{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:25:36Z","timestamp":1776205536519,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Indiana Corn and Marketing Council","award":["40001376"],"award-info":[{"award-number":["40001376"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Tar spot is a foliar disease of corn characterized by fungal fruiting bodies that resemble tar spots. The disease emerged in the U.S. in 2015, and severe outbreaks in 2018 caused an economic impact on corn yields throughout the Midwest. Adequate epidemiological surveillance and disease quantification are necessary to develop immediate and long-term management strategies. This study presents a measurement framework that evaluates the disease severity of tar spot using unmanned aircraft systems (UAS)-based plant phenotyping and regression techniques. UAS-based plant phenotypic information, such as canopy cover, canopy volume, and vegetation indices, were used as explanatory variables. Visual estimations of disease severity were performed by expert plant pathologists per experiment plot basis and used as response variables. Three regression methods, namely ordinary least squares (OLS), support vector regression (SVR), and multilayer perceptron (MLP), were used to determine an optimal regression method for UAS-based tar spot measurement. The cross-validation results showed that the regression model based on MLP provides the highest accuracy of disease measurements. By training and testing the model with spatially separated datasets, the proposed regression model achieved a Lin\u2019s concordance correlation coefficient (\u03c1c) of 0.82 and a root mean square error (RMSE) of 6.42. This study demonstrated that we could use the proposed UAS-based method for the disease quantification of tar spot, which shows a gradual spectral response as the disease develops.<\/jats:p>","DOI":"10.3390\/rs13132567","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T02:44:39Z","timestamp":1625107479000},"page":"2567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Tar Spot Disease Quantification Using Unmanned Aircraft Systems (UAS) Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2337-9693","authenticated-orcid":false,"given":"Sungchan","family":"Oh","sequence":"first","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1484-1199","authenticated-orcid":false,"given":"Da-Young","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Botany and Plant Pathology, College of Agriculture, Purdue University, West Lafayette, IN 47907, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2392-193X","authenticated-orcid":false,"given":"Carlos","family":"Gongora-Canul","sequence":"additional","affiliation":[{"name":"Department of Botany and Plant Pathology, College of Agriculture, Purdue University, West Lafayette, IN 47907, USA"},{"name":"Tecnol\u00f3gico Nacional de M\u00e9xico\/IT Conkal, Av. Tecnol\u00f3gico s\/n, Conkal, Yucat\u00e1n 97345, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4050-0301","authenticated-orcid":false,"given":"Akash","family":"Ashapure","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA"}]},{"given":"Joshua","family":"Carpenter","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA"}]},{"given":"A. P.","family":"Cruz","sequence":"additional","affiliation":[{"name":"Department of Botany and Plant Pathology, College of Agriculture, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Mariela","family":"Fernandez-Campos","sequence":"additional","affiliation":[{"name":"Department of Botany and Plant Pathology, College of Agriculture, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Brenden Z.","family":"Lane","sequence":"additional","affiliation":[{"name":"Department of Botany and Plant Pathology, College of Agriculture, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"Darcy E. P.","family":"Telenko","sequence":"additional","affiliation":[{"name":"Department of Botany and Plant Pathology, College of Agriculture, Purdue University, West Lafayette, IN 47907, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1176-3540","authenticated-orcid":false,"given":"Jinha","family":"Jung","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2287-6754","authenticated-orcid":false,"given":"C. 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Mex. Fitopatol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mueller, D., Wise, K., and Sisson, A. (2018). Corn disease management:Corn disease loss estimate from the United States and Ontario, Canada-2017. CP 2007 17 W. Crop Prot. Netw.","DOI":"10.31274\/cpn-20190620-040"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Telenko, D.E.P., Chilvers, M.I., Kleczewski, N., Smith, D.L., Byrne, A.M., Devillez, P., Diallo, T., Higgins, R., Joos, D., and Kohn, K. (2019). How tar spot of corn impacted hybrid yields during the 2018 Midwest epidemic. Crop Prot. Netw.","DOI":"10.31274\/cpn-20190729-002"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1094\/PDIS-12-15-1506-PDN","article-title":"First report of tar spot on corn caused by phyllachora maydis in the United States","volume":"100","author":"Ruhl","year":"2016","journal-title":"Plant Dis."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1080\/09670879409371868","article-title":"Control of tar spot of maize and its effect on yield","volume":"40","author":"Bajet","year":"1994","journal-title":"Int. J. Pest Manag."