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      Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach

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          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).

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          Most cited references23

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          Opportunities and limitations for image-based remote sensing in precision crop management

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            Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine.

            This paper reviews and discusses strategies for the use of thermal imaging for studies of stomatal conductance in the field and compares techniques for image collection and analysis. Measurements were taken under a range of environmental conditions and on sunlit and shaded canopies to illustrate the variability of temperatures and derived stress indices. A simple procedure is presented for correcting for calibration drift within the images from the low-cost thermal imager used (SnapShot 225, Infrared Solutions, Inc.). The use of wet and dry reference surfaces as thresholds to eliminate the inclusion of non-leaf material in the analysis of canopy temperature is discussed. An index that is proportional to stomatal conductance was compared with stomatal measurements with a porometer. The advantages and disadvantages of a possible new approach to the use of thermal imagery for the detection of stomatal closure in grapevine canopies, based on an analysis of the temperature of shaded leaves, rather than sunlit leaves, are discussed. Evidence is presented that the temperature of reference surfaces exposed within the canopy can be affected by the canopy water status.
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              Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                30 July 2019
                August 2019
                : 19
                : 15
                : 3335
                Affiliations
                [1 ]School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
                [2 ]School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia
                Author notes
                [* ]Correspondence: sfuentes@ 123456unimelb.edu.au ; Tel.: +61-3-9035-9670
                Author information
                https://orcid.org/0000-0002-0377-5085
                https://orcid.org/0000-0001-9207-9307
                https://orcid.org/0000-0002-6056-9325
                https://orcid.org/0000-0001-6724-9837
                Article
                sensors-19-03335
                10.3390/s19153335
                6696063
                31366016
                73af1ed0-2346-4508-8ea3-75b372b1400e
                © 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/).

                History
                : 16 July 2019
                : 28 July 2019
                Categories
                Article

                Biomedical engineering
                bushfires,infrared thermography,near-infrared spectroscopy,smoke taint,artificial intelligence

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