Journal article

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

Sigfredo Fuentes, Eden Jane Tongson, Roberta De Bei, Claudia Gonzalez Viejo, Renata Ristic, Stephen Tyerman, Kerry Wilkinson

Sensors | MDPI | Published : 2019

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..

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University of Melbourne Researchers

Grants

Awarded by Australian Research Council


Funding Acknowledgements

This research was supported under the Australian Research Council's Linkage Projects funding scheme (LP0989138); the financial contributions of industry partners are also gratefully acknowledged. The machine learning modeling research was supported by the Digital Viticulture program funded by the University of Melbourne's Networked Society Institute, Australia.