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

dc.contributor.authorPeng, Y.
dc.contributor.authorDallas, M.M.
dc.contributor.authorAscencio‑Ibanez, J.T.
dc.contributor.authorHoyer, J.S.
dc.contributor.authorLegg, J.
dc.contributor.authorHanley‑Bowdoin, L.
dc.contributor.authorGrieve, B.
dc.contributor.authorYin, H.
dc.date.accessioned2023-03-01T07:57:15Z
dc.date.available2023-03-01T07:57:15Z
dc.date.issued2022-02-24
dc.identifier.citationPeng, Y., Dallas, M.M., Ascencio-Ibáñez, J.T., Hoyer, J.S., Legg, J., Hanley-Bowdoin, L., ... & Yin, H. (2022). Early detection of plant virus infection using multispectral imaging and spatial–spectral machine learning. Scientific Reports, 12(1): 3113, 1-14.
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/20.500.12478/8069
dc.description.abstractCassava brown streak disease (CBSD) is an emerging viral disease that can greatly reduce cassava productivity, while causing only mild aerial symptoms that develop late in infection. Early detection of CBSD enables better crop management and intervention. Current techniques require laboratory equipment and are labour intensive and often inaccurate. We have developed a handheld active multispectral imaging (A-MSI) device combined with machine learning for early detection of CBSD in real-time. The principal benefits of A-MSI over passive MSI and conventional camera systems are improved spectral signal-to-noise ratio and temporal repeatability. Information fusion techniques further combine spectral and spatial information to reliably identify features that distinguish healthy cassava from plants with CBSD as early as 28 days post inoculation on a susceptible and a tolerant cultivar. Application of the device has the potential to increase farmers’ access to healthy planting materials and reduce losses due to CBSD in Africa. It can also be adapted for sensing other biotic and abiotic stresses in real-world situations where plants are exposed to multiple pest, pathogen and environmental stresses.
dc.format.extent1-14
dc.language.isoen
dc.subjectPlant Viruses
dc.subjectMachine Learning
dc.subjectCassava
dc.subjectProductivity
dc.titleEarly detection of plant virus infection using multispectral imaging and spatial-spectral machine learning
dc.typeJournal Article
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationUniversity of Manchester
cg.contributor.affiliationNorth Carolina State University
cg.contributor.affiliationRutgers University
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.coverage.regionAfrica
cg.coverage.regionEast Africa
cg.coverage.countryKenya
cg.coverage.countryUganda
cg.coverage.hubEastern Africa Hub
cg.researchthemePlant Production and Health
cg.identifier.bibtexciteidPENG:2022
cg.isijournalISI Journal
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectAgronomy
cg.iitasubjectCassava
cg.iitasubjectFood Security
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Diseases
cg.iitasubjectPlant Health
cg.iitasubjectPlant Production
cg.journalScientific Reports
cg.notesOpen Access Journal; Published online: 24 Feb 2022
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.1038/s41598-022-06372-8
cg.iitaauthor.identifierJames Legg: 0000-0003-4140-3757
cg.futureupdate.requiredNo
cg.identifier.issue1
cg.identifier.volume12


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