{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T16:28:54Z","timestamp":1780417734016,"version":"3.54.1"},"reference-count":93,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T00:00:00Z","timestamp":1611273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Aeronautics and Space Administration","award":["NNX15AK03H"],"award-info":[{"award-number":["NNX15AK03H"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, \u221292.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400\u20131000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial\u2013spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900\u2013940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400\u2013700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.<\/jats:p>","DOI":"10.3390\/s21030742","type":"journal-article","created":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T11:13:53Z","timestamp":1611314033000},"page":"742","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":249,"title":["Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"given":"Canh","family":"Nguyen","sequence":"first","affiliation":[{"name":"Geospatial Institute, Saint Louis University, Saint Louis, MO 63108, USA"},{"name":"Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4375-2096","authenticated-orcid":false,"given":"Vasit","family":"Sagan","sequence":"additional","affiliation":[{"name":"Geospatial Institute, Saint Louis University, Saint Louis, MO 63108, USA"},{"name":"Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4241-6181","authenticated-orcid":false,"given":"Matthew","family":"Maimaitiyiming","sequence":"additional","affiliation":[{"name":"Division of Food Sciences, University of Missouri, Columbia, MO 65211, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6153-1583","authenticated-orcid":false,"given":"Maitiniyazi","family":"Maimaitijiang","sequence":"additional","affiliation":[{"name":"Geospatial Institute, Saint Louis University, Saint Louis, MO 63108, USA"},{"name":"Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5832-4695","authenticated-orcid":false,"given":"Sourav","family":"Bhadra","sequence":"additional","affiliation":[{"name":"Geospatial Institute, Saint Louis University, Saint Louis, MO 63108, USA"},{"name":"Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3097-9638","authenticated-orcid":false,"given":"Misha T.","family":"Kwasniewski","sequence":"additional","affiliation":[{"name":"Division of Food Sciences, University of Missouri, Columbia, MO 65211, USA"},{"name":"Department of Food Sciences, The Pennsylvania State University, University Park, PA 16802, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1080\/15427528.2014.865412","article-title":"Climate change impacts on plant pathogens and plant diseases","volume":"28","author":"Elad","year":"2014","journal-title":"J. Crop Improv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s13007-017-0233-z","article-title":"Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress","volume":"13","author":"Lowe","year":"2017","journal-title":"Plant Methods"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Qiu, W., and Schoelz, J. (2017). Grapevine vein clearing virus: Diagnostics, genome, genetic diversity, and management. Grapevine Viruses: Molecular Biology, Diagnostics and Management, Springer.","DOI":"10.1007\/978-3-319-57706-7_15"},{"key":"ref_4","unstructured":"Jones, H.G., and Vaughan, R.A. (2010). Remote Sensing of Vegetation: Principles, Techniques, and Applications, Oxford University Press."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1146\/annurev.phyto.41.121702.103726","article-title":"The potential of optical canopy measurement for targeted control of field crop diseases","volume":"41","author":"West","year":"2003","journal-title":"Annu. Rev. Phytopathol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3937","DOI":"10.1093\/jxb\/ert029","article-title":"Thermography to explore plant\u2013environment interactions","volume":"64","author":"Costa","year":"2013","journal-title":"J. Exp. Bot."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sagan, V., Maimaitijiang, M., Sidike, P., Eblimit, K., Peterson, K.T., Hartling, S., Esposito, F., Khanal, K., Necomb, M., and Pauli, D. (2019). UAV-Based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 p, FLIR Vue Pro R 640, and thermomap cameras. Remote Sens., 11.","DOI":"10.3390\/rs11030330"},{"key":"ref_8","unstructured":"Meroni, M., Rossini, M., and Colombo, R. (2010). Characterization of leaf physiology using reflectance and fluorescence hyperspectral measurements. Optical observation of vegetation properties and characteristics. Res. Signpost, 165\u2013187."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11119-007-9036-y","article-title":"Multi-Temporal wheat disease detection by multi-spectral remote sensing","volume":"8","author":"Franke","year":"2007","journal-title":"Precis. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13593-014-0246-1","article-title":"Advanced methods of plant disease detection. A review","volume":"35","author":"Martinelli","year":"2015","journal-title":"Agron. Sustain. Dev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2012.09.019","article-title":"Development of spectral indices for detecting and identifying plant diseases","volume":"128","author":"Mahlein","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s11119-010-9180-7","article-title":"Spectral signatures of sugar beet leaves for the detection and differentiation of diseases","volume":"11","author":"Mahlein","year":"2010","journal-title":"Precis. