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dc.contributor.authorRoy, B.
dc.contributor.authorSagan, V.
dc.contributor.authorHaireti, A.
dc.contributor.authorNewcomb, M.
dc.contributor.authorTuberosa, R.
dc.contributor.authorLeBauer, D.
dc.contributor.authorShakoor, N.
dc.date.accessioned2024-03-22T17:33:59Z
dc.date.available2024-03-22T17:33:59Z
dc.date.issued2023-12-30
dc.identifier.citationRoy, B.; Sagan, V.; Haireti, A.; Newcomb, M.; Tuberosa, R.; LeBauer, D.; Shakoor, N. Early Detection of Drought Stress in Durum Wheat Using Hyperspectral Imaging and Photosystem Sensing. Remote Sens. 2024, 16, 155. https://doi.org/10.3390/rs16010155
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs16010155
dc.identifier.urihttp://hdl.handle.net/10150/671694
dc.description.abstractWheat, being the third largest U.S. crop and the principal food grain, faces significant risks from climate extremes such as drought. This necessitates identifying and developing methods for early water-stress detection to prevent yield loss and improve water-use efficiency. This study investigates the potential of hyperspectral imaging to detect the early stages of drought stress in wheat. The goal is to utilize this technology as a tool for screening and selecting drought-tolerant wheat genotypes in breeding programs. Additionally, this research aims to systematically evaluate the effectiveness of various existing sensors and methods for detecting early stages of water stress. The experiment was conducted in a durum wheat experimental field trial in Maricopa, Arizona, in the spring of 2019 and included well-watered and water-limited treatments of a panel of 224 replicated durum wheat genotypes. Spectral indices derived from hyperspectral imagery were compared against other plant-level indicators of water stress such as Photosystem II (PSII) and relative water content (RWC) data derived from proximal sensors. Our findings showed a 12% drop in photosynthetic activity in the most affected genotypes when compared to the least affected. The Leaf Water Vegetation Index 1 (LWVI1) highlighted differences between drought-resistant and drought-susceptible genotypes. Drought-resistant genotypes retained 43.36% more water in leaves under well-watered conditions compared to water-limited conditions, while drought-susceptible genotypes retained only 15.69% more. The LWVI1 and LWVI2 indices, aligned with the RWC measurements, revealed a strong inverse correlation in the susceptible genotypes, underscoring their heightened sensitivity to water stress in earlier stages. Several genotypes previously classified based on their drought resistance showed spectral indices deviating from expectations. Results from this research can aid farmers in improving crop yields by informing early management practices. Moreover, this research offers wheat breeders insights into the selection of drought-tolerant genotypes, a requirement that is becoming increasingly important as weather patterns continue to change. © 2023 by the authors.
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectclimate change adaptation in agriculture
dc.subjectdrought-stress detection
dc.subjecthyperspectral imaging
dc.subjectwater-use efficiency
dc.subjectwheat genotypes
dc.titleEarly Detection of Drought Stress in Durum Wheat Using Hyperspectral Imaging and Photosystem Sensing
dc.typeArticle
dc.typetext
dc.contributor.departmentExperiment Station, University of Arizona
dc.identifier.journalRemote Sensing
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal Published Version
dc.source.journaltitleRemote Sensing
refterms.dateFOA2024-03-22T17:33:59Z


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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.