{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:50:42Z","timestamp":1767192642205,"version":"build-2065373602"},"reference-count":82,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Council for Scientific and Technological Development","award":["168180\/2022\u22127","CP 19\/2022","001","pn 2021\/05129\u22128"],"award-info":[{"award-number":["168180\/2022\u22127","CP 19\/2022","001","pn 2021\/05129\u22128"]}]},{"name":"CEAGRE\u2014Centro de Excel\u00eancia em Agricultura Exponencial","award":["168180\/2022\u22127","CP 19\/2022","001","pn 2021\/05129\u22128"],"award-info":[{"award-number":["168180\/2022\u22127","CP 19\/2022","001","pn 2021\/05129\u22128"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s24196490","type":"journal-article","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T07:39:52Z","timestamp":1728459592000},"page":"6490","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2343-5045","authenticated-orcid":false,"given":"Renan","family":"Falcioni","sequence":"first","affiliation":[{"name":"Department of Agronomy, State University of Maring\u00e1, Av. Colombo, 5790, Maring\u00e1 87020-900, Paran\u00e1, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7338-2666","authenticated-orcid":false,"given":"Roney Berti de","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maring\u00e1, Av. Colombo, 5790, Maring\u00e1 87020-900, Paran\u00e1, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1631-3709","authenticated-orcid":false,"given":"Marcelo Luiz","family":"Chicati","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maring\u00e1, Av. Colombo, 5790, Maring\u00e1 87020-900, Paran\u00e1, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8430-2791","authenticated-orcid":false,"given":"Werner Camargos","family":"Antunes","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maring\u00e1, Av. Colombo, 5790, Maring\u00e1 87020-900, Paran\u00e1, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5328-0323","authenticated-orcid":false,"given":"Jos\u00e9 Alexandre M.","family":"Dematt\u00ea","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Luiz de Queiroz College of Agriculture, University of S\u00e3o Paulo, Av. P\u00e1dua Dias, 11, Piracicaba 13418-260, S\u00e3o Paulo, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-2661","authenticated-orcid":false,"given":"Marcos Rafael","family":"Nanni","sequence":"additional","affiliation":[{"name":"Department of Agronomy, State University of Maring\u00e1, Av. Colombo, 5790, Maring\u00e1 87020-900, Paran\u00e1, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.infrared.2018.01.027","article-title":"Soybean Varieties Discrimination Using Non-imaging Hyperspectral Sensor","volume":"89","author":"Nanni","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, D., Cao, W., Zhang, F., Li, Z., Xu, S., and Wu, X. (2022). A Review of Deep Learning in Multiscale Agricultural Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14030559"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"110959","DOI":"10.1016\/j.rse.2018.11.002","article-title":"Estimating Leaf Mass per Area and Equivalent Water Thickness Based on Leaf Optical Properties: Potential and Limitations of Physical Modelling and Machine Learning","volume":"231","author":"Jay","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1601952","DOI":"10.1080\/15592324.2019.1601952","article-title":"Tissue Specific Disruption of Photosynthetic Electron Transport Rate in Pigeonpea (Cajanus cajan L.) under Elevated Temperature","volume":"14","author":"Gupta","year":"2019","journal-title":"Plant Signal. Behav."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111833","DOI":"10.1016\/j.plantsci.2023.111833","article-title":"Thylakoid Membrane Appression in the Giant Chloroplast of Selaginella martensii Spring: A Lycophyte Challenges Grana Paradigms in Shade-Adapted Species","volume":"336","author":"Colpo","year":"2023","journal-title":"Plant Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.jphotobiol.2010.12.010","article-title":"Govindjee On the Relation between the Kautsky Effect (Chlorophyll a Fluorescence Induction) and Photosystem II: Basics and Applications of the OJIP Fluorescence Transient","volume":"104","author":"Stirbet","year":"2011","journal-title":"J. Photochem. Photobiol. B