In order to avoid ambiguity, we choose to call these systems as UAS, with the definition used by the United States Federal Aviation Administration (FAA): “an unmanned aircraft (an aircraft that is operated without the possibility of direct human intervention from within or on the aircraft) and associated elements (including communication links and the components that control the unmanned aircraft) that are required for the pilot in command to operate safely and efficiently in the national airspace system.” (Public Law 112-95, Section 331 (8-9) United States). There are, however, a diversity of names and nomenclature for these devices depending on country of use, with drones, unmanned air vehicle (UAV), and unmanned aircraft system (UAS) being commonly used terms. Most air vehicles used for plant phenotyping are based on the concept of a remotely piloted aircraft system (RPAS) as defined by the International Civil Aviation Organization (ICAO). This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network.
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