Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/35364

Title: Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery
Authors: Su, Jinya
Liu, Cunjia
Coombes, Matthew
Hu, Xiaoping
Wang, Conghao
Xu, Xiangming
Li, Qingdong
Guo, Lei
Chen, Wen-Hua
Keywords: Wheat yellow rust
Multispectral image
Spectral vegetation index (SVI)
Unmanned Aerial Vehicle (UAV)
Random forest
Issue Date: 2018
Publisher: Elsevier © The Authors
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.
Abstract: 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.
Description: 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/
Sponsor: This work was supported by Science and Technology Facilities Council (STFC) under Newton fund with grant number ST/N006852/1.
Version: Published
DOI: 10.1016/j.compag.2018.10.017
URI: https://dspace.lboro.ac.uk/2134/35364
Publisher Link: https://doi.org/10.1016/j.compag.2018.10.017
ISSN: 0168-1699
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

Files associated with this item:

File Description SizeFormat
1-s2.0-S0168169918312584-main.pdfPublished version2.24 MBAdobe PDFView/Open

 

SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.