Biol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kalaji, H.M., Jajoo, A., Oukarroum, A., Brestic, M., Zivcak, M., Samborska, I.A., Cetner, M.D., \u0141ukasik, I., Goltsev, V., and Ladle, R.J. (2014). The Use of Chlorophyll Fluorescence Kinetics Analysis to Study the Performance of Photosynthetic Machinery in Plants, Academic Press.","DOI":"10.1016\/B978-0-12-800875-1.00015-6"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cheng, T., Song, R., Li, D., Zhou, K., Zheng, H., Yao, X., Tian, Y., Cao, W., and Zhu, Y. (2017). Spectroscopic Estimation of Biomass in Canopy Components of Paddy Rice Using Dry Matter and Chlorophyll Indices. Remote Sens., 9.","DOI":"10.3390\/rs9040319"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"B\u00e9gu\u00e9, A., Arvor, D., Bellon, B., Betbeder, J., de Abelleyra, D., Ferraz, R.P.D., Lebourgeois, V., Lelong, C., Sim\u00f5es, M., and Ver\u00f3n, S.R. (2018). Remote Sensing and Cropping Practices: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10010099"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"153161","DOI":"10.1016\/j.jplph.2020.153161","article-title":"High Resolution Leaf Spectral Signature as a Tool for Foliar Pigment Estimation Displaying Potential for Species Differentiation","volume":"249","author":"Falcioni","year":"2020","journal-title":"J. Plant Physiol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Falcioni, R., Oliveira, R.B.d., Chicati, M.L., Antunes, W.C., Dematt\u00ea, J.A.M., and Nanni, M.R. (2024). Estimation of Biochemical Compounds in Tradescantia Leaves Using VIS-NIR-SWIR Hyperspectral and Chlorophyll a Fluorescence Sensors. Remote Sens., 16.","DOI":"10.3390\/rs16111910"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shurygin, B., Chivkunova, O., Solovchenko, O., Solovchenko, A., Dorokhov, A., Smirnov, I., Astashev, M.E., and Khort, D. (2021). Comparison of the Noninvasive Monitoring of Fresh-Cut Lettuce Condition with Imaging Reflectance Hyperspectrometer and Imaging PAM-Fluorimeter. Photonics, 8.","DOI":"10.20944\/preprints202109.0049.v1"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s13007-019-0448-2","article-title":"Predicting the Quality of Ryegrass Using Hyperspectral Imaging","volume":"15","author":"Shorten","year":"2019","journal-title":"Plant Methods"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"83","DOI":"10.31545\/intagr\/147227","article-title":"Hyperspectral Imaging Coupled with Multivariate Analysis and Artificial Intelligence to the Classification of Maize Kernels","volume":"36","author":"Alimohammadi","year":"2022","journal-title":"Int. Agrophys."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2069","DOI":"10.1093\/pcp\/pcp127","article-title":"Sodmergen Arrested Differentiation of Proplastids into Chloroplasts in Variegated Leaves Characterized by Plastid Ultrastructure and Nucleoid Morphology","volume":"50","author":"Sakamoto","year":"2009","journal-title":"Plant Cell Physiol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Falcioni, R., Gon\u00e7alves, J.V.F., Oliveira, K.M.d., Antunes, W.C., and Nanni, M.R. (2022). VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce. Remote Sens., 14.","DOI":"10.3390\/rs14246330"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.jphotobiol.2017.11.023","article-title":"Noninvasive Quantification of Foliar Pigments: Possibilities and Limitations of Reflectance- and Absorbance-Based Approaches","volume":"178","author":"Gitelson","year":"2018","journal-title":"J. Photochem. Photobiol. B Biol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sobejano-Paz, V., Mikkelsen, T.N., Baum, A., Mo, X., Liu, S., K\u00f6ppl, C.J., Johnson, M.S., Gulyas, L., and Garc\u00eda, M. (2020). Hyperspectral and Thermal Sensing of Stomatal Conductance, Transpiration, and Photosynthesis for Soybean and Maize under Drought. Remote Sens., 12.","DOI":"10.3390\/rs12193182"},{"key":"ref_19","unstructured":"Jacquemoud, S., and Ustin, S. (2019). Applications of Leaf Optics, Cambridge University Press. [1st ed.]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"677","DOI":"10.2307\/2657068","article-title":"Leaf Optical Properties in Higher Plants: Linking Spectral Characteristics to Stress and Chlorophyll Concentration","volume":"88","author":"Carter","year":"2001","journal-title":"Am. J. Bot."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, K.-Y., de Lima, R., Burnside, N.G., Vahtm\u00e4e, E., Kutser, T., Sepp, K., Cabral Pinheiro, V.H., Yang, M.