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Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery
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Raza, Shan-e-Ahmed, Smith, Hazel K., Clarkson, Graham J. J., Taylor, Gail, Thompson, Andrew J., Clarkson, John P. and Rajpoot, Nasir M. (Nasir Mahmood) (2014) Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. PLoS One, Volume 9 (Number 6). Article number e97612. doi:10.1371/journal.pone.0097612 ISSN 1932-6203.
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Official URL: http://dx.doi.org/10.1371/journal.pone.0097612
Abstract
Thermal imaging has been used in the past for remote detection of regions of canopy showing symptoms of stress, including water deficit stress. Stress indices derived from thermal images have been used as an indicator of canopy water status, but these depend on the choice of reference surfaces and environmental conditions and can be confounded by variations in complex canopy structure. Therefore, in this work, instead of using stress indices, information from thermal and visible light imagery was combined along with machine learning techniques to identify regions of canopy showing a response to soil water deficit. Thermal and visible light images of a spinach canopy with different levels of soil moisture were captured. Statistical measurements from these images were extracted and used to classify between canopies growing in well-watered soil or under soil moisture deficit using Support Vector Machines (SVM) and Gaussian Processes Classifier (GPC) and a combination of both the classifiers. The classification results show a high correlation with soil moisture. We demonstrate that regions of a spinach crop responding to soil water deficit can be identified by using machine learning techniques with a high accuracy of 97%. This method could, in principle, be applied to any crop at a range of scales.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics S Agriculture > SB Plant culture |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) |
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Library of Congress Subject Headings (LCSH): | Spinach, Soil moisture -- Computer programs, Soil moisture -- Measurement, Infrared imaging | ||||||||
Journal or Publication Title: | PLoS One | ||||||||
Publisher: | Public Library of Science | ||||||||
ISSN: | 1932-6203 | ||||||||
Official Date: | 3 June 2014 | ||||||||
Dates: |
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Volume: | Volume 9 | ||||||||
Number: | Number 6 | ||||||||
Number of Pages: | 10 | ||||||||
Article Number: | Article number e97612 | ||||||||
DOI: | 10.1371/journal.pone.0097612 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 27 December 2015 | ||||||||
Date of first compliant Open Access: | 27 December 2015 | ||||||||
Funder: | Horticultural Development Company, University of Warwick. Department of Computer Science, Vitacress Salads Ltd., Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC) | ||||||||
Embodied As: | 1 |
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