{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T23:07:06Z","timestamp":1774998426243,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2014,5,26]],"date-time":"2014-05-26T00:00:00Z","timestamp":1401062400000},"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 detect different plant diseases. Wheat leaf rust manifests itself as an early symptom with the leaves turning yellow and orange. The sign of advancing disease is the leaf colour changing to brown while the final symptom is when the leaf becomes dry. The goal of this work is to develop spectral disease indices for the detection of leaf rust. The reflectance spectra of the wheat\u2019s infected and non-infected leaves at different disease stages were collected using a spectroradiometer. As ground truth, the ratio of the disease-affected area to the total leaf area and the fractions of the different symptoms were extracted using an RGB digital camera. Fractions of the various disease symptoms extracted by the digital camera and the measured reflectance spectra of the infected leaves were used as input to the spectral mixture analysis (SMA). Then, the spectral reflectance of the different disease symptoms were estimated using SMA and the least squares method. The reflectance of different disease symptoms in the 450~1000 nm were studied carefully using the Fisher function. Two spectral disease indices were developed based on the reflectance at the 605, 695 and 455 nm wavelengths. In both indices, the R2 between the estimated and the observed was as highas 0.94.<\/jats:p>","DOI":"10.3390\/rs6064723","type":"journal-article","created":{"date-parts":[[2014,5,27]],"date-time":"2014-05-27T02:36:58Z","timestamp":1401158218000},"page":"4723-4740","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":138,"title":["Developing Two Spectral Disease Indices for Detection of  Wheat Leaf Rust (Pucciniatriticina)"],"prefix":"10.3390","volume":"6","author":[{"given":"Davoud","family":"Ashourloo","sequence":"first","affiliation":[{"name":"Remote Sensing Group, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697-15433, Iran"}]},{"given":"Mohammad","family":"Mobasheri","sequence":"additional","affiliation":[{"name":"Remote Sensing Group, Faculty of Geodesy and Geomatics Engineering, 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":[[2014,5,26]]},"reference":[{"key":"ref_1","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_2","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_3","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_4","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_5","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_6","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_7","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 neural networks","volume":"44","author":"Moshou","year":"2004","journal-title":"Comput. Electron. Agric"},{"key":"ref_8","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_9","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_10","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_11","first-page":"221","article-title":"Semiempirical indices to assess carotenoids\/chlorophyll a ratio from leaf spectral reflectance","volume":"31","author":"Baret","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.compag.2008.11.007","article-title":"The potential of spectral reflectance technique for the detection of Grapevine leaf roll-associated virus-3 in two red-berried wine grape cultivars","volume":"66","author":"Naidu","year":"2009","journal-title":"Comput. Electron. Agric"},{"key":"ref_13","unstructured":"Chen, B., Wang, K., Li, S., Wang, J., Bai, J., Xiao, C., and Lai, J. (2008). IFIP International Federation for Information Processing, Springer."},{"key":"ref_14","unstructured":"Muhammad, H.H. (2002, January 16\u201318). Using Hyperspectral Reflectance Data for Discrimination between Healthy and Diseased Plants, and Determination of Damage-level in Diseased Plants. Washington, DC, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1146\/annurev.phyto.41.121702.103726","article-title":"The potential of optical canopy measurement for targeted control of field crop disease","volume":"41","author":"West","year":"2003","journal-title":"Annu. Rev. Phytopathol"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1364\/AO.47.001922","article-title":"Detection of mechanical and disease stresses in citrus plants by fluorescence spectroscopy","volume":"47","author":"Belasque","year":"2008","journal-title":"Appl. Opt"},{"key":"ref_17","first-page":"180","article-title":"Spectral characteristics of rice plants infested by brown planthoppers","volume":"25","author":"Yang","year":"2001","journal-title":"Proc. Natl. Sci. Counc. Repub. China Part B Life Sci"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"329","DOI":"10.2135\/cropsci2006.05.0335","article-title":"Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder","volume":"47","author":"Yang","year":"2007","journal-title":"Crop. Sci"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.biosystemseng.2007.01.008","article-title":"Near-infrared