-D., Vain, A., and Sepp, K. (2022). Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation. Remote Sens., 14.","DOI":"10.3390\/rs14051114"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4373","DOI":"10.1093\/jxb\/erab118","article-title":"Predicting Photosynthetic Capacity in Tobacco Using Shortwave Infrared Spectral Reflectance","volume":"72","author":"Sexton","year":"2021","journal-title":"J. Exp. Bot."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1016\/j.cub.2012.06.039","article-title":"A Molecular Framework of Light-Controlled Phytohormone Action in Arabidopsis","volume":"22","author":"Zhong","year":"2012","journal-title":"Curr. Biol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hassanzadeh, A., Murphy, S.P., Pethybridge, S.J., and van Aardt, J. (2020). Growth Stage Classification and Harvest Scheduling of Snap Bean Using Hyperspectral Sensing: A Greenhouse Study. Remote Sens., 12.","DOI":"10.3390\/rs12223809"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Falcioni, R., Santos, G.L.A.A.d., Crusiol, L.G.T., Antunes, W.C., Chicati, M.L., Oliveira, R.B.d., Dematt\u00ea, J.A.M., and Nanni, M.R. (2023). Non-invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy. Plants, 12.","DOI":"10.3390\/plants12132526"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ling, B., Goodin, D.G., Raynor, E.J., and Joern, A. (2019). Hyperspectral Analysis of Leaf Pigments and Nutritional Elements in Tallgrass Prairie Vegetation. Front. Plant Sci., 10.","DOI":"10.3389\/fpls.2019.00142"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.rse.2017.03.004","article-title":"PROSPECT-D: Toward Modelling Leaf Optical Properties through a Complete Lifecycle","volume":"193","author":"Gitelson","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"153277","DOI":"10.1016\/j.jplph.2020.153277","article-title":"Foliar Absorption Coefficient Derived from Reflectance Spectra: A Gauge of the Efficiency of in Situ Light-Capture by Different Pigment Groups","volume":"254","author":"Gitelson","year":"2020","journal-title":"J. Plant Physiol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Falcioni, R., Antunes, W.C., Oliveira, R.B.d., Chicati, M.L., Dematt\u00ea, J.A.M., and Nanni, M.R. (2024). Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants. Cells, 13.","DOI":"10.3390\/cells13110952"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Falcioni, R., Gon\u00e7alves, J.V.F., Oliveira, K.M.d., Oliveira, C.A.d., Dematt\u00ea, J.A.M., Antunes, W.C., and Nanni, M.R. (2023). Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms. Plants, 12.","DOI":"10.3390\/plants12061333"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4869","DOI":"10.3390\/s90604869","article-title":"Applications of Remote Sensing to Alien Invasive Plant Studies","volume":"9","author":"Huang","year":"2009","journal-title":"Sensors"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bloem, E., Gerighausen, H., Chen, X., and Schnug, E. (2020). The Potential of Spectral Measurements for Identifying Glyphosate Application to Agricultural Fields. Agronomy, 10.","DOI":"10.3390\/agronomy10091409"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Boshkovski, B., Doupis, G., Zapolska, A., Kalaitzidis, C., and Koubouris, G. (2022). Hyperspectral Imagery Detects Water Deficit and Salinity Effects on Photosynthesis and Antioxidant Enzyme Activity of Three Greek Olive Varieties. Sustainability, 14.","DOI":"10.3390\/su14031432"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1104\/pp.16.01447","article-title":"High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance","volume":"173","author":"Yendrek","year":"2017","journal-title":"Plant Physiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3765","DOI":"10.3390\/s110403765","article-title":"Hyperspectral and Chlorophyll Fluorescence Imaging to Analyse the Impact of Fusarium culmorum on the Photosynthetic Integrity of Infected Wheat Ears","volume":"11","author":"Bauriegel","year":"2011","journal-title":"Sensors"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/s13007-019-0450-8","article-title":"High-Throughput Analysis of Leaf Physiological and Chemical Traits with VIS\u2013NIR\u2013SWIR Spectroscopy: A Case Study with a Maize Diversity Panel","volume":"15","author":"Ge","year":"2019","journal-title":"Plant