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

dc.contributor.authorGrieve, B.
dc.contributor.authorDuffy, S.
dc.contributor.authorDallas, M.M.
dc.contributor.authorAscencio‑Ibanez, J.T.
dc.contributor.authorAlonso-Chavez, V.
dc.contributor.authorLegg, J.
dc.contributor.authorHanley-Bowdoin, L.
dc.contributor.authorYin, H.
dc.date.accessioned2025-01-08T11:34:25Z
dc.date.available2025-01-08T11:34:25Z
dc.date.issued2024-07-31
dc.identifier.citationGrieve, 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, (2024): 1-11.
dc.identifier.issn2959-880X
dc.identifier.urihttps://hdl.handle.net/20.500.12478/8650
dc.description.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.
dc.format.extent1-11
dc.language.isoen
dc.subjectCassava Brown Streak Disease
dc.subjectCassava
dc.subjectMultispectral Imager
dc.subjectMachine Learning
dc.subjectPlant Viruses
dc.titleEarly detection of plant virus infection using multispectral imaging and machine learning
dc.typeJournal Article
cg.contributor.affiliationUniversity of Manchester
cg.contributor.affiliationRutgers University
cg.contributor.affiliationNorth Carolina State University
cg.contributor.affiliationRothamsted Research
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.coverage.regionAfrica
cg.coverage.regionEast Africa
cg.coverage.countryTanzania
cg.coverage.hubEastern Africa Hub
cg.researchthemePlant Production and Health
cg.identifier.bibtexciteidGRIEVE:2024
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectCassava
cg.iitasubjectFarm Management
cg.iitasubjectFood Security
cg.iitasubjectPests of Plants
cg.iitasubjectPlant Diseases
cg.iitasubjectPlant Health
cg.journalPlant Health Cases
cg.accessibilitystatusLimited Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCopyrighted; all rights reserved
cg.targetaudienceScientists
cg.identifier.doihttps://doi.org/10.1079/planthealthcases.2024.0010
cg.iitaauthor.identifierJames Legg: 0000-0003-4140-3757
cg.futureupdate.requiredNo


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