Identifying Optimal Wavelengths as Disease Signatures Using Hyperspectral Sensor and Machine Learning

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dc.contributor.authorWei, Xingen
dc.contributor.authorJohnson, Marcela A.en
dc.contributor.authorLangston, David B.en
dc.contributor.authorMehl, Hillary L.en
dc.contributor.authorLi, Songen
dc.contributor.departmentSchool of Plant and Environmental Sciencesen
dc.contributor.departmentVirginia Agricultural Experiment Stationen
dc.date.accessioned2021-07-23T17:28:55Zen
dc.date.available2021-07-23T17:28:55Zen
dc.date.issued2021-07-19en
dc.date.updated2021-07-23T13:27:39Zen
dc.description.abstractHyperspectral sensors combined with machine learning are increasingly utilized in agricultural crop systems for diverse applications, including plant disease detection. This study was designed to identify the most important wavelengths to discriminate between healthy and diseased peanut (<i>Arachis hypogaea</i> L.) plants infected with <i>Athelia rolfsii</i>, the causal agent of peanut stem rot, using in-situ spectroscopy and machine learning. In greenhouse experiments, daily measurements were conducted to inspect disease symptoms visually and to collect spectral reflectance of peanut leaves on lateral stems of plants mock-inoculated and inoculated with <i>A. rolfsii</i>. Spectrum files were categorized into five classes based on foliar wilting symptoms. Five feature selection methods were compared to select the top 10 ranked wavelengths with and without a custom minimum distance of 20 nm. Recursive feature elimination methods outperformed the chi-square and SelectFromModel methods. Adding the minimum distance of 20 nm into the top selected wavelengths improved classification performance. Wavelengths of 501–505, 690–694, 763 and 884 nm were repeatedly selected by two or more feature selection methods. These selected wavelengths can be applied in designing optical sensors for automated stem rot detection in peanut fields. The machine-learning-based methodology can be adapted to identify spectral signatures of disease in other plant-pathogen systems.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationWei, X.; Johnson, M.A.; Langston, D.B., Jr.; Mehl, H.L.; Li, S. Identifying Optimal Wavelengths as Disease Signatures Using Hyperspectral Sensor and Machine Learning. Remote Sens. 2021, 13, 2833.en
dc.identifier.doihttps://doi.org/10.3390/rs13142833en
dc.identifier.urihttp://hdl.handle.net/10919/104379en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectsoilborne diseasesen
dc.subjectpeanut stem roten
dc.subjectAthelia rolfsiien
dc.subjectSclerotium rolfsiien
dc.subjectspectroscopyen
dc.subjectrandom foresten
dc.subjectsupport vector machineen
dc.subjectrecursive feature eliminationen
dc.subjectfeature selectionen
dc.subjecthyperspectral band selectionen
dc.titleIdentifying Optimal Wavelengths as Disease Signatures Using Hyperspectral Sensor and Machine Learningen
dc.title.serialRemote Sensingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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Journal Articles, Multidisciplinary Digital Publishing Institute (MDPI)
Scholarly Works, School of Plant and Environmental Sciences
Scholarly Works, Virginia Agricultural Experiment Station