Methods"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1007\/s11119-022-09876-5","article-title":"Strategies for Monitoring Within-Field Soybean Yield Using Sentinel-2 Vis-NIR-SWIR Spectral Bands and Machine Learning Regression Methods","volume":"23","author":"Crusiol","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1590\/S1677-04202004000300002","article-title":"Narrow Band Spectral Indexes for Chlorophyll Determination in Soybean Canopies [Glycine max (L.) Merril]","volume":"16","author":"Ferri","year":"2004","journal-title":"Braz. J. Plant Physiol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zheng, W., Lu, X., Li, Y., Li, S., and Zhang, Y. (2021). Hyperspectral Identification of Chlorophyll Fluorescence Parameters of Suaeda salsa in Coastal Wetlands. Remote Sens., 13.","DOI":"10.3390\/rs13112066"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fernandes, A.M., Fortini, E.A., M\u00fcller, L.A.d.C., Batista, D.S., Vieira, L.M., Silva, P.O., Amaral, C.H.d., Poethig, R.S., and Otoni, W.C. (2020). Leaf Development Stages and Ontogenetic Changes in Passionfruit (Passiflora edulis Sims.) Are Detected by Narrowband Spectral Signal. J. Photochem. Photobiol. B Biol., 209.","DOI":"10.1016\/j.jphotobiol.2020.111931"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1080\/11263504.2017.1403392","article-title":"Chlorophyll-a Fluorescence Evaluation of PEG-Induced Osmotic Stress on PSII Activity in Arabidopsis Plants Expressing SIP1","volume":"152","author":"Gururani","year":"2018","journal-title":"Plant Biosyst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Crusiol, L.G.T., Sun, L., Sun, Z., Chen, R., Wu, Y., Ma, J., and Song, C. (2022). In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data. Sustainability, 14.","DOI":"10.3390\/su14159039"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"108089","DOI":"10.1016\/j.agwat.2022.108089","article-title":"Assessing the Sensitive Spectral Bands for Soybean Water Status Monitoring and Soil Moisture Prediction Using Leaf-Based Hyperspectral Reflectance","volume":"277","author":"Crusiol","year":"2023","journal-title":"Agric. Water Manag."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2659","DOI":"10.1111\/nph.17947","article-title":"Automated Hyperspectral Vegetation Index Derivation Using a Hyperparameter Optimisation Framework for High-Throughput Plant Phenotyping","volume":"233","author":"Koh","year":"2022","journal-title":"New Phytol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Falcioni, R., Antunes, W.C., Dematt\u00ea, J.A.M., and Nanni, M.R. (2023). Biophysical, Biochemical, and Photochemical Analyses Using Reflectance Hyperspectroscopy and Chlorophyll a Fluorescence Kinetics in Variegated Leaves. Biology, 12.","DOI":"10.3390\/biology12050704"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.jplph.2017.08.009","article-title":"In Situ Optical Properties of Foliar Flavonoids: Implication for Nondestructive Estimation of Flavonoid Content","volume":"218","author":"Gitelson","year":"2017","journal-title":"J. Plant Physiol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1016\/j.foodchem.2014.09.119","article-title":"Predicting the Anthocyanin Content of Wine Grapes by NIR Hyperspectral Imaging","volume":"172","author":"Chen","year":"2015","journal-title":"Food Chem."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kumar, A., Kashyap, Y., and Kosmopoulos, P. (2023). Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach. Remote Sens., 15.","DOI":"10.3390\/rs15010107"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/cem.877","article-title":"Combination of Support Vector Machines (SVM) and near-Infrared (NIR) Imaging Spectroscopy for the Detection of Meat and Bone Meal (MBM) in Compound Feeds","volume":"18","author":"Pierna","year":"2004","journal-title":"J. Chemom."