{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T03:58:41Z","timestamp":1791086321803,"version":"4.1.0"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,23]],"date-time":"2019-02-23T00:00:00Z","timestamp":1550880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Science and Technology Program of Shaanxi Province of China","award":["2015KTZDNY01-06, 2017NY-104"],"award-info":[{"award-number":["2015KTZDNY01-06, 2017NY-104"]}]},{"name":"the Science and Technology Plan Project of Yangling Demonstration Area, China","award":["2015NY-10"],"award-info":[{"award-number":["2015NY-10"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wheat stripe rust is one of the most important and devastating diseases in wheat production. In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper. Hyperspectral images of wheat leaves infected by stripe rust for 15 consecutive days were collected, and their corresponding chlorophyll content (SPAD value) were measured using a handheld SPAD-502 chlorophyll meter. The spectral reflectance of the samples were then extracted from the hyperspectral images, using image segmentation based on a leaf mask. The effective wavebands were selected by the loadings of principal component analysis (PCA-loadings) and the successive projections algorithm (SPA). Next, the regression model of the SPAD values in wheat leaves was established, based on the back propagation neural network (BPNN), using the full spectra and the selected effective wavelengths as inputs, respectively. The results showed that the PCA-loadings\u2013BPNN model had the best performance, which modeling accuracy (RC2) and validation accuracy (RP2) were 0.921 and 0.918, respectively, and the RPD was 3.363. The number of effective wavelengths extracted by this model accounted for only 3.12% of the total number of wavelengths, thus simplifying the models and improving the rate of operation greatly. Finally, the optimal models were used to estimate the SPAD of each pixel within the wheat leaf images, to generate spatial distribution maps of chlorophyll content. The visualized distribution map showed that wheat leaves infected by stripe rust could be identified six days after inoculation, and at least three days before the appearance of visible symptoms, which provides a new method for the early detection of wheat stripe rust.<\/jats:p>","DOI":"10.3390\/s19040952","type":"journal-article","created":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T03:06:52Z","timestamp":1551064012000},"page":"952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Early Visual Detection of Wheat Stripe Rust Using Visible\/Near-Infrared Hyperspectral Imaging"],"prefix":"10.3390","volume":"19","author":[{"given":"Zhifeng","family":"Yao","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Xianyang 712100, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang 712100, China"},{"name":"Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Xianyang 712100, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang 712100, China"},{"name":"Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongjian","family":"He","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&amp;F University, Xianyang 712100, China"},{"name":"Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang 712100, China"},{"name":"Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Xianyang 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1146\/annurev.phyto.40.020102.111645","article-title":"Stripe rust of wheat and barley in North America: A retrospective historical review","volume":"40","author":"Line","year":"2002","journal-title":"Annu. Rev. Phytopathol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1080\/07060660509507230","article-title":"Epidemiology and control of stripe rust puccinia striiformis f. sp. tritici on wheat","volume":"27","author":"Chen","year":"2005","journal-title":"Can. J. Plant Pathol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1111\/mpp.12116","article-title":"Wheat stripe (yellow) rust caused by Puccinia striiformis f. sp. tritici","volume":"15","author":"Chen","year":"2014","journal-title":"Mol. Plant Pathol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"223","DOI":"10.15302\/J-FASE-2014025","article-title":"Modeling of the overwintering distribution of Puccinia striiformis f. sp. tritici based on meteorological data from 2001 to 2012 in China","volume":"1","author":"Wang","year":"2014","journal-title":"Front. Agric. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1094\/PDIS-09-14-0928-RE","article-title":"Determination of the role of berberis spp. in wheat stem rust in China","volume":"99","author":"Zhao","year":"2015","journal-title":"Plant Dis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1071\/AR06142","article-title":"Wheat stripe rust in China","volume":"58","author":"Wan","year":"2007","journal-title":"Aust. J. Agric. Res."},{"key":"ref_7","first-page":"183","article-title":"Early detection of wheat disease based on thermal infrared imaging","volume":"30","author":"Li","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/9740295","article-title":"Application of near-infrared spectroscopy to quantitatively determine relative content of Puccnia striiformis f. sp. tritici DNA in wheat leaves in incubation period","volume":"2017","author":"Zhao","year":"2017","journal-title":"J. Spectrosc."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.17957\/IJAB\/15.0034","article-title":"Hyperspectral measurements for estimating vertical infection of yellow rust on winter wheat plant","volume":"17","author":"Huang","year":"2015","journal-title":"Int. J. Agric. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.sajb.2007.02.092","article-title":"Microscopic studies on stripe rust-infected doubled haploid wheat lines derived from a cross Kariega Avocet S","volume":"73","author":"Moldenhauer","year":"2007","journal-title":"S. Afr. J. Bot."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1023\/A:1017510331721","article-title":"Molecular mapping and detection of the yellow rust resistance gene Yr26 in wheat transferred from triticum turgidum L. Using microsatellite markers","volume":"120","author":"Ma","year":"2001","journal-title":"Euphytica"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"4897","DOI":"10.1080\/01431160701253253","article-title":"Estimating and mapping grass phosphorus concentration in an African savanna using hyperspectral image data","volume":"28","author":"Mutangao","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.scienta.2012.02.024","article-title":"Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging","volume":"138","author":"Shi","year":"2012","journal-title":"Sci. Hortic."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.biosystemseng.2013.02.007","article-title":"Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging","volume":"115","author":"Zhang","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, T., Zeng, R., Guo, W., Hou, X., Lan, Y., and Zhang, L. (2018). Detection of stress in cotton (Gossypium hirsutum L.) caused by aphids using leaf level hyperspectral measurements. Sensors, 18.","DOI":"10.3390\/s18092798"},{"key":"ref_18","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_19","first-page":"1853","article-title":"Study on photosynthetic rate of wheat under powdery mildew stress using hyperspectral image","volume":"20","author":"Zhang","year":"2018","journal-title":"Int. J. Agric. Biol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.scitotenv.2016.08.014","article-title":"Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance","volume":"578","author":"Sytar","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.snb.2018.06.121","article-title":"Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging","volume":"273","author":"Susic","year":"2018","journal-title":"Sens. Actuators B Chem."