleaf traits related with maize water status","volume":"184","author":"Cotrozzi","year":"2020","journal-title":"Plant Physiol."},{"issue":"12","key":"2026092911110724000_cit60","doi-asserted-by":"publisher","first-page":"1736","DOI":"10.21273\/HORTSCI18177-24","article-title":"Three new\ncultivars of pothos (Epipremnum aureum)","volume":"59","author":"Chen","year":"2024","journal-title":"HortScience"},{"key":"2026092911110724000_cit61","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the\nmean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"11","key":"2026092911110724000_cit62","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy11112197","article-title":"Monitoring optical tool to determine\nthe chlorophyll\nconcentration in ornamental plants","volume":"11","author":"Jime\u0301nez-Lao","year":"2021","journal-title":"Agronomy"},{"key":"2026092911110724000_cit63","doi-asserted-by":"publisher","DOI":"10.1016\/j.rsase.2020.100400","article-title":"Hyperspectral reflectance\nimaging to classify lettuce varieties by optimum selected wavelengths\nand linear discriminant analysis","volume":"20","author":"Furlanetto","year":"2020","journal-title":"Remote Sens.\nAppl.: Soc. Environ."},{"key":"2026092911110724000_cit64","doi-asserted-by":"publisher","DOI":"10.1016\/j.trac.2022.116804","article-title":"Deep learning for near-infrared\nspectral data modelling: hypes and benefits","volume":"157","author":"Mishra","year":"2022","journal-title":"TrAC Trends Anal. Chem."}],"container-title":["ACS Omega"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acsomega.6c05120","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/38\/57197\/68496295\/acsomega.6c05120.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/11\/38\/57197\/68496295\/acsomega.6c05120.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:51:15Z","timestamp":1790697075000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/acsodf\/article\/11\/38\/57197\/5429179\/VIS-NIR-SWIR-Proximal-Spectroradiometry-Coupled"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,14]]},"references-count":64,"journal-issue":{"issue":"38","published-online":{"date-parts":[[2026,9,14]]},"published-print":{"date-parts":[[2026,9,29]]}},"URL":"https:\/\/doi.org\/10.1021\/acsomega.6c05120","relation":{},"ISSN":["2470-1343"],"issn-type":[{"value":"2470-1343","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,14]]}}}