{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:41:46Z","timestamp":1776357706096,"version":"3.51.2"},"reference-count":76,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,6,8]],"date-time":"2019-06-08T00:00:00Z","timestamp":1559952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A remote sensing technique was developed to detect citrus canker in laboratory conditions and was verified in the grove by utilizing an unmanned aerial vehicle (UAV). In the laboratory, a hyperspectral (400\u20131000 nm) imaging system was utilized for the detection of citrus canker in several disease development stages (i.e., asymptomatic, early, and late symptoms) on Sugar Belle leaves and immature (green) fruit by using two classification methods: (i) radial basis function (RBF) and (ii) K nearest neighbor (KNN). The same imaging system mounted on an UAV was used to detect citrus canker on tree canopies in the orchard. The overall classification accuracy of the RBF was higher (94%, 96%, and 100%) than the KNN method (94%, 95%, and 96%) for detecting canker in leaves. Among the 31 studied vegetation indices, the water index (WI) and the Modified Chlorophyll Absorption in Reflectance Index (ARI and TCARI 1) more accurately detected canker in laboratory and in orchard conditions, respectively. Immature fruit was not a reliable tissue for early detection of canker. However, the proposed technique successfully distinguished the late stage canker-infected fruit with 92% classification accuracy. The UAV-based technique achieved 100% classification accuracy for identifying healthy and canker-infected trees.<\/jats:p>","DOI":"10.3390\/rs11111373","type":"journal-article","created":{"date-parts":[[2019,6,10]],"date-time":"2019-06-10T03:16:51Z","timestamp":1560136611000},"page":"1373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":215,"title":["UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Jaafar","family":"Abdulridha","sequence":"first","affiliation":[{"name":"Agricultural and Biological Engineering department, Southwest Florida Research and Education Center, University of Florida, IFAS, 2685 SR 29 North Immokalee, FL 34142, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3975-2317","authenticated-orcid":false,"given":"Ozgur","family":"Batuman","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, Southwest Florida Research and Education Center, University of Florida, IFAS, 2685 SR 29 North Immokalee, FL 34142, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3660-3298","authenticated-orcid":false,"given":"Yiannis","family":"Ampatzidis","sequence":"additional","affiliation":[{"name":"Agricultural and Biological Engineering department, Southwest Florida Research and Education Center, University of Florida, IFAS, 2685 SR 29 North Immokalee, FL 34142, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1094\/PD-89-0071","article-title":"Effect of stimulated wind-driven rain on duration and distance of dispersal of Xanthomonas axonompodis pv. citri from canker-infected citrus trees","volume":"89","author":"Bock","year":"2005","journal-title":"Plant Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1128\/aem.59.4.1143-1148.1993","article-title":"Detection of anthomonas-campestris pv. citri by the polymerase chain-reaction method","volume":"59","author":"Hartung","year":"1993","journal-title":"Appl. Environ. Microbiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1908","DOI":"10.1111\/mpp.12667","article-title":"Functional characterization of the citrus canker susceptibility gene CsLOB1","volume":"19","author":"Duan","year":"2018","journal-title":"Mol. Plant Pathol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s10658-010-9624-y","article-title":"Wind speed and wind-associated leaf injury affect severity of citrus canker on Swingle citrumelo","volume":"128","author":"Bock","year":"2010","journal-title":"Eur. J. Plant Pathol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gottwald, R.T., Graham, H.J., and Schubert, T.S. (2002). Citrus canker: the pathogen and its impact. Online. Plant Health Progress.","DOI":"10.1094\/PHP-2002-0812-01-RV"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1046\/j.1364-3703.2004.00197.x","article-title":"Xanthomonas axonopodis pv. citri: Factors affecting successful eradication of citrus canker","volume":"5","author":"Graham","year":"2004","journal-title":"Mol. Plant Pathol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.micres.2005.07.005","article-title":"Sensitive and specific detection of Xanthomonas axonopodis pv. citri by PCR using pathovar specific primers based on hrpW gene sequences","volume":"161","author":"Park","year":"2006","journal-title":"Microbiol. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1094\/PDIS.2004.88.7.745","article-title":"Lack of control of citrus canker by induced systemic resistance compounds","volume":"88","author":"Graham","year":"2004","journal-title":"Plant Dis."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.compag.2018.12.048","article-title":"Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence","volume":"157","author":"Partel","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.compag.2019.04.022","article-title":"Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence","volume":"162","author":"Partel","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.postharvbio.2018.05.004","article-title":"Non-destructive recognition and classification of citrus fruit blemishes based on ant colony optimized spectral information","volume":"143","author":"Zhang","year":"2018","journal-title":"Postharvest Biol. Technol."