{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T02:04:16Z","timestamp":1775873056711,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T00:00:00Z","timestamp":1631232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Plan of China","award":["2019YFE0126900"],"award-info":[{"award-number":["2019YFE0126900"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31671582"],"award-info":[{"award-number":["31671582"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Projects (Advanced Technology) of Jiangsu Province","award":["BE 2019383"],"award-info":[{"award-number":["BE 2019383"]}]},{"name":"the 111 project","award":["B16026"],"award-info":[{"award-number":["B16026"]}]},{"name":"333 Project of Jiangsu Province","award":["JS333"],"award-info":[{"award-number":["JS333"]}]},{"DOI":"10.13039\/501100018522","name":"Jiangsu Collaborative Innovation Center for Modern Crop Production","doi-asserted-by":"publisher","award":["JCICMCP"],"award-info":[{"award-number":["JCICMCP"]}],"id":[{"id":"10.13039\/501100018522","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Priority Academic Program Development of Jiangsu Higher Education Institutions","award":["PAPD"],"award-info":[{"award-number":["PAPD"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs &amp; NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs &amp; NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R2) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops.<\/jats:p>","DOI":"10.3390\/rs13183612","type":"journal-article","created":{"date-parts":[[2021,9,12]],"date-time":"2021-09-12T21:48:01Z","timestamp":1631483281000},"page":"3612","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat"],"prefix":"10.3390","volume":"13","author":[{"given":"Imran Haider","family":"Khan","sequence":"first","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Haiyan","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Aizhong","family":"Cao","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Crop Genetics and Germplasm Enhancement, Cytogenetics Institute, Nanjing Agricultural University\/JCIC-MCP, Nanjing 210095, China"}]},{"given":"Xue","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Hongyan","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4184-0730","authenticated-orcid":false,"given":"Tao","family":"Cheng","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Yongchao","family":"Tian","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1884-2404","authenticated-orcid":false,"given":"Yan","family":"Zhu","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"given":"Weixing","family":"Cao","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1093-1736","authenticated-orcid":false,"given":"Xia","family":"Yao","sequence":"additional","affiliation":[{"name":"National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"ref_1","first-page":"137","article-title":"Monitoring and evaluation of the diseases of and yield winter wheat from multi-temporal remotely-sensed data","volume":"25","author":"Liu","year":"2009","journal-title":"Trans. CSAE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1094\/PHYTO-96-0680","article-title":"Major genetic changes in wheat with potential to affect disease tolerance","volume":"96","author":"Foulkes","year":"2006","journal-title":"Phytopathology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1111\/j.1744-7348.2008.00291.x","article-title":"Crop traits and the tolerance of wheat and barley to foliar disease","volume":"154","author":"Bingham","year":"2009","journal-title":"Ann. Appl. Biol."},{"key":"ref_4","first-page":"27","article-title":"Droplet deposition and efficiency of fungicides sprayed with small UAV against wheat powdery mildew","volume":"11","author":"Qin","year":"2018","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13007-015-0073-7","article-title":"Hyperspectral phenotyping on the microscopic scale: Towards automated characterization of plant-pathogen interactions","volume":"11","author":"Kuska","year":"2015","journal-title":"Plant Methods"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"734","DOI":"10.3389\/fpls.2014.00734","article-title":"Image-based phenotyping of plant disease symptoms","volume":"5","author":"Mutka","year":"2015","journal-title":"Front. Plant Sci."},{"key":"ref_7","first-page":"783","article-title":"A lightweight mobile system for crop disease diagnosis","volume":"Volume 9730","author":"Siricharoen","year":"2016","journal-title":"International Conference Image Analysis and Recognition. Lecture Notes in Computer Science"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.biosystemseng.2016.01.017","article-title":"A review on the main challenges in automatic plant disease identification based on visible range images","volume":"144","author":"Barbedo","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1094\/PDIS-04-10-0256","article-title":"Satellite remote sensing of wheat infected by wheat streak mosaic virus","volume":"95","author":"Mirik","year":"2011","journal-title":"Plant Dis."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhou, X., Zhang, J., Lan, Y., Xu, C., and Liang, D. (2018). Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0187470"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20078","DOI":"10.3390\/s141120078","article-title":"A review of imaging techniques for plant phenotyping","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2013.02.003","article-title":"Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance","volume":"133","author":"Morales","year":"2013","journal-title":"Remote. Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"15919","DOI":"10.1038\/srep15919","article-title":"Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis","volume":"5","author":"Kim","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1007\/s11119-019-09661-x","article-title":"Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging","volume":"21","author":"Ye","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.3389\/fpls.2018.01962","article-title":"Early detection of magnaporthe oryzae-infected barley leaves and lesion visualization based on hyperspectral imaging","volume":"9","author":"Zhou","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shi, Y., Huang, W., Gonz\u00e1lez-Moreno, P., Luke, B., Dong, Y., Zheng, Q., Ma, H., and Liu, L. (2018). Wavelet-based rust spectral feature set (WRSFS): A novel spectral feature set based on continuous wavelet transformation for tracking progressive host\u2013pathogen interaction of yellow rust on wheat. Remote Sens., 10.","DOI":"10.3390\/rs10040525"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.compag.2012.03.006","article-title":"Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements","volume":"85","author":"Zhang","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.compag.2017.07.019","article-title":"Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis","volume":"141","author":"Shi","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zheng, Q., Huang, W., Cui, X., Dong, Y., Shi, Y., Ma, H., and Liu, L. (2019). Identification of wheat yellow rust using optimal three-band spectral indices in different growth stages. Sensors, 19.","DOI":"10.3390\/s19010035"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.tifs.2007.06.001","article-title":"Hyperspectral imaging\u2014An emerging process analytical tool for food quality and safety control","volume":"18","author":"Gowen","year":"2007","journal-title":"Trends Food Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"810","DOI":"10.3390\/rs4040810","article-title":"Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data","volume":"4","author":"Eckert","year":"2012","journal-title":"Remote Sens."