GiESCO 2019 banner
IVES 9 IVES Conference Series 9 GiESCO 9 Machines and fire: developing a rapid detection system for grapevine smoke contamination using NIR spectroscopy and machine learning modelling

Machines and fire: developing a rapid detection system for grapevine smoke contamination using NIR spectroscopy and machine learning modelling

Abstract

Context and purpose of the study – Bushfires are a common occurrence throughout Australia and their incidence is predicted to both rise and increase in severity due to climate change. Many of these bushfires occur in areas close to wine regions, which receive different levels of exposure to smoke. Wine produced from smoke-affected grapes are characterised by unpalatable smoky aromas such as “burning rubber”, “smoked meats” and “burnt wood”. These smoke tainted wines are unprofitable and result in significant financial losses for winegrowers. This study investigated the use of near-infrared (NIR) spectroscopy and machine learning (ML) modelling for the rapid and non-destructive detection of grapevine smoke exposure by analysing grapevine leaves and/or grape berries.

Materials and methods – The trial was conducted during the 2018/2019 season at the University of Adelaide’s Waite campus in Adelaide, South Australia (34° 58’ S, 138° 38’ E) and involved the application of five different smoke and water misting treatments to Cabernet Sauvignon grapevines at approximately seven days post-veraison. Treatment vines were exposed to straw-based smoke for one hour under experimental conditions described previously by Kennison et al. (2008) and Ristic et al. (2011). Near-infrared (NIR) measurements were then taken from berries and leaves a day after smoking using the microPHAZIR TM RX NIR Analyser (Thermo Fisher Scientific, Waltham, USA) which has a spectral range of 1600-2396 nm. The NIR spectra were then used as inputs to train different ML algorithms, which resulted in two artificial neural networks (ANNs) with the best classification performance for either berry or leaf readings according to the different smoke treatments.

Results – Both ANN models found were able to correctly classify the leaf and berry spectral readings with high accuracy. The leaf model had an overall accuracy of 95.2%, 97.7% accuracy during training with a mean square error (MSE) 0.0082, 90.9% during validation with a MSE of 0.0353 and 88.1% during the testing stage with a MSE of 0.0386, while the berry model had an overall accuracy of 91.7%, 95.2% accuracy during training with a MSE of 0.0173, 86.4% during validation with a MSE of 0.0560 and 80.2% during the testing stage with a MSE of 0.0560. These results showed the potential of developing a rapid, non-destructive, in-field detection system for assessing grapevine smoke contamination following a bushfire using NIR spectroscopy and artificial neural network modelling.

DOI:

Publication date: September 28, 2023

Issue: GiESCO 2019

Type: Poster

Authors

Vasiliki SUMMERSON, Claudia GONZALEZ VIEJO, Damir TORRICO, Sigfredo FUENTES*

The University of Melbourne, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, Parkville 3010, Victoria, Australia

Contact the author

Keywords

bushfires, machine learning, smoke taint, climate change, non-destructive

Tags

GiESCO | GiESCO 2019 | IVES Conference Series

Citation

Related articles…

Survey of pesticide residues in vineyard soils from the Denomination of Origin Ribeiro

Vineyards from mild temperature, high humidity locations receive often treatments with fungicides to prevent damages produced by fungi responsible for mildium, oidium and botrytis infections. In addition, insecticides are also applied to vineyards to fight again pests, which affect directly, or indirectly (as vectors of different diseases), their productivity. A fraction of the above compounds reaches the soil of vineyards, either during application, or when released from the canopy of vines due to rain-wash-off. Thereafter, depending on soil conditions (pH, organic matter) and environmental variables (regimen of rain, slope of vineyards), they might persist in this compartment, be degraded and/or transferred to water masses, modifying the biodiversity of soils and/or affecting the quality of water reservoirs.

High-throughput screening of physical-mechanical berry skin traits facilitates targeted selection of breeding material with resistance to Botrytis bunch rot and grape sunburn

The ongoing climate change implies an increasing mean air temperature, which is signified by weather extremes or sudden changes between drought and local heavy rainfalls. These changing conditions are especially challenging for the established grapevine varieties growing under cool climate conditions due to an increased risk for fungal diseases like downy mildew (DM) and Botrytis bunch rot (BBR) as well as for grape sunburn. To meet that demand, the scope of most grapevine breeding programs is the selection of mildew fungus-resistant and climatic adapted grapevines with balanced, healthy yield and outstanding wine quality.

Green berries on Gewürztraminer (Vitis vinifera L.) in South Tyrol (Italy)

The grape variety Gewürztraminer is known to be affected by two physiological disorders namely berry shrivel and bunch stem necrosis. During the season 2014 we noticed a new symptomatology type of ripening disorder on the variety. The new symptom showed not all berries fallowing the normal maturation stages, but single berries remaining at a soft but green stage till harvest. The broad distribution of these so called “green berries” symptoms in different production sites of our region, caused huge damage due to the difficulty of eliminating single berries per bunch before harvesting. Therefore, the Research Centre Laimburg began to investigate the reasons and origins of this new symptom. This work shows the results of first attempts to find causes for the symptom as well as the resulting approach to mitigate symptoms. Applications of magnesium leaf fertilizer showed first promising results against this putative disorder. To study the causal effect of the green berries 30 symptomatic vineyards in 2014 have been selected for a monitoring during the season 2016. To evaluate the foliar nutrient treatment two vineyards have been selected for application of magnesium sulfate and magnesium chloride. Leaf and berry nutrient analysis, as well as the main quality parameters during ripening have been performed. As soon as “green berries” symptoms appeared, incidence and severity have been evaluated. Most of the symptomatic vineyards of the 2016 monitoring showed light to clear magnesium deficit symptoms on their foliage. Only during the seasons 2020 and 2021 “green berries” symptoms could be found in the leaf fertilizer treatment vineyards. Both seasons showed a significant effect of the magnesium treatments to reduce the incidence and severity of the symptom. It seems that the appearance of the “green berries” symptom on Gewürztraminer is correlated to a disturbed uptake of magnesium of the vines.

Yeast interactions in chardonnay wine fermentation: impact of different yeast species using ultra high resolution mass spectrometry

During alcoholic fermentation, when yeasts grow simultaneously, they often do not coexist passively and in most cases interact with each others

INFLUENCE OF CHITOSAN, ABSCISIC ACID AND BENZOTHIADIAZOLE TREATMENTS ON SAVVATIANO (VITIS VINIFERA L.) WINES VOLATILE COMPOSITION PROFILE

In the last decades the use of bioestimulants in viticulture have been promoted as alternative to conven- tional pesticides. Moreover, as bioestimulants promote the biosynthesis of secondary metabolites in grape berries, several studies had investigated their influence on the accumulation of phenolic com- pounds (Monteiro et al., 2022). However, few studies, so far, are focused on the accumulation of the vo- latile compounds and their impact on the produced wines (Giménez-Bañón et al., 2022; Gomez- Plaza et al., 2012; Ruiz Garcia et al., 2014).
This study was conducted in a single vineyard of white autochthonous grapevine variety Savvatia- no (Vitis vinifera L.) in Muses Valley (Askri, Viotia, Greece). Chitosan (CHT), Abscisic Acid (ABA) and Benzothiadiazole (BTH) were applied.