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Madden, L.V., Hughes, G., and van den Bosch, F. (2017). The Study of Plant Disease Epidemics, American Phytopathological Society.","DOI":"10.1094\/9780890545058"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1002\/rob.21706","article-title":"UAV Localization in Row Crops","volume":"34","author":"Anthony","year":"2017","journal-title":"J. F. Robot."},{"key":"ref_10","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":"CRC. Crit. Rev. Plant Sci."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.07.007","article-title":"Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak","volume":"131","author":"Dash","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.compag.2016.08.021","article-title":"A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding","volume":"128","author":"Bai","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.compag.2016.07.028","article-title":"Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging","volume":"127","author":"Ge","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1016\/j.molp.2015.06.005","article-title":"A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria","volume":"8","author":"Fahlgren","year":"2015","journal-title":"Mol. Plant"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s42483-020-00049-8","article-title":"From visual estimates to fully automated sensor-based measurements of plant disease severity: Status and challenges for improving accuracy","volume":"2","author":"Bock","year":"2020","journal-title":"Phytopathol. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2019.04.003","article-title":"A novel framework to detect conventional tillage and no-tillage cropping system effect on cotton growth and development using multi-temporal UAS data","volume":"152","author":"Ashapure","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ashapure, A., Jung, J., Chang, A., Oh, S., Maeda, M., and Landivar, J. (2019). A comparative study of RGB and multispectral sensor-based cotton canopy cover modelling using multi-temporal UAS data. Remote Sens., 11.","DOI":"10.3390\/rs11232757"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.compag.2019.02.011","article-title":"Validation of agronomic UAV and field measurements for tomato varieties","volume":"158","author":"Enciso","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yeom, J., Jung, J., Chang, A., Maeda, M., and Landivar, J. (2018). Automated open cotton boll detection for yield estimation using unmanned aircraft vehicle (UAV) data. Remote Sens., 10.","DOI":"10.3390\/rs10121895"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.compag.2018.06.051","article-title":"Unmanned aerial system assisted framework for the selection of high yielding cotton genotypes","volume":"152","author":"Jung","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.compag.2017.07.008","article-title":"Crop height monitoring with digital imagery from Unmanned Aerial System (UAS)","volume":"141","author":"Chang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s10658-006-9027-2","article-title":"Impact of foliar diseases on photosynthesis, protein content and seed yield of alfalfa and efficacy of fungicide application","volume":"115","author":"Hwang","year":"2006","journal-title":"Eur. J. Plant Pathol."},{"key":"ref_24","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_25","first-page":"1","article-title":"Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize","volume":"11","author":"Vergara","year":"2015","journal-title":"Plant Methods"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2233","DOI":"10.1094\/PDIS-01-18-0054-RE","article-title":"Integrating Spectroscopy with Potato Disease Management","volume":"102","author":"Couture","year":"2018","journal-title":"Plant Dis."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s13007-015-0048-8","article-title":"Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach","volume":"11","author":"Liebisch","year":"2015","journal-title":"Plant Methods"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chivasa, W., Mutanga, O., and Biradar, C. (2020). UAV-based multispectral phenotyping for disease resistance to accelerate crop improvement under changing climate conditions. Remote Sens., 12.","DOI":"10.3390\/rs12152445"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1079\/PAVSNNR20116027","article-title":"Detection and measurement of plant disease symptoms using visible-wavelength photography and image analysis","volume":"6","author":"Bock","year":"2011","journal-title":"CAB Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"552","DOI":"10.3389\/fpls.2019.00552","article-title":"Application of remote sensing for phenotyping tar spot complex resistance in maize","volume":"10","author":"Loladze","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_31","unstructured":"(2021, June 28). Google Maps. Available online: https:\/\/www.google.com\/maps\/place\/41%C2%B027\u201920.0%22N+86%C2%B056\u201929.7%22W\/@41.455894,-86.9377691,961m\/data=!3m1!1e3!4m6!3m5!1s0x88119db35908d59d:0xe2e10c4ade176d89!7e2!8m2!3d41.4555489!4d-86.941579."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Acquaah, G. (2012). Principles of Plant Genetics and Breeding: Second Edition, John Wiley & Sons.","DOI":"10.1002\/9781118313718"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1094\/PDIS-05-19-0985-RE","article-title":"Novel sources of wheat head blast resistance in modern breeding lines and wheat wild relatives","volume":"104","author":"Cruppe","year":"2020","journal-title":"Plant Dis."