Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/27235
Title: A Non-Reference Temperature Histogram Method for Determining Tc from Ground-Based Thermal Imagery of Orchard Tree Canopies
Contributor(s): Salgadoe, Arachchige Surantha Ashan  (author); Robson, Andrew James  (author)orcid ; Lamb, David William  (author); Schneider, Derek  (author)orcid 
Publication Date: 2019-03-25
Open Access: Yes
DOI: 10.3390/rs11060714
Handle Link: https://hdl.handle.net/1959.11/27235
Abstract: Obtaining average canopy temperature (Tc) by thresholding canopy pixels from on-ground thermal imagery has historically been undertaken using ‘wet’ and ‘dry’ reference surfaces in the field (reference temperature thresholding). However, this method is extremely time inefficient and can suffer inaccuracies if the surfaces are non-standardised or unable to stabilise with the environment. The research presented in this paper evaluates non-reference techniques to obtain average canopy temperature (Tc) from thermal imagery of avocado trees, both for the shaded side and sunlit side, without the need of reference temperature values. A sample of 510 thermal images (from 130 avocado trees) were acquired with a FLIR B250 handheld thermal imaging camera. Two methods based on temperature histograms were evaluated for removing non-canopy-related pixel information from the analysis, enabling Tc to be determined. These approaches included: 1) Histogram gradient thresholding based on temperature intensity changes (HG); and 2) histogram thresholding at one or more standard deviation (SD) above and below the mean. The HG method was found to be more accurate (R2 > 0.95) than the SD method in defining canopy pixels and calculating Tc from each thermal image (shaded and sunlit) when compared to the standard reference temperature thresholding method. The results from this study present an alternative non-reference method for determining Tc from ground-based thermal imagery without the need of calibration surfaces. As such, it offers a more efficient and computationally autonomous method that will ultimately support the greater adoption of non-invasive thermal technologies within a precision agricultural system.
Publication Type: Journal Article
Source of Publication: Remote Sensing, 11(6), p. 1-15
Publisher: MDPI AG
Place of Publication: Switzerland
ISSN: 2072-4292
Fields of Research (FoR) 2008: 070603 Horticultural Crop Protection (Pests, Diseases and Weeds)
080106 Image Processing
070104 Agricultural Spatial Analysis and Modelling
Fields of Research (FoR) 2020: 460306 Image processing
300804 Horticultural crop protection (incl. pests, diseases and weeds)
300206 Agricultural spatial analysis and modelling
Socio-Economic Objective (SEO) 2008: 820299 Horticultural Crops not elsewhere classified
Socio-Economic Objective (SEO) 2020: 260599 Horticultural crops not elsewhere classified
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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