Early detection of plant virus infection using multispectral imaging and machine learning

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cg.authorship.typesCGIAR and advanced research instituteen_US
cg.contributor.affiliationUniversity of Manchesteren_US
cg.contributor.affiliationRutgers Universityen_US
cg.contributor.affiliationNorth Carolina State Universityen_US
cg.contributor.affiliationRothamsted Researchen_US
cg.contributor.affiliationInternational Institute of Tropical Agricultureen_US
cg.coverage.countryTanzaniaen_US
cg.coverage.iso3166-alpha2TZen_US
cg.coverage.regionAfricaen_US
cg.coverage.regionEastern Africaen_US
cg.creator.identifierJames Legg: 0000-0003-4140-3757en_US
cg.howPublishedFormally Publisheden_US
cg.identifier.doihttps://doi.org/10.1079/planthealthcases.2024.0010en_US
cg.identifier.iitathemePLANT PRODUCTION & HEALTHen_US
cg.issn2959-880Xen_US
cg.journalPlant Health Casesen_US
cg.reviewStatusPeer Reviewen_US
cg.subject.actionAreaGenetic Innovationen_US
cg.subject.iitaCASSAVAen_US
cg.subject.iitaFARM MANAGEMENTen_US
cg.subject.iitaFOOD SECURITYen_US
cg.subject.iitaPLANT DISEASESen_US
cg.subject.iitaPESTS OF PLANTSen_US
dc.contributor.authorGrieve, B.en_US
dc.contributor.authorDuffy, S.en_US
dc.contributor.authorDallas, M. M.en_US
dc.contributor.authorAscencio‑Ibanez, J. T.en_US
dc.contributor.authorAlonso-Chavez, V.en_US
dc.contributor.authorLegg, J.en_US
dc.contributor.authorHanley-Bowdoin, L.en_US
dc.contributor.authorYin, H.en_US
dc.date.accessioned2024-11-12T14:31:39Zen_US
dc.date.available2024-11-12T14:31:39Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/159585en_US
dc.titleEarly detection of plant virus infection using multispectral imaging and machine learningen_US
dcterms.abstractClimate change-resilient crops like cassava are projected to play a key role in 21st-century food security. However, cassava production in East Africa is limited by RNA viruses that cause cassava brown streak disease (CBSD). CBSD typically causes subtle or no symptoms on stems and leaves, while destroying the root tissue, which means farmers are often unaware their fields are infected until they have a failed harvest. The subtle symptoms of CBSD have made it difficult to study the spread of the disease in fields. We will use an engineering advancement, our active multispectral imager (MSI), to rapidly determine the infection status of plants in the field in Tanzania. The MSI observes leaves using many different wavelengths, and the resulting light spectra are interpreted by machine learning models trained on cassava leaf scans. Under laboratory conditions, the MSI detects CBSD infection with 95% accuracy at 28 days post-infection, when plants have no visible symptoms. Our multinational team is studying and modeling the spread of CBSD to assess the efficacy of using the MSI to detect and remove infected cassava plants from fields before CBSD can spread. In addition to improving the food security of people who eat cassava in sub-Saharan Africa, our technology and modeling framework may be useful in diseases of other vegetatively propagated crops such as banana/plantain, potato, sweet potato, and yam.en_US
dcterms.accessRightsLimited Accessen_US
dcterms.audienceScientistsen_US
dcterms.available2024-07en_US
dcterms.bibliographicCitationGrieve, B., Duffy, S., Dallas, M. M., Ascencio-Ibáñez, J. T., Alonso-Chavez, V., Legg, J., ... & Yin, H. (2024). Early detection of plant virus infection using multispectral Imaging and machine learning. Plant Health Cases, 1-11.en_US
dcterms.extent1-11en_US
dcterms.issued2024en_US
dcterms.languageenen_US
dcterms.licenseCopyrighted; all rights reserveden_US
dcterms.publisherCABI Publishingen_US
dcterms.subjectcassava brown streak diseaseen_US
dcterms.subjectcassavaen_US
dcterms.subjectmultispectral imageren_US
dcterms.subjectmachine learningen_US
dcterms.subjectPlant virusesen_US
dcterms.typeCase Studyen_US

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