A review on plant high-throughput phenotyping traits using UAV-based sensors

Chuanqi Xie, Ce Yang

Research output: Contribution to journalReview articlepeer-review

165 Scopus citations


The current methods of phenotyping for breeding lines require a lot of time, labor and cost. In recent years, unmanned aerial system (UAS) has paved the way for the development of field high-throughput phenotyping for crops rapidly. Different sensors such as regular RGB camera (Red, Green and Blue), multispectral imaging camera (several wavebands), hyperspectral imaging camera (hundreds and even thousands of wavebands), thermal imaging sensor and light detection and ranging (LiDAR) sensor can be placed on unmanned aerial vehicle (UAV) to collect remote sensing data in field-scale trials. Based on this technique, the plant traits (e.g., yield, biomass, height, and leaf area index) can be estimated non-destructively, which is critical for high-throughput phenotyping in agriculture. Compared with ground vehicle-based sensors, UAS can increase throughput and frequency for phenotyping. It is low-cost and could provide high-resolution images compared with satellite-based technique. Based upon the phenotypic traits, those crops with high yield and strong stress resistance (e.g., disease resistance and salt resistance) can be selected, which could finally improve the production. This paper talked about the plant high-throughput phenotyping traits based on the sensors on the UAV. Also, the challenges and obstacles of UAV (e.g., flight safety, flight altitude, flight time, and sensor accuracy) were analyzed. In order to provide the updated information of the relationships between remote sensing information taken from UAV and plant phenotyping traits, we summarized the sensors, plants and traits reported in previous research articles. As a result, the review can be very useful for researchers to use appropriate UAV-based sensors to carry out plant phenotyping experiments, and for farmers to use this advanced technology in managing agricultural production.

Original languageEnglish (US)
Article number105731
JournalComputers and Electronics in Agriculture
StatePublished - Nov 2020

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.


  • Phenotyping
  • Plant
  • Sensors
  • Traits
  • Unmanned aerial system (UAS)


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