File(s) under embargo

Reason: Publisher requirement

6

days

24

hours

until file(s) become available

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

journal contribution
posted on 03.10.2019 by Jinya Su, Cunjia Liu, Xiaoping Hu, Xiangming Xu, Lei Guo, Wen-Hua Chen
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.

Funding

Science and Technology Facilities Council (STFC) under Newton fund with Grant No. ST/N006852/1

National Natural Science Foundation of China with Grant No. 31772102

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Computers and Electronics in Agriculture

Volume

167

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier B.V.

Publisher statement

This paper was accepted for publication in the journal Computers and Electronics in Agriculture and the definitive published version is available at https://doi.org/10.1016/j.compag.2019.105035.

Acceptance date

29/09/2019

Publication date

2019-10-05

Copyright date

2019

ISSN

0168-1699

Language

en

Depositor

Dr Cunjia Liu

Article number

105035

Exports

Logo branding

Categories

Exports