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1094\/PDIS-09-15-1006-RE","article-title":"Climate suitability for Magnaporthe oryzae Triticum pathotype in the United States","volume":"100","author":"Cruz","year":"2016","journal-title":"Plant Dis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2252","DOI":"10.1094\/PDIS-12-19-2672-RE","article-title":"Epidemiological criteria to support breeding tactics against the emerging, high-consequence wheat blast disease","volume":"104","author":"Das","year":"2020","journal-title":"Plant Dis."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10681-017-2087-x","article-title":"Review on resistance to wheat blast disease (Magnaporthe oryzae Triticum) from the breeder point-of-view: Use of the experience on resistance to rice blast disease","volume":"214","author":"Vales","year":"2018","journal-title":"Euphytica"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s10658-005-1230-z","article-title":"Disease assessment concepts and the advancements made in improving the accuracy and precision of plant disease data","volume":"115","author":"Nutter","year":"2006","journal-title":"Eur. J. Plant Pathol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1080\/22797254.2019.1642143","article-title":"Aerial multispectral imagery for plant disease detection: Radiometric calibration necessity assessment","volume":"52","author":"Pourazar","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.isprsjprs.2019.01.016","article-title":"Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols","volume":"149","author":"Cao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3101","DOI":"10.1080\/01431161.2016.1230291","article-title":"A physical-based atmospheric correction algorithm of unmanned aerial vehicles images and its utility analysis","volume":"38","author":"Yu","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., and Frahm, J.M. (2016, January 27\u201330). Structure-from-Motion Revisited. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.isprsjprs.2020.04.016","article-title":"Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools","volume":"167","author":"Jiang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.2134\/agronj15.0150","article-title":"Canopeo: A powerful new tool for measuring fractional green canopy cover","volume":"107","author":"Patrignani","year":"2015","journal-title":"Agron. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/02757259509532298","article-title":"A review of vegetation indices","volume":"13","author":"Bannari","year":"1995","journal-title":"Remote Sens. Rev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3640","DOI":"10.1016\/j.rse.2011.09.002","article-title":"Broadband, red-edge information from satellites improves early stress detection in a New Mexico conifer woodland","volume":"115","author":"Eitel","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.compag.2008.03.009","article-title":"Verification of color vegetation indices for automated crop imaging applications","volume":"63","author":"Meyer","year":"2008","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1002\/wics.199","article-title":"The Bayesian information criterion: Background, derivation, and applications","volume":"4","author":"Neath","year":"2012","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hutcheson, G. (2011). Ordinary Least-Squares Regression. The Multivariate Social Scientist, SAGE Publications.","DOI":"10.4135\/9781446251119.n67"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A Library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"8082","DOI":"10.1029\/2019GL083015","article-title":"Using Satellite-Based Vegetation Cover as Indicator of Groundwater Storage in Natural Vegetation Areas","volume":"46","author":"Bhanja","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_51","first-page":"1","article-title":"A Practical Guide to Support Vector Classification","volume":"2003","author":"Hsu","year":"2008","journal-title":"BJU Int."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Pacifico, L.D.S., Macario, V., and Oliveira, J.F.L. (2018, January 8\u201313). Plant Classification Using Artificial Neural Networks. Proceedings of the International Joint Conference on Neural Networks, Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489701"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Masjedi, A., Zhao, J., and Crawford, M.M. (2017, January 23\u201328). Prediction of sorghum biomass based on image based features derived from time series of UAV images. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128413"},{"key":"ref_54","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_55","first-page":"1875","article-title":"Nonparametric regression using deep neural networks with relu activation function","volume":"48","year":"2020","journal-title":"Ann. Stat."},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"S-117","DOI":"10.2134\/agronj2006.0370c","article-title":"Application of spectral remote sensing for agronomic decisions","volume":"100","author":"Hatfield","year":"2008","journal-title":"Agron. J."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"22482","DOI":"10.1038\/srep22482","article-title":"Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants","volume":"6","author":"Wahabzada","year":"2016","journal-title":"Sci. Rep."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2567\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:24:19Z","timestamp":1760163859000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2567"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,30]]},"references-count":58,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13132567"],"URL":"https:\/\/doi.org\/10.3390\/rs13132567","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,30]]}}}