{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:46:21Z","timestamp":1780919181436,"version":"3.54.1"},"reference-count":65,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,10,4]],"date-time":"2020-10-04T00:00:00Z","timestamp":1601769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No.2662019PY057"],"award-info":[{"award-number":["No.2662019PY057"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting corn quality and yields globally. Field-based fast, nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety. The development of spectral disease indices (SDIs), based on in situ leaf reflectance spectra, has proven to be an effective method in detecting plant diseases in the field. However, little is known about leaf spectral signatures that can assist in the accurate diagnosis of SCR, and no SDIs-based model has been reported for the field-based SCR monitoring. Here, to address those issues, we developed SDIs-based monitoring models to detect SCR-infected leaves and classify SCR damage severity. In detail, we first collected in situ leaf reflectance spectra (350\u20132500 nm) of healthy and infected corn plants with three severity levels (light, medium, and severe) using a portable spectrometer. Then, the RELIEF-F algorithm was performed to select the most discriminative features (wavelengths) and two band normalized differences for developing SDIs (i.e., health index and severity index) in SCR detection and severity classification, respectively. The leaf reflectance spectra, most sensitive to SCR detection and severity classification, were found in the 572 nm, 766 nm, and 1445 nm wavelength and 575 nm, 640 nm, and 1670 nm wavelength, respectively. These spectral features were associated with leaf pigment and leaf water content. Finally, by employing a support vector machine (SVM), the performances of developed SCR-SDIs were assessed and compared with 38 stress-related vegetation indices (VIs) identified in the literature. The SDIs-based models developed in this study achieved an overall accuracy of 87% and 70% in SCR detection and severity classification, 1.1% and 8.3% higher than the other best VIs-based model under study, respectively. Our results thus suggest that the SCR-SDIs is a promising tool for fast, nondestructive diagnosis of SCR in the field over large areas. To our knowledge, this study represents one of the first few efforts to provide a theoretical basis for remote sensing of SCR at field and larger scales. With the increasing use of unmanned aerial vehicles (UAVs) with hyperspectral measurement capability, more studies should be conducted to expand our developed SCR-SDIs for SCR monitoring at different study sites and growing stages in the future.<\/jats:p>","DOI":"10.3390\/rs12193233","type":"journal-article","created":{"date-parts":[[2020,10,5]],"date-time":"2020-10-05T08:35:57Z","timestamp":1601886957000},"page":"3233","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4756-9934","authenticated-orcid":false,"given":"Ran","family":"Meng","sequence":"first","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengang","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianbing","family":"Yan","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gengshen","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4534-933X","authenticated-orcid":false,"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linglin","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Guanxi Key Laboratory of Water Engineering Materials and Structures, Guanxi Institute of Water Resources Research, Nanning 530023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binyuan","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.2135\/cropsci2010.06.0327","article-title":"General Resistance in Maize to Southern Rust (Puccinia polysora Underw.)","volume":"51","author":"Brewbaker","year":"2011","journal-title":"Crop Sci."},{"key":"ref_2","first-page":"44","article-title":"Analysis of the main occurrence characteristics and causes of the southern corn rust in China in 2015","volume":"36","author":"Liu","year":"2016","journal-title":"China Plant Prot."},{"key":"ref_3","first-page":"71","article-title":"A report about the occurrence area of southern corn rust and the resistance of the corn cultivars in china","volume":"3","author":"Liu","year":"2009","journal-title":"Crops"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1094\/PHP-RS-16-0030","article-title":"Corn Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada from 2012 to 2015","volume":"17","author":"Mueller","year":"2016","journal-title":"Plant Health Prog."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s10658-011-9878-z","article-title":"Recent advances in sensing plant diseases for precision crop protection","volume":"133","author":"Mahlein","year":"2012","journal-title":"Eur. 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