{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T22:00:22Z","timestamp":1775944822289,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,11]],"date-time":"2023-06-11T00:00:00Z","timestamp":1686441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Science and Technology \u2013 Engineering Research and Development for Technology (DOST-ERDT)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring the plant\u2019s health and early detection of disease are essential to facilitate effective management, decrease disease spread, and minimize yield loss. Spectroscopic techniques in remote sensing offer less laborious methods and high spatiotemporal scale to monitor diseases in crops. Spectral measurements during the development of disease infection may reveal differences among diseases and determine the stage it can be effectively detected. In this study, spectral analysis was performed over the visible and near-infrared (400\u2013850 nm) portions of the spectrum to detect and differentiate three major rice diseases in the Philippines, namely tungro, BLB, and blast disease. Reflectance of infected rice leaves was recorded repeatedly from inoculation to the late stage of each disease. Results show that spectral reflectance is characteristically affected by each disease, resulting in different spectral, signature sensitivity, and first-order derivatives. Red and red-edge wavelength ranges are the most sensitive to the three diseases. Near-infrared wavelengths decreased as tungro and blast diseases progressed. In addition, the spectral reflectance was resampled to common reflectance sensitivity bands of optical sensors and used in the cluster analysis. It showed that BLB and blast can be detected in the early disease stage on the IRRI Standard Evaluation System (SES) scale of 1 and 3, respectively. Alternatively, tungro was detected in its later stage, with an 11\u201330% height reduction and no distinct yellow to yellow-orange discoloration (5 SES scale). Three regression techniques, Partial Least Square, Random Forest, and Support Vector Regression were performed separately on each disease to develop models predicting its severity. The validation results of the PLSR and SVR models in tungro and blast show accuracy levels that are promising to be used in estimating the severity of the disease in leaves while RFR shows the best results for BLB. Early disease detection and regression models from spectral measurements and analysis for disease severity estimation can help in disease monitoring and proper disease management implementation.<\/jats:p>","DOI":"10.3390\/rs15123058","type":"journal-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T01:59:07Z","timestamp":1686535147000},"page":"3058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Leaf Spectral Analysis for Detection and Differentiation of Three Major Rice Diseases in the Philippines"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5256-4676","authenticated-orcid":false,"given":"Jean Rochielle F.","family":"Mirandilla","sequence":"first","affiliation":[{"name":"Philippine Rice Research Institute, Science City of Munoz 3119, Philippines"},{"name":"Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo 183-8509, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0295-396X","authenticated-orcid":false,"given":"Megumi","family":"Yamashita","sequence":"additional","affiliation":[{"name":"Graduate School of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu, Tokyo 183-8509, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitsunori","family":"Yoshimura","sequence":"additional","affiliation":[{"name":"Department of Forest Science, College of Bioresource Sciences, Nihon University, 1866, Kameino, Fujisawa 252-0880, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2239-5870","authenticated-orcid":false,"given":"Enrico C.","family":"Paringit","sequence":"additional","affiliation":[{"name":"Department of Science and Technology, Philippine Council for Industry, Energy and Emerging Technologies R&D (DOST-PCIEERD), University of the Philippines Diliman, Quezon City 1101, Philippines"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"key":"ref_1","unstructured":"Ponce, E.R., and Inocencio, A.B. (2019, July 04). Toward a More Resilient and Competitive Philippine Rice Industry: Lessons from the Past Three Decades. Available online: https:\/\/www.irri.org\/resources\/publications\/books\/toward-a-more-resilient-and-competitive-philippine-rice-industry-lesson-from-the-past-three-decades."},{"key":"ref_2","first-page":"1","article-title":"Game Changer: Is PH rice ready to compete at least regionally?","volume":"6","author":"Bordey","year":"2015","journal-title":"Rice Sci. Decis. Mak."},{"key":"ref_3","unstructured":"Philippine Statistics Authority (PSA) (2019, July 04). Selected Statistics on Agriculture 2018, Available online: https:\/\/psa.gov.ph."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1038\/s41559-018-0793-y","article-title":"The global burden of pathogens and pests on major food crops","volume":"3","author":"Savary","year":"2019","journal-title":"Nat. Ecol. Evol."},{"key":"ref_5","unstructured":"PhilRice (2019, June 05). Preventing Rice Pests and Diseases in Rainy Seasons. 4 July 2016, Available online: https:\/\/www.philrice.gov.ph\/preventing-rice-pests-diseases-rainy-season\/."},{"key":"ref_6","unstructured":"Herriman, R. (2019, June 05). Rice Blast, Sheath Blight Threatens Iloilo Rice Crops. Outbreak News Today. 10 September 2016. Available online: http:\/\/outbreaknewstoday.com\/rice-blast-sheath-blight-threaten-iloilo-rice-crops-44115\/http:\/\/outbreaknewstoday.com\/rice-blast-sheath-blight-threaten-iloilo-rice-crops-44115\/."},{"key":"ref_7","unstructured":"IRRI, and Administration G\u00e9n\u00e9rale de la Coop\u00e9ration au D\u00e9veloppement (1989). Bacterial Blight of Rice: Proceedings of the International Workshop on Bacterial Blight of Rice, 14\u201318 March 1988, International Rice Research Institute."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"537","DOI":"10.3390\/bios5030537","article-title":"Current and prospective methods for plant disease detection","volume":"4","author":"Fang","year":"2015","journal-title":"Biosensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13593-014-0246-1","article-title":"Advanced methods of plant disease detection. A review","volume":"35","author":"Martinelli","year":"2015","journal-title":"Agron. Sustain. Dev."