The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne
platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. The research is mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. An experiment was carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where aerial multispectral images under different developmental stages of yellow rust were captured by an Unmanned Aerial Vehicle
at an altitude of 16–24m with a ground resolution of 1–1.5cm/pixel. An automated yellow rust detection system is
developed by learning (via random forest classifier) from labelled UAV aerial multispectral imagery. Experimental
results indicate that: (i) good classification performance (with an average Precision, Recall and Accuracy of 89.2%, 89.4% and 89.3%) was achieved by the developed yellow rust monitoring at a diseased stage (45 days after inoculation); (ii) the top three SVIs for separating healthy and yellow rust infected wheat plants are RVI, NDVI and OSAVI; while the top two spectral bands are NIR and Red. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at
farmland scales.
Funding
This work was supported by Science and Technology Facilities Council (STFC) under Newton fund with grant number ST/N006852/1.
History
School
Aeronautical, Automotive, Chemical and Materials Engineering
Department
Aeronautical and Automotive Engineering
Published in
Computers and Electronics in Agriculture
Volume
155
Pages
157 - 166
Citation
SU, J. ... et al, 2018. Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. Computers and Electronics in Agriculture, 155, pp.157-166.
This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/
Acceptance date
2018-10-10
Publication date
2018-10-17
Notes
This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/