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Nalepa, J. (2021). Recent Advances in Multi- and Hyperspectral Image Analysis. Sensors, 21.","DOI":"10.3390\/s21186002"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s11119-020-09740-4","article-title":"Vegetation Indices and NIR-SWIR Spectral Bands as a Phenotyping Tool for Water Status Determination in Soybean","volume":"22","author":"Braga","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Cezar, E., Nanni, M.R., Crusiol, L.G.T., Sun, L., Chicati, M.S., Furlanetto, R.H., Rodrigues, M., Sibaldelli, R.N.R., Silva, G.F.C., and de Oliveira, K.M. (2021). Strategies for the Development of Spectral Models for Soil Organic Matter Estimation. Remote Sens., 13.","DOI":"10.3390\/rs13071376"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"de Oliveira, K.M., Falcioni, R., Gon\u00e7alves, J.V.F., de Oliveira, C.A., Mendon\u00e7a, W.A., Crusiol, L.G.T., de Oliveira, R.B., Furlanetto, R.H., Reis, A.S., and Nanni, M.R. (2023). Rapid Determination of Soil Horizons and Suborders Based on VIS-NIR-SWIR Spectroscopy and Machine Learning Models. Remote Sens., 15.","DOI":"10.3390\/rs15194859"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"103378","DOI":"10.1016\/j.vibspec.2022.103378","article-title":"ATR-FTIR Spectroscopy Combined With Machine Learning For Classification of PVA\/PVP Blends in Low Concentration","volume":"120","author":"Franca","year":"2022","journal-title":"Vib. Spectrosc."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Falcioni, R., Antunes, W.C., Dematt\u00ea, J.A.M., and Nanni, M.R. (2023). Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops. Plants, 12.","DOI":"10.3390\/plants12122347"},{"key":"ref_56","first-page":"101362","article-title":"Classification of Soil Horizons Based on VisNIR and SWIR Hyperespectral Images and Machine Learning Models","volume":"36","author":"Falcioni","year":"2024","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4175","DOI":"10.1364\/BOE.9.004175","article-title":"Analysis and Classification of Kidney Stones Based on Raman Spectroscopy","volume":"9","author":"Cui","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_58","first-page":"1","article-title":"Digital Mapping of Soil Carbon","volume":"Volume 3","author":"Minasny","year":"2013","journal-title":"Advances in Agronomy"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Falcioni, R., Gon\u00e7alves, J.V.F., de Oliveira, K.M., de Oliveira, C.A., Reis, A.S., Crusiol, L.G.T., Furlanetto, R.H., Antunes, W.C., Cezar, E., and de Oliveira, R.B. (2023). Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy. Plants, 12.","DOI":"10.3390\/plants12193424"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Baio, F.H.R., Santana, D.C., Teodoro, L.P.R., de Oliveira, I.C., Gava, R., de Oliveira, J.L.G., Silva Junior, C.A.d., Teodoro, P.E., and Shiratsuchi, L.S. (2023). Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management. Remote Sens., 15.","DOI":"10.3390\/rs15010079"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Jin, J., Huang, N., Huang, Y., Yan, Y., Zhao, X., and Wu, M. (2022). Proximal Remote Sensing-Based Vegetation Indices for Monitoring Mango Tree Stem Sap Flux Density. Remote Sens., 14.","DOI":"10.3390\/rs14061483"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.envexpbot.2017.06.001","article-title":"Distinct Growth Light and Gibberellin Regimes Alter Leaf Anatomy and Reveal Their Influence on Leaf Optical Properties","volume":"140","author":"Falcioni","year":"2017","journal-title":"Environ. Exp. Bot."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Fu, Y., Yang, G., Song, X., Li, Z., Xu, X., Feng, H., and Zhao, C. (2021). Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13040581"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.compag.2016.07.028","article-title":"Temporal Dynamics of Maize Plant Growth, Water Use, and Leaf Water Content Using Automated High Throughput RGB and Hyperspectral Imaging","volume":"127","author":"Ge","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Kycko, M., Zagajewski, B., Lavender, S., and Dabija, A. (2019). In Situ Hyperspectral Remote Sensing for Monitoring of Alpine Trampled and Recultivated Species. Remote Sens., 11.","DOI":"10.3390\/rs11111296"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Hu, Y., Wang, Z., Li, X., Li, L., Wang, X., and Wei, Y. (2022). Nondestructive Classification of Maize Moldy Seeds by Hyperspectral Imaging and Optimal Machine Learning Algorithms. Sensors, 22.","DOI":"10.3390\/s22166064"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Iqbal, I.M., Balzter, H., and Shabbir, A. (2021). Identifying the Spectral Signatures of Invasive and Native Plant Species in Two Protected Areas of Pakistan through Field Spectroscopy. Remote Sens., 13.","DOI":"10.3390\/rs13194009"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Jin, J., Arief Pratama, B., and Wang, Q. (2020). Tracing Leaf Photosynthetic Parameters Using