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.compag.2018.10.016","article-title":"Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado","volume":"155","author":"Abdulridha","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.compag.2017.01.017","article-title":"Field detection of anthracnose crown rot in strawberry using spectroscopy technology","volume":"135","author":"Lu","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.tplants.2018.11.007","article-title":"Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture","volume":"24","author":"Maes","year":"2019","journal-title":"Trends Plant Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ampatzidis, Y., and Partel, V. (2019). UAV-based high throughput phenotyping in citrus utilizing multispectral imaging and artificial intelligence. Remote Sens., 11.","DOI":"10.3390\/rs11040410"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"279","DOI":"10.3389\/fpls.2019.00279","article-title":"Applications of unmanned aerial vehicle based imagery in turfgrass field trials","volume":"10","author":"Zhang","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_19","unstructured":"Albetis, J., Jacquin, A., Goulard, M., Poilve, H., Rousseau, J., Clenet, H., Dedieu, G., and Duthoit, S. (2019). On the potentiality of UAV multispectral imagery to detect flavescence doree and grapevine trunk diseases. Remote Sens., 11."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Albetis, J., Duthoit, S., Guttler, F., Jacquin, A., Goulard, M., Poilve, H., Feret, J.B., and Dedieu, G. (2017). Detection of flavescence doree grapevine disease using unmanned aerial vehicle (UAV) multispectral imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040308"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.compag.2018.10.006","article-title":"Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images","volume":"155","author":"Kerkech","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Dash, J.P., Pearse, G.D., and Watt, M.S. (2018). UAV multispectral imagery can complement satellite data for monitoring forest health. Remote Sens., 10.","DOI":"10.3390\/rs10081216"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, Q., Song, H., Liu, G., Huang, C., and Li, H. (2019). Evaluating the potential of multi-seasonal CBERS-04 imagery for mapping the quasi-circular vegetation patches in the Yellow River delta using random forest. Remote Sens., 11.","DOI":"10.3390\/rs11101216"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.jfoodeng.2009.01.014","article-title":"Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence","volume":"93","author":"Qin","year":"2009","journal-title":"J. Food Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.snb.2018.08.020","article-title":"Hyperspectral reflectance imaging combined with carbohydrate metabolism analysis for diagnosis of citrus Huanglongbing in different seasons and cultivars","volume":"275","author":"Weng","year":"2018","journal-title":"Sens. Actuators B Chem."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.compag.2018.04.023","article-title":"Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection","volume":"150","author":"Sharif","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.postharvbio.2017.11.004","article-title":"From hyperspectral imaging to multispectral imaging: Portability and stability of HIS-MIS algorithms for common defect detection","volume":"137","author":"Zhang","year":"2018","journal-title":"Postharvest Biol. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.compag.2011.03.004","article-title":"Visible-near infrared spectroscopy for detection of Huanglongbing in citrus orchards","volume":"77","author":"Sankaran","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"711","DOI":"10.13031\/2013.41369","article-title":"Identification of citrus greening (HLB) using a VIS-NIR spectroscopy technique","volume":"55","author":"Mishra","year":"2012","journal-title":"Trans. ASABE"},{"key":"ref_30","first-page":"75","article-title":"Comparison of visible near infrared and mid-infrared spectroscopy for classification of Huanglongbing and citrus canker infected leaves","volume":"15","author":"Sankaran","year":"2013","journal-title":"Agric. Eng. Int. CIGR J."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"254","DOI":"10.21273\/HORTTECH.26.3.254","article-title":"Spectral characteristics of citrus black spot disease","volume":"26","author":"Pourreza","year":"2016","journal-title":"Horttechnology"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1128\/JCM.38.4.1628-1631.2000","article-title":"Molecular phylogenetic evidence for noninvasive zoonotic transmission of Staphylococcus intermedius from a canine pet to a human","volume":"38","author":"Tanner","year":"2000","journal-title":"J. Clin. Microbiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1080\/014311697217396","article-title":"Estimation of plant water concentration by the reflectance water index WI (R900\/R970)","volume":"18","author":"Penuelas","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","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_35","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_36","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/S0176-1617(96)80284-7","article-title":"Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll","volume":"148","author":"Gitelson","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/0034-4257(92)90089-3","article-title":"Ration analysis of reflectance spectra (RARS)\u2014An algorithm for the remote estimation concentration of chlorophyll-a, chlorophyll-b, and carotenoid soybean leaves","volume":"39","author":"Chappelle","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"131","DOI":"10.2134\/agronj2001.931131x","article-title":"In-season prediction of potential grain yield in winter wheat using canopy reflectance","volume":"93","author":"Raun","year":"2001","journal-title":"Agron. J."},{"key":"ref_41","first-page":"221","article-title":"Semiempirical indexes to assess carotenoids chlorophyll-a ratio from leaf spectral reflectance","volume":"31","author":"Penuelas","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_42","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":"Penuelas","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","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 chlorophylls-B in lichens and higher-plants","volume":"32","author":"Barnes","year":"1992","journal-title":"Environ. Exp. Bot."