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dc.contributor.authorJohansen, Kasper
dc.contributor.authorMorton, Mitchell J. L.
dc.contributor.authorMalbeteau, Yoann
dc.contributor.authorAragon Solorio, Bruno Jose Luis
dc.contributor.authorAlmashharawi, Samir
dc.contributor.authorZiliani, Matteo G.
dc.contributor.authorAngel, Yoseline
dc.contributor.authorFiene, Gabriele
dc.contributor.authorNegrão, Sónia
dc.contributor.authorMousa, Magdi A. A.
dc.contributor.authorTester, Mark A.
dc.contributor.authorMcCabe, Matthew
dc.date.accessioned2019-04-02T07:11:35Z
dc.date.available2019-04-02T07:11:35Z
dc.date.issued2019-03-29
dc.identifier.citationJohansen K, Morton MJL, Malbeteau YM, Aragon B, Al-Mashharawi SK, et al. (2019) Unmanned Aerial Vehicle-Based Phenotyping Using Morphometric and Spectral Analysis Can Quantify Responses of Wild Tomato Plants to Salinity Stress. Frontiers in Plant Science 10. Available: http://dx.doi.org/10.3389/fpls.2019.00370.
dc.identifier.issn1664-462X
dc.identifier.doi10.3389/fpls.2019.00370
dc.identifier.urihttp://hdl.handle.net/10754/631795
dc.description.abstractWith salt stress presenting a major threat to global food production, attention has turned to the identification and breeding of crop cultivars with improved salt tolerance. For instance, some accessions of wild species with higher salt tolerance than commercial varieties are being investigated for their potential to expand food production into marginal areas or to use brackish waters for irrigation. However, assessment of individual plant responses to salt stress in field trials is time-consuming, limiting, for example, longitudinal assessment of large numbers of plants. Developments in Unmanned Aerial Vehicle (UAV) sensing technologies provide a means for extensive, repeated and consistent phenotyping and have significant advantages over standard approaches. In this study, 199 accessions of the wild tomato species, Solanum pimpinellifolium, were evaluated through a field assessment of 600 control and 600 salt-treated plants. UAV imagery was used to: (1) delineate tomato plants from a time-series of eight RGB and two multi-spectral datasets, using an automated object-based image analysis approach; (2) assess four traits, i.e., plant area, growth rates, condition and Plant Projective Cover (PPC) over the growing season; and (3) use the mapped traits to identify the best-performing accessions in terms of yield and salt tolerance. For the first five campaigns, >99% of all tomato plants were automatically detected. The omission rate increased to 2–5% for the last three campaigns because of the presence of dead and senescent plants. Salt-treated plants exhibited a significantly smaller plant area (average control and salt-treated plant areas of 0.55 and 0.29 m2, respectively), maximum growth rate (daily maximum growth rate of control and salt-treated plant of 0.034 and 0.013 m2, respectively) and PPC (5–16% difference) relative to control plants. Using mapped plant condition, area, growth rate and PPC, we show that it was possible to identify eight out of the top 10 highest yielding accessions and that only five accessions produced high yield under both treatments. Apart from showcasing multi-temporal UAV-based phenotyping capabilities for the assessment of plant performance, this research has implications for agronomic studies of plant salt tolerance and for optimizing agricultural production under saline conditions.
dc.description.sponsorshipFunding: MT and his team were supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. 2302-01-01 for undertaking the plant experiments. MFM and his team were supported by Competitive Research Grant Nos. URF/1/2550-1 and URF/1/3413-01 for undertaking the UAV-based component of this research. Acknowledgments: We would like to thank all the workers at the King Abdulaziz University Agricultural Research Station in Hada Al-Sham for their extensive help with removal of weeds, plant maintenance and harvesting. Khadija Zemmouri and Dinara Utarbayeva prepared plots and undertook sowing of all plants.
dc.publisherFrontiers Media SA
dc.relation.urlhttps://www.frontiersin.org/articles/10.3389/fpls.2019.00370/full
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleUnmanned Aerial Vehicle-Based Phenotyping Using Morphometric and Spectral Analysis Can Quantify Responses of Wild Tomato Plants to Salinity Stress
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentDesert Agriculture Initiative
dc.contributor.departmentEarth System Observation and Modelling
dc.contributor.departmentEnvironmental Science and Engineering
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentPlant Science
dc.contributor.departmentThe Salt Lab
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.identifier.journalFrontiers in Plant Science
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionSchool of Biology and Environmental Science, University College Dublin, Belfield, Ireland
dc.contributor.institutionDepartment of Vegetables, Faculty of Agriculture, Assiut University, Assiut, Egypt
dc.contributor.institutionDepartment of Arid Land Agriculture, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah, Saudi Arabia
kaust.personJohansen, Kasper
kaust.personMorton, Mitchell J. L.
kaust.personMalbeteau, Yoann
kaust.personAragon Solorio, Bruno Jose Luis
kaust.personAlmashharawi, Samir
kaust.personZiliani, Matteo G.
kaust.personAngel Lopez, Yoseline
kaust.personFiene, Gabriele
kaust.personNegrão, Sónia
kaust.personTester, Mark A.
kaust.personMcCabe, Matthew
kaust.grant.number2302-01-01
refterms.dateFOA2019-04-02T08:49:08Z


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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Except where otherwise noted, this item's license is described as This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.