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

Background: 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...

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Detalles Bibliográficos
Autores: Zaman-Allah, M., Vergara Diaz, O., Araus, J.L., Tarekegne, A.T., Magorokosho, C., Zarco-Tejada, P.J., Hornero, A., Hernández-Alba, A., Das, B., Craufurd, P., Olsen, M., Prasanna, B.M., Cairns, J.E.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:México
Institución:Centro Internacional de Mejoramiento de Maíz y Trigo
Repositorio:Repositorio Institucional de Publicaciones Multimedia del CIMMYT
OAI Identifier:oai:repository.cimmyt.org:10883/16941
Acceso en línea:http://hdl.handle.net/10883/16941
Access Level:acceso abierto
Palabra clave:AGRICULTURAL SCIENCES AND BIOTECHNOLOGY
Phenotyping Platforms
UAP
MAIZE
PHENOTYPES
REMOTE SENSING
UNMANNED AERIAL VEHICLES
NITROGEN FERTILIZERS
Descripción
Sumario:Background: 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.

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