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Roscher, R., Behmann, J., Mahlein, A.K., Dupuis, J., Kuhlmann, H., and Pl\u00fcmer, L. (2016, January 12\u201319). Detection of Disease Symptoms on Hyperspectral 3D Plant Models. Proceedings of the ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic.","DOI":"10.5194\/isprsannals-III-7-89-2016"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4344","DOI":"10.1109\/JSTARS.2016.2575360","article-title":"An Investigation into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement","volume":"9","author":"Ashourloo","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.compag.2010.12.006","article-title":"Early detection of Fusarium infection in wheat using hyper-spectral imaging","volume":"75","author":"Bauriegel","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.eja.2007.02.005","article-title":"Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications","volume":"27","author":"Delalieux","year":"2007","journal-title":"Eur. J. Agron."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Behmann, J., Acebron, K., Emin, D., Bennertz, S., Matsubara, S., Thomas, S., Bohnenkamp, D., Kuska, M.T., Jussila, J., and Salo, H. (2018). Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection. Sensors, 18.","DOI":"10.3390\/s18020441"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1071\/FP16127","article-title":"Observation of plant\u2013pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements","volume":"44","author":"Thomas","year":"2017","journal-title":"Funct. Plant Biol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1080\/01431160701281007","article-title":"Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN)","volume":"29","author":"Wang","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ijfoodmicro.2010.08.001","article-title":"Early detection of toxigenic fungi on maize by hyperspectral imaging analysis","volume":"144","author":"Reverberi","year":"2010","journal-title":"Int. J. Food Microbiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S1537-5110(02)00269-6","article-title":"Early disease detection in wheat fields using spectral reflectance","volume":"84","author":"Bravo","year":"2003","journal-title":"Biosyst. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1080\/01431160310001618031","article-title":"Detecting sugarcane \u2019orange rust\u2019 disease using EO-1 Hyperion hyperspectral imagery","volume":"25","author":"Apan","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","first-page":"1","article-title":"Hyperspectral imaging for presymptomatic detection of tobacco disease with successive projections algorithm and machine-learning classifiers","volume":"7","author":"Zhu","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.fcr.2011.02.007","article-title":"Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields","volume":"122","author":"Hillnhutter","year":"2011","journal-title":"Field Crops Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s13007-017-0198-y","article-title":"Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images","volume":"13","author":"Knauer","year":"2017","journal-title":"Plant Methods"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abdulridha, J., Batuman, O., and Ampatzidis, Y. (2019). UAV-Based remote sensing technique to detect citrus canker disease utilizing hyperspectral imaging and machine learning. Remote Sens., 11.","DOI":"10.3390\/rs11111373"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Albetis, J., Duthoit, S., Guttler, F., Jacquin, A., Goulard, M., Poilv\u00e9, H., F\u00e9ret, J.B., and Dedieu, G. (2017). Detection of Flavescence dor\u00e9e Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040308"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Albetis, J., Jacquin, A., Goulard, M., Poilv\u00e9, H., Rousseau, J., Clenet, H., Dedieu, G., and Duthoit, S. (2019). On the Potentiality of UAV Multispectral Imagery to Detect Flavescence dor\u00e9e and Grapevine Trunk Diseases. Remote Sens., 11.","DOI":"10.3390\/rs11010023"},{"key":"ref_29","first-page":"262","article-title":"Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex","volume":"55","author":"Battiston","year":"2016","journal-title":"Phytopathologia Mediterranea"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5529","DOI":"10.1093\/jxb\/erw318","article-title":"Hyperspectral phenotyping of the reaction of grapevine genotypes to Plasmopara viticola","volume":"67","author":"Oerke","year":"2016","journal-title":"J. Exp. Bot."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105807","DOI":"10.1016\/j.compag.2020.105807","article-title":"Early detection of grapevine leafroll disease in a red-berried wine grape cultivar using hyperspectral imaging","volume":"179","author":"Gao","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.compag.2016.10.003","article-title":"Remote hyperspectral imaging of grapevine leafroll-associated virus 3 in cabernet sauvignon vineyards","volume":"130","author":"MacDonald","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Al-Saddik, H., Laybros, A., Billiot, B., and Cointault, F. (2018). Using image texture and spectral reflectance analysis to detect Yellowness and Esca in grapevines at leaf-level. Remote Sens., 10.","DOI":"10.3390\/rs10040618"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bhadra, S., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Newcomb, M., Shakoor, N., and Mockler, T.C. (2020). Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning. Remote Sens., 12.","DOI":"10.3390\/rs12132082"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Maimaitiyiming, M., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Newcomb, M., Shakoor, N., and Mockler, T.C. (2019). Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality. Remote Sens., 11.","DOI":"10.3390\/rs11070740"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xie, C., and He, Y. (2016). Spectrum and image texture features analysis for early blight disease detection on eggplant leaves. Sensors, 16.","DOI":"10.3390\/s16050676"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s41348-020-00344-8","article-title":"Hyperspectral imaging of symptoms induced by Rhizoctonia solani in sugar beet: Comparison of input data and different machine learning algorithms","volume":"127","author":"Barreto","year":"2020","journal-title":"J. Plant Dis. Prot."},{"key":"ref_39","first-page":"182","article-title":"Detection of scab in wheat ears using in situ hyperspectral data and support vector machine optimized by genetic algorithm","volume":"13","author":"Huang","year":"2020","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"389","DOI":"10.5721\/EuJRS20144723","article-title":"A review of remote sensing image classification techniques: The role of spatio-contextual information","volume":"47","author":"Li","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, F., and Xiao, Z. (2020, January 27\u201329). Disease Spots Identification of Potato Leaves in Hyperspectral Based on Locally Adaptive 1D-CNN. Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China.","DOI":"10.1109\/ICAICA50127.2020.9182577"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Jin, X., Jie, L., Wang, S., Qi, H.J., and Li, S.W. (2018). Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sens., 10.","DOI":"10.3390\/rs10030395"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hru\u0161ka, J., Ad\u00e3o, T., P\u00e1dua, L., Marques, P., Peres, E., Sousa, A., Morais, R., and Sousa, J.J. (2018, January 22\u201327). Deep Learning-Based Methodological Approach for Vineyard Early Disease Detection Using Hyperspectral Data. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519136"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s13007-019-0479-8","article-title":"Plant disease identification using explainable 3D deep learning on hyperspectral images","volume":"15","author":"Nagasubramanian","year":"2019","journal-title":"Plant Methods"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, L., Dong, Y., Shi, Y., Huang, W., Han, L., Gonzalez-Moreno, P., Ma, H., Ye, H., and Sobeih, T. (2019). A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral uav images. Remote Sens., 11.","DOI":"10.3390\/rs11131554"},{"key":"ref_47","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 Hyperspectral-Based Plant Disease Detection in the Past Three Decades. Remote Sens., 12.","DOI":"10.3390\/rs12193188"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-1483-2020","article-title":"Uav Images and Deep-Learning Algorithms for Detecting Flavescence Doree Disease in Grapevine Orchards","volume":"43","author":"Musci","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Saleem, M.H., Potgieter, J., and Arif, K.M. (2019). Plant Disease Detection and Classification by Deep Learning. Plants, 8.","DOI":"10.3390\/plants8110468"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1109\/TPAMI.2003.1177153","article-title":"Lambertian reflectance and linear subspaces. IEEE Trans","volume":"25","author":"Basri","year":"2003","journal-title":"Pattern Anal. Mach. Intell."},{"key":"ref_51","first-page":"401","article-title":"A new nonparametric Levene test for equal variances","volume":"31","author":"Nordstokke","year":"2010","journal-title":"Psicologica"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/1011-1344(93)06963-4","article-title":"Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves","volume":"22","author":"Gitelson","year":"1994","journal-title":"J. Photochem. Photobiol. B Biol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_55","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_56","first-page":"221","article-title":"Semi-Empirical indices to assess carotenoids\/chlorophyll a ratio from leaf spectral reflectance","volume":"31","author":"Penuelas","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.13031\/2013.27665","article-title":"Remote Sensing of Plant Nitrogen Status in Corn","volume":"39","author":"Bausch","year":"1996","journal-title":"Trans. ASAE"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/0034-4257(92)90089-3","article-title":"Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves","volume":"39","author":"Chappelle","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1080\/014311698215919","article-title":"Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves","volume":"19","author":"Blackburn","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0034-4257(94)90136-8","article-title":"Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves","volume":"48","author":"Gamon","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1080\/01431160210163074","article-title":"Vegetation indices derived from high-resolution airborne videography for precision crop management","volume":"24","author":"Metternicht","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Schell","year":"1973","journal-title":"NASA Spec. Publ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1080\/014311697217558","article-title":"Remote estimation of chlorophyll content in higher plant leaves","volume":"18","author":"Gitelson","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf-area index from quality of light on the forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_68","unstructured":"Barnes, E., Clarke, T.R., Richards, S.E., Colaizzi, P.D., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T. (2000, January 16\u201319). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/S0034-4257(00)00149-8","article-title":"Chlorophyll fluorescence effects on vegetation apparent reflectance: II. Laboratory and airborne canopy-level measurements with hyperspectral data","volume":"74","author":"Miller","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/S0034-4257(00)00148-6","article-title":"Chlorophyll fluorescence effects on vegetation apparent reflectance: I. Leaf-level measurements and model simulation","volume":"74","author":"Miller","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.rse.2005.05.006","article-title":"Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale","volume":"97","author":"Dobrowski","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/0098-8472(92)90034-Y","article-title":"A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants","volume":"32","author":"Barnes","year":"1992","journal-title":"Environ. Exp. Bot."},{"key":"ref_75","unstructured":"Merton, R. (1998, January 12\u201316). Monitoring community hysteresis using spectral shift analysis and the red-edge vegetation stress index. Proceedings of the Seventh Annual JPL Airborne Earth Science Workshop, Pasadena, CA, USA."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1080\/01431169308954010","article-title":"The reflectance at the 950\u2013970 nm region as an indicator of plant water status","volume":"14","author":"Filella","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.2135\/cropsci2005.0211","article-title":"Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat","volume":"46","author":"Babar","year":"2006","journal-title":"Crop Sci."},{"key":"ref_78","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_79","first-page":"697","article-title":"Hyperspectral remote sensing of vegetation and agricultural crops","volume":"80","author":"Thenkabail","year":"2014","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Morellos, A., Tziotzios, G., Orfanidou, C., Pantazi, X.E., Sarantaris, C., Maliogka, V., Alexandridis, T.K., and Moshou, D. (2020). Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis-NIR Spectroscopy. Remote Sens., 12.","DOI":"10.3390\/rs12121920"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1093\/jexbot\/51.345.659","article-title":"Chlorophyll fluorescence\u2014A practical guide","volume":"51","author":"Maxwell","year":"2000","journal-title":"J. Exp. Bot."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1034\/j.1399-3054.1999.106119.x","article-title":"Non-Destructive optical detection of pigment changes during leaf senescence and fruit ripening","volume":"106","author":"Merzlyak","year":"1999","journal-title":"Physiologia Plantarum"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2020.02.010","article-title":"Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis","volume":"162","author":"Poblete","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Sagan, V., Maimaitiyiming, M., and Fishman, J. (2018). Effects of Ambient Ozone on Soybean Biophysical Variables and Mineral Nutrient Accumulation. Remote Sens., 10.","DOI":"10.3390\/rs10040562"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.compag.2018.12.036","article-title":"Detection of peanut leaf spots disease using canopy hyperspectral reflectance","volume":"156","author":"Chen","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Moghadam, P., Ward, D., Goan, E., Jayawardena, S., Sikka, P., and Hernandez, E. (December, January 29). Plant Disease Detection Using Hyperspectral Imaging. Proceedings of the 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia.","DOI":"10.1109\/DICTA.2017.8227476"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.agrformet.2008.07.007","article-title":"A model for chlorophyll fluorescence and photosynthesis at leaf scale","volume":"149","author":"Verhoef","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Lang, W., Chen, X., Liang, L., Ren, S., and Qian, S. (2019). Geographic and Climatic Attributions of Autumn Land Surface Phenology Spatial Patterns in the Temperate Deciduous Broadleaf Forest of China. Remote Sens., 11.","DOI":"10.3390\/rs11131546"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1007\/s42161-019-00334-2","article-title":"Identification of wheat powdery mildew using in-situ hyperspectral data and linear regression and support vector machines","volume":"101","author":"Huang","year":"2019","journal-title":"J. Plant Pathol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s41324-019-00302-z","article-title":"Extraction of onion fields infected by anthracnose-twister disease in selected municipalities of Nueva Ecija using UAV imageries","volume":"28","author":"Alberto","year":"2020","journal-title":"Spat. Inf. Res."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Zhang, D., Wang, Q., Lin, F., Yin, X., Gu, C., and Qiao, H. (2020). Development and Evaluation of a New Spectral Disease Index to Detect Wheat Fusarium Head Blight Using Hyperspectral Imaging. Sensors, 20.","DOI":"10.3390\/s20082260"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"105221","DOI":"10.1016\/j.compag.2020.105221","article-title":"Hyperspectral remote sensing for assessment of chlorophyll sufficiency levels in mature oil palm (Elaeis guineensis) based on frond numbers: Analysis of decision tree and random forest","volume":"169","author":"Amirruddin","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Mondal, M., Muharam, F.M., Ismail, M.H., Ismail, M.F., Tan, N.P., and Karam, D.S. (2019, January 6\u20138). Plants Stress Response Detection by Selecting Minimal Bands of Hyperspectral Images. Proceedings of the 9th International Conference on Advances in Computing and Communication (ICACC), Rajagiri Valley, India.","DOI":"10.1109\/ICACC48162.2019.8986161"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/742\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:14:12Z","timestamp":1760159652000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/742"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,22]]},"references-count":93,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030742"],"URL":"https:\/\/doi.org\/10.3390\/s21030742","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,22]]}}}