},{"key":"ref_22","unstructured":"Yang, Y. (2012). The Key Diagnosis Technology of Rice Blast Based on Hyperspectral Image, Zhejiang University."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/s11119-017-9524-7","article-title":"Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves","volume":"19","author":"Lu","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s11306-010-0213-z","article-title":"Recipe for revealing informative metabolites based on model population analysis","volume":"6","author":"Li","year":"2010","journal-title":"Metabolomics"},{"key":"ref_27","first-page":"125","article-title":"Qualitative detection of haloxyfop- P- methyl residue in edible oil by near infrared spectroscopy combined with variable selection method","volume":"37","author":"Mo","year":"2018","journal-title":"Chin. J. Anal. Lab."},{"key":"ref_28","first-page":"342","article-title":"Discrimination of camellia oil adulteration by NIR spectra and subwindow permutation analysis","volume":"35","author":"Sun","year":"2015","journal-title":"Acta Opt. Sin."},{"key":"ref_29","unstructured":"Wang, W.Y. (2016). Monitoring Powdery Mildew with Hyperspectral Reflectance in Wheat, Nanjing Agricultural University."},{"key":"ref_30","first-page":"275","article-title":"Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements","volume":"1","author":"Graeff","year":"2006","journal-title":"Cent. Eur. J. Biol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/36.3001","article-title":"A transformation for ordering multispectral data in terms of image quality with implications for noise removal","volume":"26","author":"Green","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s11119-018-9600-7","article-title":"Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery","volume":"20","author":"Zheng","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1109\/JSTARS.2013.2294961","article-title":"New optimized spectral indices for identifying and monitoring winter wheat diseases","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","first-page":"1169","article-title":"Spectrum characteristics of cotton canopy infected with verticillium wilt and inversion of severity level","volume":"2","author":"Chen","year":"2008","journal-title":"Comput. Comput. Technol. Agric."},{"key":"ref_36","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_37","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_38","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1080\/01431160152558332","article-title":"A generalized confusion matrix for assessing area estimates from remotely sensed data","volume":"22","author":"Lewis","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","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_40","unstructured":"Merton, R., and Huntington, J. (1999, January 8\u201311). Early simulation results of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS. Proceedings of the Eighth Annual JPL Airborne Earth Science Workshop, Pasadena, CA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/S0034-4257(99)00067-X","article-title":"Hyperspectral vegetation indices and their relationships with agricultural crop characteristics","volume":"71","author":"Thenkabail","year":"2000","journal-title":"Remote. Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.2135\/cropsci1995.0011183X003500050023x","article-title":"Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis","volume":"35","author":"Filella","year":"1995","journal-title":"Crop. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0034-4257(01)00332-7","article-title":"Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data","volume":"81","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_44","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_45","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":"Physiol. Plant."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.compag.2005.11.004","article-title":"Using digital image analysis and spectral reflectance data to quantify damage by greenbug (Hemitera: Aphididae) in winter wheat","volume":"51","author":"Mirik","year":"2006","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Maimaitiyiming, M., Ghulam, A., Bozzolo, A., Wilkins, J.L., and Kwasniewski, M.T. (2017). Early detection of plant physiological responses to different levels of water stress using reflectance spectroscopy. Remote Sens., 9.","DOI":"10.3390\/rs9070745"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"16564","DOI":"10.1038\/srep16564","article-title":"Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging","volume":"5","author":"Xie","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1094\/PDIS-02-17-0168-RE","article-title":"Effects of climate change on epidemics of powdery mildew in winter wheat in China","volume":"101","author":"Tang","year":"2017","journal-title":"Plant Dis."},{"key":"ref_50","first-page":"1101","article-title":"Investigation of the hyperspectral image characteristics of wheat leaves under different stress","volume":"31","author":"Zhang","year":"2011","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_51","first-page":"1364","article-title":"Indices and modeling of wheat powdery mildew epidemic based on hourly air temperature and humidity data","volume":"32","author":"Yao","year":"2013","journal-title":"Shengtaixue Zazhi"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2010.02.007","article-title":"A review of advanced techniques for detecting plant diseases","volume":"72","author":"Sankaran","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_53","unstructured":"Yuan, L. (2015). Identification and Differentiation of Wheat Disease and Insects with Multi-Source and Multi-Scale Remote Sensing Data, Zhejiang University."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3612\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:00:27Z","timestamp":1760166027000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3612"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,10]]},"references-count":53,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183612"],"URL":"https:\/\/doi.org\/10.3390\/rs13183612","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,10]]}}}