{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T20:56:33Z","timestamp":1771102593685,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,26]],"date-time":"2022-05-26T00:00:00Z","timestamp":1653523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31972211"],"award-info":[{"award-number":["31972211"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Maize is one of the most important crops in China, and it is under a serious, ever-increasing threat from southern corn rust (SCR). The identification of wheat rust based on hyperspectral data has been proved effective, but little research on detecting maize rust has been reported. In this study, full-range hyperspectral data (350~2500 nm) were collected under solar illumination, and spectra collected under solar illumination (SCUSI) were separated into several groups according to the disease severity, measuring height and leaf curvature (the smoothness of the leaf surface). Ten indices were selected as candidate indicators for SCR classification, and their sensitivities to the disease severity, measuring height and leaf curvature, were subjected to analysis of variance (ANOVA). The better-performing indices according to the ANOVA test were applied to a random forest classifier, and the classification results were evaluated by using a confusion matrix. The results indicate that the PRI was the optimal index for SCR classification based on the SCUSI, with an overall accuracy of 81.30% for mixed samples. The results lay the foundation for SCR detection in the incubation period and reveal potential for SCR detection based on UAV and satellite imageries, which may provide a rapid, timely and cost-effective detection method for SCR monitoring.<\/jats:p>","DOI":"10.3390\/rs14112551","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:25:12Z","timestamp":1653956712000},"page":"2551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Classification of Southern Corn Rust Severity Based on Leaf-Level Hyperspectral Data Collected under Solar Illumination"],"prefix":"10.3390","volume":"14","author":[{"given":"Jianmeng","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}]},{"given":"Mingliang","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"},{"name":"Food Crop Research Institute, Yunnan Academy of Agriculture Sciences, Kunming 650205, China"}]},{"given":"Qiuyu","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}]},{"given":"Jiayu","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}]},{"given":"Huanyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Entomology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4910-5001","authenticated-orcid":false,"given":"Zhanhong","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,26]]},"reference":[{"key":"ref_1","unstructured":"FAO (2021, July 19). FAOSTAT-Agriculture, Food and Agricultural Organizations of the United Nations. Available online: http:\/\/faostat3.fao.org\/brpwse\/Q\/QC\/E."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, Y.Q., Gao, F., Gao, G.Y., Zhao, J.Y., Wang, X.G., and Zhang, R. (2019). Production and cultivated area variation in cereal, rice, wheat and maize in China (1998\u20132016). Agronomy, 9.","DOI":"10.3390\/agronomy9050222"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101426","DOI":"10.1016\/j.pmpp.2019.101426","article-title":"Non-destructive techniques of detecting plant diseases: A review","volume":"108","author":"Ali","year":"2019","journal-title":"Physiol. Mol. Plant Pathol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s42483-021-00102-0","article-title":"Southern corn rust caused by Puccinia polysora Underw.: A review","volume":"3","author":"Sun","year":"2021","journal-title":"Phytopathol. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"18029","DOI":"10.1038\/s41598-021-97556-1","article-title":"De novo transcriptome assembly, polymorphic SSR markers development and population genetics analyses for southern corn rust (Puccinia polysora)","volume":"11","author":"Sun","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1094\/PDIS-08-11-0680","article-title":"Field distribution of wheat stripe rust latent infection using real-time PCR","volume":"96","author":"Yan","year":"2012","journal-title":"Plant Dis."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1111\/ppa.13020","article-title":"Hyperspectral signal decomposition and symptom detection of wheat rust disease at the leaf scale using pure fungal spore spectra as reference","volume":"68","author":"Bohnenkamp","year":"2019","journal-title":"Plant Pathol."},{"key":"ref_8","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-infrared and middle-infrared reflectances","volume":"30","author":"Hunt","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1016\/j.rse.2010.11.001","article-title":"Spectroscopic determination of leaf water content using continuous wavelet analysis","volume":"115","author":"Cheng","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s41348-017-0124-6","article-title":"Benefits of hyperspectral imaging for plant disease detection and plant protection: A technical perspective","volume":"125","author":"Thomas","year":"2017","journal-title":"J. Plant Dis. Prot."},{"key":"ref_11","unstructured":"(2022, May 10). Visible Light Definition and Wavelengths. Available online: https:\/\/www.thoughtco.com\/definition-of-visible-light-605941."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2004.06.002","article-title":"Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks","volume":"92","author":"Smith","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1080\/01431169408954177","article-title":"The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status","volume":"15","author":"Filella","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1023\/A:1025667309714","article-title":"Theoretical and empirical analysis of relief and rrelieff","volume":"53","author":"Kononenko","year":"2003","journal-title":"Mach. Learn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6961387","DOI":"10.1155\/2017\/6961387","article-title":"Detecting the early stage of phaeosphaeria leaf spot infestations in maize crop using in-situ hyperspectral data and guided regularized random forest algorithm","volume":"2017","author":"Adam","year":"2017","journal-title":"J. Spectros."