{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:52:08Z","timestamp":1775199128889,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2014,6,5]],"date-time":"2014-06-05T00:00:00Z","timestamp":1401926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spectral Vegetation Indices (SVIs) have been widely used to indirectly detect plant diseases. The aim of this research is to evaluate the effect of different disease symptoms on SVIs and introduce suitable SVIs to detect rust disease. Wheat leaf rust is one of the prevalent diseases and has different symptoms including yellow, orange, dark brown, and dry areas. The reflectance spectrum data for healthy and infected leaves were collected using a spectroradiometer in the 450 to 1000 nm range. The ratio of the  disease-affected area to the total leaf area and the proportion of each disease symptoms were obtained using RGB digital images. As the disease severity increases, so does the scattering of all SVI values. The indices were categorized into three groups based on their accuracies in disease detection. A few SVIs showed an accuracy of more than 60% in classification. In the first group, NBNDVI, NDVI, PRI, GI, and RVSI showed the highest amount of classification accuracy. The second and third groups showed classification accuracies of about 20% and 40% respectively. Results show that few indices have the ability to indirectly detect plant disease.<\/jats:p>","DOI":"10.3390\/rs6065107","type":"journal-article","created":{"date-parts":[[2014,6,5]],"date-time":"2014-06-05T11:18:20Z","timestamp":1401967100000},"page":"5107-5123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":109,"title":["Evaluating the Effect of Different Wheat Rust Disease Symptoms on Vegetation Indices Using Hyperspectral Measurements"],"prefix":"10.3390","volume":"6","author":[{"given":"Davoud","family":"Ashourloo","sequence":"first","affiliation":[{"name":"Remote Sensing Department, Faculty of Geodesy and Geomatics Eng., K.N.Toosi University of Technology, Tehran 19697-15433, Iran"}]},{"given":"Mohammad","family":"Mobasheri","sequence":"additional","affiliation":[{"name":"Remote Sensing Department, Faculty of Geodesy and Geomatics Eng., K.N.Toosi University of Technology, Tehran 19697-15433, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2809-2376","authenticated-orcid":false,"given":"Alfredo","family":"Huete","sequence":"additional","affiliation":[{"name":"Plant Functional Biology and Climate Change Cluster, University of Technology Sydney, Ultimo, NSW 2007, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2014,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6323","DOI":"10.3390\/rs5126323","article-title":"Trait estimation in herbaceous plant assemblages from in situ canopy spectra","volume":"5","author":"Roelofsen","year":"2013","journal-title":"Remote Sens"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"858","DOI":"10.3390\/rs1040858","article-title":"Hyperspectral reflectance and fluorescence imaging to detect scab induced stress in Apple leaves","volume":"1","author":"Delalieux","year":"2009","journal-title":"Remote Sens"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10343-008-0194-2","article-title":"Sensor use in plant protection","volume":"60","author":"Steiner","year":"2008","journal-title":"Gesunde Pflanz"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.fcr.2012.05.011","article-title":"Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses","volume":"134","author":"Zhang","year":"2012","journal-title":"Field Crops Res"},{"key":"ref_5","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":"Mahlein","year":"2011","journal-title":"Field Crops Res"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.compag.2004.04.003","article-title":"Automatic detection of \u201cyellow rust\u201d in wheat using reflectance measurements and neuralnetworks","volume":"44","author":"Moshou","year":"2004","journal-title":"Comput. 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