Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120943
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Type: Journal article
Title: Non-invasive tools to detect smoke contamination in grapevine canopies, berries and wine: a remote sensing and machine learning modeling approach
Author: Fuentes, S.
Tongson, E.
De Bei, R.
Viejo, C.
Ristic, R.
Tyerman, S.
Wilkinson, K.
Citation: Sensors, 2019; 19(15):3335-1-3335-16
Publisher: MDPI AG
Issue Date: 2019
ISSN: 1424-2818
1424-8220
Statement of
Responsibility: 
Sigfredo Fuentes, Eden Jane Tongson, Roberta De Bei, Claudia Gonzalez Viejo, Renata Ristic, Stephen Tyerman, and Kerry Wilkinson
Abstract: Bushfires are becoming more frequent and intensive due to changing climate. Those that occur close to vineyards can cause smoke contamination of grapevines and grapes, which can affect wines, producing smoke-taint. At present, there are no available practical in-field tools available for detection of smoke contamination or taint in berries. This research proposes a non-invasive/in-field detection system for smoke contamination in grapevine canopies based on predictable changes in stomatal conductance patterns based on infrared thermal image analysis and machine learning modeling based on pattern recognition. A second model was also proposed to quantify levels of smoke-taint related compounds as targets in berries and wines using near-infrared spectroscopy (NIR) as inputs for machine learning fitting modeling. Results showed that the pattern recognition model to detect smoke contamination from canopies had 96% accuracy. The second model to predict smoke taint compounds in berries and wine fit the NIR data with a correlation coefficient (R) of 0.97 and with no indication of overfitting. These methods can offer grape growers quick, affordable, accurate, non-destructive in-field screening tools to assist in vineyard management practices to minimize smoke taint in wines with in-field applications using smartphones and unmanned aerial systems (UAS).
Keywords: Bushfires; infrared thermography; near-infrared spectroscopy; smoke taint; artificial intelligence
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
RMID: 0030131834
DOI: 10.3390/s19153335
Grant ID: http://purl.org/au-research/grants/arc/LP0989138
Appears in Collections:Agriculture, Food and Wine publications

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