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…

Effects of graft quality on growth and grapevine-water relations

Climate change is challenging viticulture worldwide compromising its sustainability due to warmer temperatures and the increased frequency of extreme events. Grafting Vitis vinifera L.

Drought effect on aromatic and phenolic potential of seven recovered grapevine varieties in Castilla-La Mancha region (Spain)

The effects of climate change are seriously affecting the quality of wine grapes. High temperatures and drought cause imbalances in the chemical composition of grapes. The result is overripe grapes with low acidity and high sugar content, which produce wines with excessive alcohol content, lacking in freshness and not very aromatic. As a consequence, the search of varieties with capacity of produce quality grapes in adverse climate conditions is a good alternative to preserve the sustainability of vineyards. In this work, quality parameters of seven Vitis vinifera L. cultivars (five whites and two reds) recently recovered from extinction and grown under two different hydric regimes (rainfed and irrigated) were analyzed during the 2020 vintage. At harvest time, weight of 100 berries, must physicochemical parameters (brix degree, total acidity, malic acid, pH), and carbon and oxygen isotope ratios (δ13C, δ18O) were determined. Subsequently, varietal aroma potential index (IPAv) and total polyphenol index (TPI) were analyzed. Quality parameters, IPAv and TPI, showed significant differences between varieties and water regimes. Both red varieties, Moribel and Tinto Fragoso, stood out for their high aromatic and phenolic potential, which was higher under rainfed regime. Regarding to white varieties, Montonera del Casar and Jarrosuelto stood out in terms of varietal aroma potential. Montonera del Casar high acidity in its musts and Jarrosuelto showed the highest berry weights.

Climate change impacts on Douro Region viticulture and adaptation measures

Climate has a significant impact in the success of any agricultural system, with a direct influence on the crops suitability to a given region, interfering on yield and quality and also with the economic sustainability of the productive activity. In the Douro Demarcated Region (RDD), as in most regions of the Mediterranean climate, the scarce precipitation (33% has less than 600 mm per year), and your high variability, associated with high rates of evapotranspiration during the summer, is usually one of the fundamental factors that limit the grapevine development, as well as the production and quality of the harvest. Thus, facing the scenario in temperature changes for the next decades (1.5-2.5°C) and confirming the predictions of precipitation decreases and/or great variability in the occurrence of heat waves and intense rainfall, the consequences for slope stability in mountain viticulture and sustainability of all operations involved, are risks to be taken into account. In this way, a deepest and sustained knowledge regarding the adaptation measures to adverse environmental conditions is of a crucial importance, enabling a more efficient adaptation of plant growth conditions and the optimization of production and quality of the grapevines. The development of this work, carried out in two commercial vineyards, one located in Soutelo do Douro, São João da Pesqueira, Cima Corgo sub-region, and another located in Numão, Vila Nova de Foz Côa, Douro Superior sub-region, it seeks to establish a relationship between climatic elements and physiological, productive and qualitative parameters, as well as to evaluate the effectiveness of adaptation measures, including different types of deficit irrigation (2002-2019) and the application of shading nets (2019-2020) in the physiological, viticultural and oenological behavior in the Touriga Nacional and Moscatel Galego Branco varieties, respectively. The results showed that the application of deficit irrigation allowed to significantly reduce the impact of the adverse weather conditions at key moments in the development of the grapevine, particularly in the period immediately before veráison and maturation, reducing the negative effects on the physiological processes and productivity, without compromise the must quality parameters. On the other hand, the application of shading nets significantly reduced de leaves temperature, allowing to increase the water potential, stomatal conductance and photosynthetic rate of grapes, which was reflected in the yield increase in the 2nd year of the study. For the maturation indicators, higher levels of total acidity, malic acid and assimilable nitrogen were obtained. The last measure presents a huge potential, being essential to carry out more years of trials to obtain stronger conclusions in terms of production parameters, but also in characteristics as important as the grape ripening components and the organoleptic characteristics of wines.

Legacy of land-cover changes on soil erosion and microbiology in Burgundian vineyards

Soils in vineyards are recognized as complex agrosystems whose characteristics reflect complex interactions between natural factors (lithology, climate, slope, biodiversity) and human activities. To date, most of the unknown lies in an incomplete understanding of soil ecosystems, and specifically in the microbial biodiversity even though soil microbiota is involved in many key functions, such as nutrient cycling and carbon sequestration. Soil biological properties are indicative of soil quality. Therefore, understanding how soil communities are related to soil ecosystem functioning is becoming an essential issue for soil strategy conservation. Here, we propose to assess the importance of land-cover history on the present-day microbiological and physico-chemical properties. The studied area was selected in the Burgundian vineyards (Pernand-Vergelesses, Burgundy, France) where land occupation has been reconstructed over the last 40 years. Soil samples were collected in five areas reflecting various land cover history (forest, vineyards, shifting from forest to vineyards). For each area, physico-chemical parameters (pH, C, N, P, grain size) were measured and DNA was extracted to characterize the abundance and diversity of microbial communities. The obtained results show significant differences in the five areas suggesting that present-day microbial molecular biomass and bacterial taxonomic is partly inherited from past land occupation. Over longer period of time, such study of land-uses legacies may help to better assess ecosystem recovery and the impact of management practices for a better soil quality and vineyards sustainability.

Impact of climate change on the viticultural climate of the Protected Designation of Origin “Jumilla” (SE Spain)

Protected Designation of Origin “Jumilla” (PDO Jumilla) is located in the Spanish provinces of Albacete and Murcia, in the South-eastern part of the Iberian Peninsula, where most of the models predict a severe impact of climate change in next decades. PDO Jumilla covers an area of 247,054 hectares, of which more than 22,000 hectares