},{"key":"ref_18","first-page":"354","article-title":"A review of neural networks in plant disease detection using hyperspectral data","volume":"5","author":"Golhani","year":"2018","journal-title":"Inf. Process. Agric."},{"key":"ref_19","first-page":"170","article-title":"Remote sensing index selection of leaf blight disease in spring maize based on hyperspectral data","volume":"33","author":"Wang","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shi, Y., Huang, W.J., Gonzalez-Moreno, P., Luke, B., Dong, Y.Y., Zheng, Q., Ma, H.Q., and Liu, L.Y. (2018). Wavelet-based rust spectral feature set (WRSFs): A novel spectral feature set based on continuous wavelet transformation for tracking progressive host-pathogen interaction of yellow rust on wheat. Remote Sens., 10.","DOI":"10.3390\/rs10040525"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, N., Yang, G.J., Pan, Y.C., Yang, X.D., Chen, L.P., and Zhao, C.J. (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_22","doi-asserted-by":"crossref","unstructured":"Meng, R., Lv, Z.G., Yan, J.B., Chen, G.S., Zhao, F., Zeng, L.L., and Xu, B.Y. (2020). Development of spectral disease indices for southern corn rust detection and severity classification. Remote Sens., 12.","DOI":"10.3390\/rs12193233"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Skoneczny, H., Kubiak, K., Spiralski, M., and Kotlarz, J. (2020). Fire blight disease detection for apple trees: Hyperspectral analysis of healthy, infected and dry leaves. Remote Sens., 12.","DOI":"10.3390\/rs12132101"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1080\/10106049.2017.1343391","article-title":"Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop","volume":"33","author":"Dhau","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.compag.2016.12.015","article-title":"Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities","volume":"135","author":"Xie","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jhydrol.2016.02.040","article-title":"Water resources climate change projections using supervised nonlinear and multivariate soft computing techniques","volume":"536","author":"Sarhadi","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_29","unstructured":"(2022, May 10). Sklearn.Metrics.Precision_Recall_Fscore_Support. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.precision_recall_fscore_support.html."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lama, G.F.C., Sadeghifar, T., Azad, M.T., Sihag, P., and Kisi, O. (2022). On the indirect estimation of wind wave heights over the southern coasts of Caspian Sea: A comparative analysis. Water, 14.","DOI":"10.3390\/w14060843"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Albarrac\u00edn, J.F.H., Oliveira, R.S., Hirota, M., DosSantos, J.A., and Torres, R.D.S. (2020). A soft computing approach for selecting and combining spectral bands. Remote Sens., 12.","DOI":"10.3390\/rs12142267"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/TCYB.2013.2289331","article-title":"Hyperspectral image classification using functional data analysis","volume":"44","author":"Li","year":"2014","journal-title":"IEEE Trans. Cybern."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"110467","DOI":"10.1016\/j.oceaneng.2021.110467","article-title":"Wave height predictions in complex sea flows through soft-computing models: Case study of Persian Gulf","volume":"245","author":"Sadeghifar","year":"2022","journal-title":"Ocean. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"147490","DOI":"10.1109\/ACCESS.2019.2944422","article-title":"Prediction of significant wave heights based on CS-BP model in the South China Sea","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s11356-020-10344-8","article-title":"Application of soft computing to predict water quality in wetland","volume":"28","author":"Pham","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Blaschke, T., Pradhan, B., Pourghasemi, H.R., Tiefenbacher, J.P., and Bui, D.T. (2020). Evaluation of recent advanced soft computing techniques for gully erosion susceptibility mapping: A Comparative Study. Sensors, 20.","DOI":"10.3390\/s20020335"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s12145-018-0352-8","article-title":"Landslide susceptibility assessment in the Anfu County, China: Comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND)","volume":"11","author":"Hong","year":"2018","journal-title":"Earth Sci. Inform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s40538-021-00217-8","article-title":"Drone and sensor technology for sustainable weed management: A review","volume":"8","author":"Esposito","year":"2021","journal-title":"Chem. Biol. Technol. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1007\/s10661-018-6848-3","article-title":"Exploring the optimal experimental setup for surface flow velocity measurements using PTV","volume":"190","author":"Pizarro","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112350","DOI":"10.1016\/j.rse.2021.112350","article-title":"Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection","volume":"257","author":"Tian","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Appeltans, S., Guerrero, A., Nawar, S., Pieters, J., and Mouazen, A.M. (2020). Practical recommendations for hyperspectral and thermal proximal disease sensing in potato and leek fields. Remote Sens., 12.","DOI":"10.3390\/rs12121939"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.biosystemseng.2020.07.005","article-title":"Hyperspectral imaging for identification of zebra chip disease in potatoes","volume":"197","author":"Garhwal","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","unstructured":"Herrmann, I., Vosberg, S.K., Ravindran, P., Singh, A., Chang, H.X., Chilvers, M.I., Conley, S.P., and Townsend, P.A. (2018). Leaf and canopy level detection