Wheat Yield and Protein Estimation with Handheld and Unmanned Aerial Vehicle-Mounted Sensors

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dc.contributor.authorWalsh, Olga S.en
dc.contributor.authorMarshall, Juliet M.en
dc.contributor.authorNambi, Evaen
dc.contributor.authorJackson, Chad A.en
dc.contributor.authorAnsah, Emmanuella Owusuen
dc.contributor.authorLamichhane, Ritikaen
dc.contributor.authorMcClintick-Chess, Jordanen
dc.contributor.authorBautista, Franciscoen
dc.date.accessioned2025-01-22T14:22:28Zen
dc.date.available2025-01-22T14:22:28Zen
dc.date.issued2023-01-10en
dc.description.abstractAccurate sensor-based prediction of crop yield and grain quality in-season would enable growers to adjust nitrogen (N) fertilizer management for optimized production. This study assessed the feasibility (and compared the accuracy) of wheat (Triticum aestivum L.) yield, grain N uptake, and protein content prediction with in-season crop spectral reflectance measurements (Normalized Difference Vegetative Index, NDVI) obtained with a handheld GreenSeeker (GS) sensor and an Unmanned Aerial Vehicle (UAV)-mounted sensor. A strong positive correlation was observed between GS NDVI and UAV NDVI at Feekes 5 (R2 = 0.78) and Feekes 10 (R2 = 0.70). At Feekes 5, GS NDVI and UAV NDVI explained 42% and 43% of wheat yield, respectively. The correlation was weaker at Feekes 10 (R2 of 0.34 and 0.25 for GS NDVI and UAV NDVI, respectively). The accuracy of wheat grain N uptake prediction was comparable to that of yield: the R2 values for GS NDVI and UAV NDVI were 0.53 and 0.37 at Feekes 5 and 0.13 and 0.20 at Feekes 10. We found that neither GS NDVI nor UAV NDVI in-season data were useful in prediction of wheat grain protein content. In conclusion, wheat yield and grain N uptake can be estimated at Feekes 5 using either handheld or aerial based NDVI with comparable accuracy.en
dc.description.versionPublished versionen
dc.format.extent14 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN 207 (Article number)en
dc.identifier.doihttps://doi.org/10.3390/agronomy13010207en
dc.identifier.eissn2073-4395en
dc.identifier.issn2073-4395en
dc.identifier.issue1en
dc.identifier.orcidWalsh, Olga [0000-0002-2958-931X]en
dc.identifier.urihttps://hdl.handle.net/10919/124309en
dc.identifier.volume13en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectnormalized difference vegetative indexen
dc.subjecthandheld sensoren
dc.subjectunmanned aerial vehicleen
dc.subjectwheaten
dc.subjectgrain yielden
dc.subjectgrain proteinen
dc.subjectgrain N uptakeen
dc.titleWheat Yield and Protein Estimation with Handheld and Unmanned Aerial Vehicle-Mounted Sensorsen
dc.title.serialAgronomyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Agriculture & Life Sciencesen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Agriculture & Life Sciences/CALS T&R Facultyen
pubs.organisational-groupVirginia Tech/Agriculture & Life Sciences/School of Plant and Environmental Sciencesen

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