spectroscopy in detecting leaf miner damage on tomato leaf","volume":"96","author":"Xu","year":"2007","journal-title":"Biosyst. Eng"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1111\/j.1364-3703.2008.00487.x","article-title":"Wheat leaf rust caused by Puccinia triticina","volume":"9","author":"Bolton","year":"2008","journal-title":"Mol. Plant. Patol"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1111\/j.1469-8137.2004.01237.x","article-title":"Wheat leaf photosynthesis loss due to leaf rust, with respect to lesion development and leaf nitrogen status","volume":"165","author":"Robert","year":"2005","journal-title":"New Phytol"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s11119-010-9180-7","article-title":"Spectral signatures of sugar beet leaves for the detection and differentiation of diseases","volume":"11","author":"Mahlein","year":"2010","journal-title":"Precis. Agric"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.compag.2010.06.009","article-title":"Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance","volume":"74","author":"Rumpf","year":"2010","journal-title":"Comput. Electron. Agric"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mahlein, A.-K., Steiner, U., Hillnh\u00fctter, C., Dehne, H.-W., and Oerke, E.-C. (2012). Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant. Methods, 8.","DOI":"10.1186\/1746-4811-8-3"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1111\/j.1365-2338.1991.tb01299.x","article-title":"Use of multispectral radiometry in wheat yellow rust experiments","volume":"21","author":"Hansen","year":"1991","journal-title":"OEPP\/EPPO Bull"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s11119-007-9038-9","article-title":"Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging","volume":"8","author":"Huang","year":"2007","journal-title":"Precis. Agric"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s11119-008-9100-2","article-title":"Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves","volume":"10","author":"Devadas","year":"2009","journal-title":"Precis. Agric"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11119-007-9036-y","article-title":"Multi-temporal wheat disease detection by multi-spectral remote sensing","volume":"8","author":"Franke","year":"2007","journal-title":"Precis. Agric"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ashourloo, D., Mobasheri, M.R., and Huete, A. (2014). Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements. Remote Sens, in press.","DOI":"10.3390\/rs6065107"},{"key":"ref_30","first-page":"31","article-title":"Fast and accurate detection and classification of plant diseases","volume":"17","author":"Reyalat","year":"2011","journal-title":"Int. J. Comput. Appl"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/LGRS.2008.919685","article-title":"Unmixing-based LANDSAT TM and MERIS FR data fusion","volume":"5","author":"Clevers","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/S0034-4257(03)00135-4","article-title":"Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE","volume":"87","author":"Dennison","year":"2003","journal-title":"Remote Sens. Environ"},{"key":"ref_33","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_34","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_35","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_36","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_37","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0034-4257(94)90136-8","article-title":"Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves","volume":"48","author":"Penuelas","year":"1995","journal-title":"Remote Sens. Environ"},{"key":"ref_38","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_39","unstructured":"Kim, M.S., Daughtry, C.S.T., Chappelle, E.W., and McMurtrey, J.E. (1994, January 17\u201321). The Use of High Spectral Resolution Bands for Estimating Absorbed Photosynthetically Active Radiation (APAR). Val d\u2019Is\u00e8re, France."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2005.09.002","article-title":"Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy","volume":"99","author":"Miller","year":"2005","journal-title":"Remote Sens. Environ"},{"key":"ref_41","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":"2000","journal-title":"Remote Sens. Environ"},{"key":"ref_42","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/6\/4723\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:11:48Z","timestamp":1760217108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/6\/6\/4723"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,5,26]]},"references-count":42,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2014,6]]}},"alternative-id":["rs6064723"],"URL":"https:\/\/doi.org\/10.3390\/rs6064723","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,5,26]]}}}