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0034-4257(94)90136-8","article-title":"Reflectance indexes associated with physiological-changes in nitrogen-limited and water-limited sunflower leaves","volume":"48","author":"Penuelas","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0034-4257(89)90046-1","article-title":"Detection of changes in leaf water content using near- and middle-infrared reflectances","volume":"30","author":"Hunt","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_48","unstructured":"Merton, R. (1998). Monitoring Community Hysteresis Using Spectral Shift Analysis and the Red-Edge Vegetation Stress Index. JPL Airborne Earth Science Workshop, NASA, Jet Propulsion Laboratory."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating par absorbed by vegetation from bidirectional reflectance measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_51","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_52","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_53","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_54","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1562\/0031-8655(2001)074<0038:OPANEO>2.0.CO;2","article-title":"Optical properties and nondestructive estimation of anthocyanin content in plant leaves","volume":"74","author":"Gitelson","year":"2001","journal-title":"Photochem. Photobiol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.measurement.2014.05.033","article-title":"Potential of radial basis function-based support vector regression for apple disease detection","volume":"55","author":"Omrani","year":"2014","journal-title":"Measurement"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1016\/j.mineng.2005.03.003","article-title":"Application of image processing and radial basis neural network techniques for ore sorting and ore classification","volume":"18","author":"Singh","year":"2005","journal-title":"Miner. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1037\/026479","article-title":"Parallel distributed-processing\u2014Explorations in the microstructure of cognition","volume":"32","author":"Palmer","year":"1987","journal-title":"Contemp. Psychol."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Barros, A.C.A., and Cavalcanti, G.D.C. (2008, January 1\u20138). Combining global optimization algorithms with a simple adaptive distance for feature selection and weighting. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2008), Hong Kong, China.","DOI":"10.1109\/IJCNN.2008.4634300"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1205\/096030801753252298","article-title":"Stepwise discriminant analysis for colour grading of oil palm using machine vision system","volume":"79","author":"Abdullah","year":"2001","journal-title":"Food Bioprod. Process."},{"key":"ref_60","first-page":"238","article-title":"Introduction to statistical pattern recognition","volume":"MC 4","author":"Swartzla","year":"1974","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_61","first-page":"207","article-title":"Distance metric learning for large margin nearest neighbor classification","volume":"10","author":"Weinberger","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Abdulridha, J., Ehsani, R., and de Castro, A. (2016). Detection and differentiation between laurel wilt disease, phytophthora disease, and salinity damage using a hyperspectral sensing technique. Agriculture, 6.","DOI":"10.3390\/agriculture6040056"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ampatzidis, Y., De Bellis, L., and Luvisi, A. (2017). iPathology: Robotic applications and management of plants and plant diseases. Sustainability, 9.","DOI":"10.3390\/su9061010"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Luvisi, A., Ampatzidis, Y.G., and De Bellis, L. (2016). Plant pathology and information technology: Opportunity for management of disease outbreak and applications in regulation frameworks. Sustainability, 8.","DOI":"10.3390\/su8080831"},{"key":"ref_65","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_66","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_67","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_68","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.biosystemseng.2018.09.018","article-title":"Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments","volume":"175","author":"Sonobe","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.fcr.2017.12.004","article-title":"Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat","volume":"217","author":"Frels","year":"2018","journal-title":"Field Crop. Res."},{"key":"ref_70","first-page":"135","article-title":"Advances in image processing for detection of plant diseases","volume":"12","author":"Patil","year":"2011","journal-title":"Adv. Bioinf. Appl. Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.compag.2012.12.002","article-title":"Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees","volume":"91","author":"Sankaran","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.compag.2018.12.018","article-title":"A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses","volume":"156","author":"Abdulridha","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/S0034-4257(03)00131-7","article-title":"Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression","volume":"86","author":"Hansen","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.rse.2015.08.034","article-title":"Importance of biocrusts in dryland monitoring using spectral indices","volume":"170","author":"Knerr","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2018.02.003","article-title":"Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform","volume":"138","author":"Asaari","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1094\/PDIS-10-17-1536-RE","article-title":"The use of features from fluorescence, thermography, and NDVI imaging to detect biotic stress in lettuce","volume":"102","author":"Sandmann","year":"2018","journal-title":"Plant. Dis."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1373\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:57:02Z","timestamp":1760187422000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,8]]},"references-count":76,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11111373"],"URL":"https:\/\/doi.org\/10.3390\/rs11111373","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,8]]}}}