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Venkateshwarulu, B., Shanker, A.K., Shanker, C., and Maheswari, M. (2011). Crop Stress and Its Management: Perspectives and Strategies, Springer Science Business Media BV.","DOI":"10.1007\/978-94-007-2220-0"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1111\/j.1472-4642.2011.00761.x","article-title":"Benefits of hyperspectral remote sensing for tracking plant invasions","volume":"17","author":"He","year":"2011","journal-title":"Divers. Distrib."},{"key":"ref_13","unstructured":"Apan, A., Datt, B., and Kelly, R. (2005, January 12\u201316). Detection of pests and diseases in vegetable crops using hyperspectral sensing: A comparison of reflectance data for different sets of symptoms. Proceedings of the SSC 2005 Spatial Intelligence, Innovation, and Praxis: The national biennial conference of the Spatial Science Institute, Melbourne 2005, Melbourne, Australia."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1094\/PHYTO.2001.91.3.316","article-title":"Detection of rice panicle blast with a multispectral radiometer and the potential of using airborne multispectral scanners","volume":"91","author":"Kobayashi","year":"2001","journal-title":"Phytopathology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2174\/1874331501610010028","article-title":"Assessment of rice panicle blast disease using airborne hyperspectral imagery","volume":"10","author":"Kobayashi","year":"2016","journal-title":"Open Agric. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1631\/jzus.2007.B0738","article-title":"Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression, and partial least-square regression","volume":"8","author":"Liu","year":"2007","journal-title":"J. Zhejiang Univ. Sci. B"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s12600-013-0375-0","article-title":"Characterization of brown planthopper damage on rice crops through hyperspectral remote sensing under field conditions","volume":"42","author":"Prasannakumar","year":"2014","journal-title":"Phytoparasitica"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1080\/09670874.2015.1072652","article-title":"Monitoring of bacterial leaf blight in rice using ground-based hyperspectral and LISS IV satellite data in Kurnool, Andhra Pradesh, India","volume":"61","author":"Das","year":"2015","journal-title":"Int. J. Pest Manag."},{"key":"ref_19","unstructured":"Singh, B., Singh, M., Singh, G., Suri, K., Pannu, P.P.S., and Bal, S.K. (2012, January 1\u20133). Hyperspectral data for the detection of rice bacterial leaf blight disease. Proceedings of the Agro-Informatics and Precision Agriculture (AIPA), Hyderabad, India."},{"key":"ref_20","unstructured":"Department of Agriculture National Seed Industry Council (1997). National Cooperative Testing Manual for Rice: Guidelines and Policies."},{"key":"ref_21","unstructured":"International Rice Research Institute (2013). Standard Evaluation System for Rice, International Rice Research Institute. [4th ed.]."},{"key":"ref_22","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_23","unstructured":"Tsai, F., and Philpot, W. (1996, January 23\u201326). Derivative Analysis of Hyperspectral Data. Proceedings of the Remote Sensing for Geography, Geology, Land Planning, and Cultural Heritage, Taormina, Italy."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.fcr.2013.11.012","article-title":"Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects","volume":"156","author":"Yuan","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Everitt, B.S., Landau, S., Leese, M., and Stahl, D. (2011). Cluster Analysis, Wiley. [5th ed.].","DOI":"10.1002\/9780470977811"},{"key":"ref_26","first-page":"1","article-title":"The pls package: Principal component and partial least squares regression in R","volume":"18","author":"Wehrens","year":"2007","journal-title":"J. Stat. Softw."},{"key":"ref_27","first-page":"18","article-title":"Classification and Regression by Random Forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_28","unstructured":"Meyer, D., Dimitriadou, E., Hornik, K., Weingesse, A., Leisch, F., Chang, C.C., and Lin, C.C. (2023, May 25). The e1071 Package: Misc. Functions of the Department of Statistics. Available online: http:\/\/cran.r-project.org\/web\/packages\/21071."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Awad, M., and Khanna, R. (2015). Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, Springer Nature.","DOI":"10.1007\/978-1-4302-5990-9"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1094\/PDIS.2002.86.2.88","article-title":"The biology, epidemiology, and management of rice Tungro disease in Asia","volume":"86","author":"Azzam","year":"2002","journal-title":"Plant Dis."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1111\/j.1439-0434.1979.tb01570.x","article-title":"Effect of rice Tungro virus on chlorophyll and anthocyanin pigments in two rice cultivars","volume":"94","author":"Subbarao","year":"1979","journal-title":"J. Phytopathol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.virol.2018.10.012","article-title":"Physical interaction of RTBV ORFI with D1 protein of Oryza sativa and Fe\/Zn homeostasis play a key role in symptoms development during rice Tungro disease to facilitate the insect mediated virus transmission","volume":"526","author":"Srilatha","year":"2019","journal-title":"Virology"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/B978-0-12-409548-9.10547-0","article-title":"Leaf pigment content","volume":"3","author":"Croft","year":"2018","journal-title":"Compr. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.isprsjprs.2007.02.001","article-title":"Red edge shift and biochemical content in grass canopies","volume":"62","author":"Muntanga","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3058\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:52:39Z","timestamp":1760125959000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/12\/3058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,11]]},"references-count":35,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15123058"],"URL":"https:\/\/doi.org\/10.3390\/rs15123058","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,11]]}}}