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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ID: 86684
2019
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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).
Reference Key
fuentes2019noninvasivesensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fuentes, Sigfredo;Tongson, Eden Jane;De Bei, Roberta;Gonzalez Viejo, Claudia;Ristic, Renata;Tyerman, Stephen;Wilkinson, Kerry;
Journal sensors
Year 2019
DOI
E3335
URL
Keywords
artificial intelligence infrared thermography near-infrared spectroscopy bushfires smoke taint

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