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Low-cost, handheld near-infrared spectroscopy for root dry matter content prediction in cassava

dc.contributor.authorHershberger, J.M.
dc.contributor.authorMbanjo, E.
dc.contributor.authorPeteti, P.
dc.contributor.authorIkpan, A.
dc.contributor.authorOgunpaimo, K.
dc.contributor.authorNafiu, K.
dc.contributor.authorRabbi, I.Y.
dc.contributor.authorGore, M.A.
dc.date.accessioned2022-12-13T09:42:59Z
dc.date.available2022-12-13T09:42:59Z
dc.date.issued2022
dc.identifier.citationHershberger, J.M., Mbanjo, E., Peteti, P., Ikpan, A., Ogunpaimo, K., Nafiu, K., ... & Gore, M.A. (2022). Low‐cost, handheld near‐infrared spectroscopy for root dry matter content prediction in cassava. The Plant Phenome Journal, 5(1): e20040,1-14.
dc.identifier.issn2578-2703
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7969
dc.description.abstractOver 800 million people across the tropics rely on cassava (Manihot esculenta Crantz) as a major source of calories. While the root dry matter content (RDMC) of this starchy root crop is important for both producers and consumers, characterization of RDMC by traditional methods is time-consuming and laborious for breeding programs. Alternate phenotyping methods have been proposed but lack the accuracy, cost, or speed ultimately needed for cassava breeding programs. For this reason, we investigated the use of a low-cost, handheld near-infrared spectrometer (740–1070 nm) for field-based RDMC prediction in cassava. Oven-dried measurements of RDMC were paired with 21,044 scans of roots of 376 diverse genotypes from 10 field trials in Nigeria and grouped into training and test sets based on cross-validation schemes relevant to plant breeding programs. Mean partial least squares regression model performance ranged from R2P = 0.62–0.89 for within-trial predictions, which is within the range achieved with laboratory-grade spectrometers in previous studies. Relative to other factors, model performance was highly affected by the inclusion of samples from the same environment in both the training and test sets. With appropriate model calibration, the tested spectrometer will allow for field-based collection of spectral data with a smartphone for accurate RDMC prediction and potentially other quality traits, a step that could be easily integrated into existing harvesting workflows of cassava breeding programs.
dc.description.sponsorshipUnited States Agency for International Development
dc.description.sponsorshipForeign, Commonwealth and Development Office NextGen Cassava
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.description.sponsorshipCornell Institute for Digital Agriculture, Cornell University
dc.format.extent1-14
dc.language.isoen
dc.subjectCassava
dc.subjectManihot Esculenta
dc.subjectNear Infrared
dc.subjectDry Matter Content
dc.subjectVarieties
dc.subjectGenotypes
dc.subjectNigeria
dc.titleLow-cost, handheld near-infrared spectroscopy for root dry matter content prediction in cassava
dc.typeJournal Article
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationCornell University
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeBiometrics
cg.identifier.bibtexciteidHERSHBERGER:2022
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectAgronomy
cg.iitasubjectBiometrics
cg.iitasubjectCassava
cg.iitasubjectFood Security
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Production
cg.journalThe Plant Phenome Journal
cg.notesOpen Access Article; Published online: 31 Mar 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/10.1002/ppj2.20040
cg.iitaauthor.identifierEdwige Gaby Nkouaya Mbanjo: 0000-0002-9982-1137
cg.iitaauthor.identifierPrasad Peteti: 0000-0002-6013-8947
cg.iitaauthor.identifierIsmail Rabbi: 0000-0001-9966-2941
cg.futureupdate.requiredNo
cg.identifier.issue1
cg.identifier.volume5


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