School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia.
School of Agriculture, Food and Wine, The University of Adelaide, PMB 1, Glen Osmond, SA 5064, Australia.
Sensors (Basel). 2019 Jul 30;19(15):3335. doi: 10.3390/s19153335.
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).
由于气候变化，丛林大火变得越来越频繁和剧烈。那些发生在葡萄园附近的大火会导致烟雾污染葡萄藤和葡萄，从而影响葡萄酒，产生烟熏味。目前，还没有可用的实用现场工具来检测浆果中的烟雾污染或污染。本研究提出了一种基于红外热图像分析和基于模式识别的机器学习建模的预测气孔导度模式变化的非侵入式/现场检测系统，用于检测葡萄树冠中的烟雾污染。还提出了第二个模型，使用近红外光谱 (NIR) 作为机器学习拟合模型的输入，以量化浆果和葡萄酒中与烟雾相关的化合物的水平作为目标。结果表明，用于检测树冠烟雾污染的模式识别模型的准确率为 96%。用于预测浆果和葡萄酒中烟雾污染化合物的第二个模型拟合 NIR 数据的相关系数 (R) 为 0.97，并且没有过度拟合的迹象。这些方法可以为葡萄种植者提供快速、经济、准确、无损的现场筛选工具，以协助葡萄园管理实践，最大限度地减少使用智能手机和无人机系统 (UAS) 进行现场应用时葡萄酒中的烟熏味。
