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Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery

Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery
Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery
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.
1932-6203
1-10
Raza, Shan-e-Ahmed
3f992409-f251-41c4-a121-04a7380cd1e5
Smith, Hazel K.
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Clarkson, Graham J.J.
ef06b7b4-509b-4ebe-9327-c8950c4f44b3
Taylor, Gail
f3851db9-d37c-4c36-8663-e5c2cb03e171
Thompson, Andrew J.
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Clarkson, John
ba6de915-5997-45c6-9afc-f482ad8b8a38
Rajpoot, Nasir M.
11ba261f-3a08-4e8f-ad6b-abd48d621910
Raza, Shan-e-Ahmed
3f992409-f251-41c4-a121-04a7380cd1e5
Smith, Hazel K.
9e5d9adc-6c4a-41a7-9418-e553275cd973
Clarkson, Graham J.J.
ef06b7b4-509b-4ebe-9327-c8950c4f44b3
Taylor, Gail
f3851db9-d37c-4c36-8663-e5c2cb03e171
Thompson, Andrew J.
86b8f744-39f4-4f95-8c96-6bc78c005fe3
Clarkson, John
ba6de915-5997-45c6-9afc-f482ad8b8a38
Rajpoot, Nasir M.
11ba261f-3a08-4e8f-ad6b-abd48d621910

Raza, Shan-e-Ahmed, Smith, Hazel K., Clarkson, Graham J.J., Taylor, Gail, Thompson, Andrew J., Clarkson, John and Rajpoot, Nasir M. (2014) Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. PLoS ONE, 9 (6), 1-10. (doi:10.1371/journal.pone.0097612).

Record type: Article

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.

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Automatic Detection of Regions in Spinach Canopies Responding to Soil Moisture Deficit Using Combined Visible and Thermal Imagery.pdf - Other
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More information

Accepted/In Press date: 23 April 2014
Published date: 3 June 2014
Organisations: Centre for Biological Sciences

Identifiers

Local EPrints ID: 367061
URI: http://eprints.soton.ac.uk/id/eprint/367061
ISSN: 1932-6203
PURE UUID: 17e51081-b2e8-4d1b-8023-7419f3af234f
ORCID for Gail Taylor: ORCID iD orcid.org/0000-0001-8470-6390

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Date deposited: 22 Jul 2014 09:54
Last modified: 10 Dec 2019 01:51

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