Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery

Jinya Su, Cunjia Liu, Xiaoping Hu, Xiangming Xu, Lei Guo, Wen-Hua Chen

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)


This work is focused on the spatio-temporal monitoring of winter wheat inoculated with various levels of yellow rust inoculum during the entire growth season. A dedicated workflow is devised to obtain time-series five-bands (visible-infrared) aerial imageries with a multispectral camera and an Unmanned Aerial Vehicle. A number of spectral indices are drawn so that the sensitive ones can be identified by statistical dependency analysis; particularly, their discriminating capabilities are evaluated at different stages for both wheat pixel segmentation and yellow rust severity. Then the spatial-temporal changes of sensitive bands/indices are evaluated and analysed quantitatively. A validation field experiment was designed in 2017–2018 by inoculating wheat with one of the six levels of yellow rust inoculum. Five-bands RedEdge camera on-board DJI S1000 was used to capture aerial images at eight time points covering the entire growth season at an altitude of about 20 meters with a ground resolution of 1–1.5 cm/pixel. Experimental results via spatio-temporal analysis show that: (1) various bands/indices should be used for wheat segmentation at different stages; (2) no bands/indices differences are observed for yellow rust inoculated wheat plots in both incubation stage (9 days after inoculation) and early onset stage (25 days after inoculation); (3) NIR and Red are the sensitive bands for wheat yellow rust in disease stages (45 days after inoculation); and their normalized difference NDVI index provides an even higher statistical dependency; (4) bands/indices’ sensitivity to yellow rust changes over time and decreases in later Heading stage until being very low in Ripening stage (61 days after inoculation). This experimental study provides a crucial guidance for future early spatio-temporal yellow rust monitoring at farmland scales.
Original languageEnglish
Article number105035
JournalComputers and Electronics in Agriculture
Early online date5 Oct 2019
Publication statusPublished - Dec 2019

Bibliographical note

This work was supported by Science and Technology Facilities Council (STFC) under Newton fund with Grant No. ST/N006852/1. Prof. Xiaoping Hu was supported by National Natural Science Foundation of China with Grant No. 31772102. Dr. Matthew Coombes was acknowledged for developing the UAV-Camera system; Xi’an Tongfei Aviation Technology Co., Ltd was acknowledged for their professional support in flying UAV for data collection; team members (e.g. Mr. Conghao Wang) of Prof. Xiaoping Hu were acknowledged for ground yellow rust data collection.


  • Multispectral image
  • Spatio-temporal analysis
  • Statistical dependency
  • UAV remote sensing
  • Yellow rust


Dive into the research topics of 'Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery'. Together they form a unique fingerprint.

Cite this