Hyperspectral Indices in an Alpine Deciduous Forest. Remote Sens., 12.","DOI":"10.3390\/rs12071124"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"e03362","DOI":"10.1002\/ecs2.3362","article-title":"Exploring the Links between Secondary Metabolites and Leaf Spectral Reflectance in a Diverse Genus of Amazonian Trees","volume":"12","author":"Fine","year":"2021","journal-title":"Ecosphere"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.talanta.2017.02.008","article-title":"Linking ATR-FTIR and Raman Features to Phenolic Extractability and Other Attributes in Grape Skin","volume":"167","author":"Rooney","year":"2017","journal-title":"Talanta"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1111\/j.1365-3040.2011.02402.x","article-title":"Changes in Leaf Optical Properties Associated with Light-Dependent Chloroplast Movements","volume":"34","author":"Davis","year":"2011","journal-title":"Plant Cell Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Tsimilli-Michael, M., and Strasser, R.J. (2008). In Vivo Assessment of Stress Impact on Plant\u2019s Vitality: Applications in Detecting and Evaluating the Beneficial Role of Mycorrhization on Host Plants. Mycorrhiza, Springer.","DOI":"10.1007\/978-3-540-78826-3_32"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1111\/pce.13544","article-title":"Predicting Dark Respiration Rates of Wheat Leaves from Hyperspectral Reflectance","volume":"42","author":"Coast","year":"2019","journal-title":"Plant Cell Environ."},{"key":"ref_74","first-page":"e20207739","article-title":"Advances in Hyperspectral Sensing in Agriculture: A Review","volume":"51","author":"Ribeiro","year":"2020","journal-title":"Rev. Cienc. Agron."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Brezini, S.E., and Deville, Y. (2023). Hyperspectral and Multispectral Image Fusion with Automated Extraction of Image-Based Endmember Bundles and Sparsity-Based Unmixing to Deal with Spectral Variability. Sensors, 23.","DOI":"10.3390\/s23042341"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1002\/jsfa.8002","article-title":"Nondestructive Prediction of Pigment Content in Lettuce Based on Visible-NIR Spectroscopy","volume":"97","author":"Moura","year":"2017","journal-title":"J. Sci. Food Agric."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Yu, L., Zhang, X., Liu, Y., Zhan, Z., Ren, L., and Luo, Y. (2022). Fusion of UAV Hyperspectral Imaging and LiDAR for the Early Detection of EAB Stress in Ash and a New EAB Detection Index NDVI(776,678). Remote Sens., 14.","DOI":"10.3390\/rs14102428"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1071\/AR07282","article-title":"Monitoring Leaf Pigment Status with Hyperspectral Remote Sensing in Wheat","volume":"59","author":"Feng","year":"2008","journal-title":"Aust. J. Agric. Res."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Fan, K., Li, F., Chen, X., Li, Z., and Mulla, D.J. (2022). Nitrogen Balance Index Prediction of Winter Wheat by Canopy Hyperspectral Transformation and Machine Learning. Remote Sens., 14.","DOI":"10.3390\/rs14143504"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"104934","DOI":"10.1016\/j.envint.2019.104934","article-title":"A Comparison of Linear Regression, Regularization, and Machine Learning Algorithms to Develop Europe-Wide Spatial Models of Fine Particles and Nitrogen Dioxide","volume":"130","author":"Chen","year":"2019","journal-title":"Environ. Int."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"7300","DOI":"10.3390\/s8117300","article-title":"Can Commercial Digital Cameras Be Used as Multispectral","volume":"8","author":"Lebourgeois","year":"2008","journal-title":"Sensors"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1111\/php.13273","article-title":"Influence of Surface Structure, Pigmentation and Particulate Matter on Plant Reflectance and Fluorescence","volume":"97","author":"Cuba","year":"2021","journal-title":"Photochem. Photobiol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6490\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:09:53Z","timestamp":1760112593000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6490"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,9]]},"references-count":82,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196490"],"URL":"https:\/\/doi.org\/10.3390\/s24196490","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,10,9]]}}}