Unmanned aerial platform‑based multi‑spectral imaging for field phenotyping of maize

dc.creatorZaman-Allah, M.
dc.creatorVergara Diaz, O.
dc.creatorAraus, J.L.
dc.creatorZarco‑Tejada, P.J.
dc.creatorHornero, A.
dc.creatorHernández-Alba, A.
dc.creatorMagorokosho, C.
dc.creatorDas, B.
dc.creatorAmsal Tesfaye Tarekegne
dc.creatorCairns, J.E.
dc.creatorPrasanna, B.M.
dc.creatorOlsen, M.
dc.creatorCraufurd, P.
dc.date2016-06-13T16:05:34Z
dc.date2016-06-13T16:05:34Z
dc.date2015
dc.date.accessioned2023-07-17T19:59:50Z
dc.date.available2023-07-17T19:59:50Z
dc.identifierhttp://hdl.handle.net/10883/16941
dc.identifier10.1186/s13007-015-0078-2
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7510204
dc.descriptionBackground: Recent developments in unmanned aerial platforms (UAP) have provided research opportunities in assessing land allocation and crop physiological traits, including response to abiotic and biotic stresses. UAP-based remote sensing can be used to rapidly and cost-effectively phenotype large numbers of plots and field trials in a dynamic way using time series. This is anticipated to have tremendous implications for progress in crop genetic improvement. Results: We present the use of a UAP equipped with sensors for multispectral imaging in spatial field variability assessment and phenotyping for low-nitrogen (low-N) stress tolerance in maize. Multispectral aerial images were used to (1) characterize experimental fields for spatial soil-nitrogen variability and (2) derive indices for crop performance under low-N stress. Overall, results showed that the aerial platform enables to effectively characterize spatial field variation and assess crop performance under low-N stress. The Normalized Difference Vegetation Index (NDVI) data derived from spectral imaging presented a strong correlation with ground-measured NDVI, crop senescence index and grain yield. Conclusion: This work suggests that the aerial sensing platform designed for phenotyping studies has the potential to effectively assist in crop genetic improvement against abiotic stresses like low-N provided that sensors have enough resolution for plot level data collection. Limitations and future potential uses are also discussed.
dc.descriptionart.35
dc.formatPDF
dc.languageEnglish
dc.publisherBioMed Central
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dc.rightsOpen Access
dc.source1
dc.source11
dc.sourcePlant Methods
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectPhenotyping Platform
dc.subjectUAP
dc.subjectMAIZE
dc.subjectPHENOTYPES
dc.subjectREMOTE SENSING
dc.subjectUNMANNED AERIAL VEHICLES
dc.subjectNITROGEN FERTILIZERS
dc.titleUnmanned aerial platform‑based multi‑spectral imaging for field phenotyping of maize
dc.typeArticle
dc.coverageETHIOPIA
dc.coverageLondon, United Kingdom


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