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.isprsjprs.2014.08.015","article-title":"Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum removal","volume":"97","author":"Sanches","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","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_24","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_25","first-page":"226","article-title":"Grading method of disease severity of wheat stripe rust based on hyperspectral imaging technology","volume":"49","author":"Lei","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_26","first-page":"136004","article-title":"Discrimination of powdery mildew and yellow rust of winter wheat using high-resolution hyperspectra and imageries","volume":"46","author":"Liang","year":"2017","journal-title":"Infrared Laser Eng."},{"key":"ref_27","unstructured":"Li, Z.Q., and Zeng, S.M. (2002). Wheat Stripe Rust in China, China Agriculture Press."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(98)00059-5","article-title":"Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches","volume":"66","author":"Blackburn","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0176-1617(11)81633-0","article-title":"Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation","volume":"143","author":"Gitelson","year":"1994","journal-title":"J. Plant Physiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13007-018-0281-z","article-title":"A robust vegetation index for remotely assessing chlorophyll content of dorsiventral leaves across several species in different seasons","volume":"14","author":"Lu","year":"2018","journal-title":"Plant Methods"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1016\/S0176-1617(96)80284-7","article-title":"Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll","volume":"148","author":"Gitelson","year":"1996","journal-title":"J. Plant Physiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.pmpp.2013.09.001","article-title":"The effect of Puccinia striiformis f. sp. tritici on the levels of water-soluble carbohydrates and the photosynthetic rate in wheat leaves","volume":"84","author":"Chang","year":"2013","journal-title":"Physiol. Mol. Plant Pathol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4059","DOI":"10.1080\/01431161.2018.1454620","article-title":"Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress","volume":"39","author":"He","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1094\/PDIS-12-14-1296-RE","article-title":"Virulence variations of Puccinia striiformis f. sp. tritici isolates collected from berberis spp. in China","volume":"100","author":"Wang","year":"2016","journal-title":"Plant Dis."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"27790","DOI":"10.1038\/srep27790","article-title":"Hyperspectral imaging for determining pigment contents in cucumber leaves in response to angular leaf spot disease","volume":"6","author":"Zhao","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/S0034-4257(98)00032-7","article-title":"Derivative analysis of hyperspectral data","volume":"66","author":"Tsai","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.postharvbio.2007.09.019","article-title":"Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content","volume":"48","author":"Peng","year":"2008","journal-title":"Postharvest Biol. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.jfoodeng.2008.12.021","article-title":"Identification of mushrooms subjected to freeze damage using hyperspectral imaging","volume":"93","author":"Gowen","year":"2009","journal-title":"J. Food Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.aca.2012.01.004","article-title":"Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging","volume":"719","author":"Barbin","year":"2012","journal-title":"Anal. Chim. Acta"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1007\/s12161-015-0278-4","article-title":"Nondestructive determination of soluble solids content of \u2018fuji\u2019 apples produced in different areas and bagged with different materials during ripening","volume":"9","author":"Dong","year":"2016","journal-title":"Food Anal. Methods"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1111\/1467-9868.00196","article-title":"Probabilistic principal component analysis","volume":"61","author":"Tipping","year":"1999","journal-title":"J. R. Stat. Soc. Series B Stat. Methodol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0169-7439(01)00119-8","article-title":"The successive projections algorithm for variable selection in spectroscopic multicomponent analysis","volume":"57","author":"Araujo","year":"2001","journal-title":"Chem. Intell. Lab. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.aca.2009.06.046","article-title":"Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration","volume":"648","author":"Li","year":"2009","journal-title":"Anal. Chim. Acta"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.postharvbio.2008.11.008","article-title":"Detecting chilling injury in red delicious apple using hyperspectral imaging and neural networks","volume":"52","author":"ElMasry","year":"2009","journal-title":"Postharvest Biol. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.compag.2018.10.019","article-title":"Nondestructive detection of chilled mutton freshness based on multi-label information fusion and adaptive bp neural network","volume":"155","author":"Jiang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","first-page":"294","article-title":"Non-destructive hyperspectral measurement model of chlorophyll content for citrus leaves","volume":"31","author":"Yue","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/07352681003617285","article-title":"Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging","volume":"29","author":"Bock","year":"2010","journal-title":"Crit. Rev. Plant Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","article-title":"Plant disease detection by imaging sensors\u2014Parallels and specific demands for precision agriculture and plant phenotyping","volume":"100","author":"Mahlein","year":"2016","journal-title":"Plant Dis."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2005.12.011","article-title":"A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method","volume":"101","author":"Cho","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, H., Qin, F., Ruan, L., Wang, R., Liu, Q., Ma, Z., Li, X., Cheng, P., and Wang, H. (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_52","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.postharvbio.2018.12.007","article-title":"Early detection of decay on apples using hyperspectral reflectance imaging combining both principal component analysis and improved watershed segmentation method","volume":"149","author":"Li","year":"2019","journal-title":"Postharvest Biol. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/952\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:34:21Z","timestamp":1760186061000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,23]]},"references-count":52,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["s19040952"],"URL":"https:\/\/doi.org\/10.3390\/s19040952","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,23]]}}}