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Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies

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dc.abstract.enIntroduction: Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking. Materials and methods: This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control. Results: Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls. Conclusion: Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Ekologii i Ochrony Środowiska
dc.contributor.authorChakhvashvili, Erekle
dc.contributor.authorMachwitz, Miriam
dc.contributor.authorAntala, Michal
dc.contributor.authorRozenstein, Offer
dc.contributor.authorPrikaziuk, Egor
dc.contributor.authorSchlerf, Martin
dc.contributor.authorNaethe, Paul
dc.contributor.authorWan, Quanxing
dc.contributor.authorKomárek, Jan
dc.contributor.authorKlouek, Tomáš
dc.contributor.authorWieneke, Sebastian
dc.contributor.authorSiegmann, Bastian
dc.contributor.authorKefauver, Shawn
dc.contributor.authorKycko, Marlena
dc.contributor.authorBalde, Hamadou
dc.contributor.authorPaz, Veronica Sobejano
dc.contributor.authorJimenez-Berni, Jose A.
dc.contributor.authorBuddenbaum, Henning
dc.contributor.authorHänchen, Lorenz
dc.contributor.authorWang, Na
dc.contributor.authorWeinman, Amit
dc.contributor.authorRastogi, Anshu
dc.contributor.authorMalachy, Nitzan
dc.contributor.authorBuchaillot, Maria-Luisa
dc.contributor.authorBendig, Juliane
dc.contributor.authorRascher, Uwe
dc.date.access2024-08-27
dc.date.accessioned2024-08-27T08:37:04Z
dc.date.available2024-08-27T08:37:04Z
dc.date.copyright2024-08-11
dc.date.issued2024
dc.description.abstract<jats:sec> <jats:title>Introduction</jats:title> <jats:p>Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.</jats:p> </jats:sec><jats:sec> <jats:title>Materials and methods</jats:title> <jats:p>This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusion</jats:title> <jats:p>Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.</jats:p> </jats:sec>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financeother
dc.description.financecost20000,00
dc.description.reviewreview
dc.description.versionfinal_published
dc.identifier.doi10.1007/s11119-024-10168-3
dc.identifier.eissn1573-1618
dc.identifier.issn1385-2256
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/1690
dc.identifier.weblinkhttps://link.springer.com/article/10.1007/s11119-024-10168-3
dc.languageen
dc.pbn.affiliationenvironmental engineering, mining and energy
dc.relation.ispartofPrecision Agriculture
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOTHER
dc.subject.enUAVs
dc.subject.enOptical sensors
dc.subject.enSensor synergy
dc.subject.enCrop stress
dc.subtypeArticleEarlyAccess
dc.titleCrop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies
dc.typeJournalArticle
dspace.entity.typePublication