of fusarium virguliforme (sudden death syndrome) in soybean. Remote Sens., 10.","DOI":"10.3390\/rs10030426"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.compag.2004.04.003","article-title":"Automatic detection of \u2018yellow rust\u2019 in wheat using reflectance measurements and neural networks","volume":"44","author":"Moshou","year":"2004","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yao, Z.F., Lei, Y., and He, D.J. (2019). Early visual detection of wheat stripe rust using visible\/near-infrared hyperspectral imaging. Sensors, 19.","DOI":"10.3390\/s19040952"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s10658-014-0397-6","article-title":"Hyperspectral measurements of severity of stripe rust on individual wheat leaves","volume":"139","author":"Zhao","year":"2014","journal-title":"Eur. J. Plant Pathol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"126090","DOI":"10.1155\/2015\/126090","article-title":"Canopy spectral characterization of wheat stripe rust in latent period","volume":"2015","author":"Liu","year":"2015","journal-title":"J. Spectrosc."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, H., Qin, F., Ruan, L., Wang, R., Liu, Q., Ma, Z.H., Li, X.L., Cheng, P., and Wang, H.G. (2016). Identification and severity determination of wheat stripe rust and wheat leaf rust based on hyperspectral data acquired using a black-paper-based measuring method. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0154648"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Dehkordi, R.H., El Jarroudi, M., Kouadio, L., Meersmans, J., and Beyer, M. (2020). Monitoring wheat leaf rust and stripe rust in winter wheat using high-resolution UAV-based red-green-blue imagery. Remote Sens., 12.","DOI":"10.3390\/rs12223696"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ruan, C., Dong, Y.Y., Huang, W.J., Huang, L.S., Ye, H.C., Ma, H.Q., Guo, A.T., and Ren, Y. (2021). Prediction of wheat stripe rust occurrence with time series sentinel-2 images. Agriculture, 11.","DOI":"10.3390\/agriculture11111079"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1364\/JOT.87.000212","article-title":"Classification of maize leaf diseases based on hyperspectral imaging technology","volume":"87","author":"Xu","year":"2020","journal-title":"J. Opt. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Luo, L.L., Chang, Q.R., Wang, Q., and Huang, Y. (2021). Identification and severity monitoring of maize dwarf mosaic virus infection based on hyperspectral measurements. Remote Sens., 13.","DOI":"10.3390\/rs13224560"},{"key":"ref_55","unstructured":"Analytical Spectral Devices, Inc. (ASD) (1999). Technical Guide, Analytical Spectral Devices, Inc.. [3rd ed.]. Available online: https:\/\/wiki.chem.gwu.edu\/MillerLab\/images\/3\/3e\/FieldSpecTechGuide.pdf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/MSP.2011.941097","article-title":"What is a savitzky-golay filter?","volume":"28","author":"Schafer","year":"2011","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_57","first-page":"100283","article-title":"Red-edge normalised difference vegetation index (NDVI705) from sentinel-2 imagery to assess post-fire regeneration","volume":"17","author":"Evangelides","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1080\/01431169508954588","article-title":"Reflectance assessment of mite effects on apple trees","volume":"16","author":"Uelas","year":"1995","journal-title":"Int. J. Remote Sens."},{"key":"ref_60","first-page":"221","article-title":"Semi empirical indexes to assess carotenoids chlorophyll-a ratio from leaf spectral reflectance","volume":"31","author":"Penuelas","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4723","DOI":"10.3390\/rs6064723","article-title":"Developing two spectral disease indices for detection of wheat leaf rust (Pucciniatriticina)","volume":"6","author":"Ashourloo","year":"2014","journal-title":"Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS terrestrial chlorophyll index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1080\/01431160310001618031","article-title":"Detecting sugarcane \u2018orange rust\u2019 disease using EO-1 hyperion hyperspectral imagery","volume":"25","author":"Apan","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.rse.2012.09.014","article-title":"Carotenoid content estimation in a heterogeneous conifer forest using narrow-band indices and PROSPECT+DART simulations","volume":"127","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_65","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_66","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_67","first-page":"102608","article-title":"Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data","volume":"105","author":"Furuya","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.agwat.2018.08.029","article-title":"Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing","volume":"213","author":"Krishna","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s11119-015-9390-0","article-title":"Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat","volume":"16","author":"Devadas","year":"2015","journal-title":"Precis. Agric."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(94)90079-5","article-title":"Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands","volume":"50","author":"Carter","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_71","first-page":"1385","article-title":"A fast method for distinguishing southern rust pathogen Puccinia polysora from common rust pathogen Puccinia sorghi","volume":"47","author":"Huang","year":"2020","journal-title":"J. Plant Prot."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2551\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:19:17Z","timestamp":1760138357000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/11\/2551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,26]]},"references-count":71,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14112551"],"URL":"https:\/\/doi.org\/10.3390\/rs14112551","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,26]]}}}