Review of Crop Phenotyping in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms

Takashi Tanaka, Sheng Wang, Johannes Ravn Jørgensen, Marco Gentili, Armelle Zaragüeta Vidal, Anders Krogh Mortensen, Bharat Sharma Acharya, Brittany Deanna Beck, René Gislum

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisReviewForskningpeer review

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Abstract

The phenotyping of field crops quantifies a plant’s structural and physiological characteristics to facilitate crop breeding. High-throughput unmanned aerial vehicle (UAV)-based remote sensing platforms have been extensively researched as replacements for more laborious and time-consuming manual field phenotyping. This review aims to elucidate the advantages and challenges of UAV-based phenotyping techniques. This is a comprehensive overview summarizing the UAV platforms, sensors, and data processing while also introducing recent technological developments. Recently developed software and sensors greatly enhance the accessibility of UAV-based phenotyping, and a summary of recent research (publications 2019–2024) provides implications for future research. Researchers have focused on integrating multiple sensing data or utilizing machine learning algorithms, such as ensemble learning and deep learning, to enhance the prediction accuracies of crop physiological traits. However, this approach will require big data alongside laborious destructive measurements in the fields. Future research directions will involve standardizing the process of merging data from multiple field experiments and data repositories. Previous studies have focused mainly on UAV technology in major crops, but there is a high potential in minor crops or cropping systems for future sustainable crop production. This review can guide new practitioners who aim to implement and utilize UAV-based phenotyping.
OriginalsprogEngelsk
Artikelnummer212
TidsskriftDrones
Vol/bind8
Nummer6
ISSN2504-446X
DOI
StatusUdgivet - maj 2024

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