GiESCO 2019 banner
IVES 9 IVES Conference Series 9 GiESCO 9 Assessing bunch architecture for grapevine yield forecasting by image analysis

Assessing bunch architecture for grapevine yield forecasting by image analysis

Abstract

Context and purpose of the study – It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages. The accuracy of the method for predicting bunch weight at different phenological stages was assessed for seven different varieties.

Material and methods – During the 2017-18 growing season in the Coombe Vineyard at the Waite Campus of the University of Adelaide seven different varieties were chosen for this study: Semillon, Grenache, Shiraz, Merlot, Sauvignon Blanc, Tempranillo and Cabernet Franc. After fruitset, 30 vines per variety were selected and two shoots were flagged on each vine. Images of bunches were taken five times from EL stage 30-31 to EL stage 37-38 using a smartphone. Bunch volumes were estimated from images. At harvest bunches were collected, weighed and imaged in the laboratory to compare with field images.

Results – This new approach using a smartphone to forecast the yield showed promising results. Accurate weight forecast models could be obtained by taking bunch images at veraison (R2 ranging from 0.71 to 0.84). As the bunch architecture of different varieties can vary further studies are required to improve the accuracy of this method. The tools used for this study are inexpensive, in common use, and do not need a high level of expertise to use them, furthermore, the labour required to obtain data, is not time-consuming.

DOI:

Publication date: September 26, 2023

Issue: GiESCO 2019

Type: Poster

Authors

Marco ZITO*1, Massimiliano COCCO2, Roberta DE BEI3, Cassandra COLLINS3

1 Istituto di Scienze della Vita, Scuola Superiore Sant’Anna, Pisa, Italy 56127
2 Dipartimento di Agraria, Università degli Studi di Sassari, Sassari, Italy 07100
3 School of Agriculture, Food and Wine, Waite Research Precinct, The University of Adelaide, PMB I, Glen Osmond, SA 5064, Australia

Contact the author

Keywords

bunch architecture, yield prediction, image analysis, non-destructive method

Tags

GiESCO | GiESCO 2019 | IVES Conference Series

Citation

Related articles…

Evolution of flavonols during Merlot winemaking processes

The phenomenon of quercetin precipitation in wine (flanovol haze), has been manifested for many years in several wine-producing regions

Development of a strategy for measuring fruity aroma potential in red wine

Levels of esters derived from substituted acids increase during the first years of aging and some of them are strongly involved in red wine fruity aromatic expression.

Unravelling Saccharomyces cerevisiae biosynthethic pathways of melatonin, serotonin and hydroxytyrosol  by UPLC-HRMS Isotopic labelling analysis

The main objective is to unravel the yeast biosynthetic pathways for MEL, SER and HT by using the respective labelled amino acids precursors: 15N2-L tryptophan and 13C-tyrosine.
The alcoholic fermentation experiments are performed with two different commercial
S cereviseae yeasts using synthetic must with the addition of the labelled compounds and the bioactive compounds were followed during the fermentation process. Six biological replicates of the fermentations were considered. MEL, SER and HT were analysed by UHPLC coupled to High Resolution Mass Spectrometry (HRMS). Accurate mass determination allowed to unequivocally distinguishing labelled and unlabelled compounds.

Evolution of cabernet sauvignon wines macerated with their own toasted vine-shoots

Toasted pruning vine-shoots represent a promising new enological tool for developing wines with chemical and organoleptic high quality, allowing that the resources of the vineyard to be returned to the wine through a “circular process”.

Exploring resilience and competitiveness of wine estates in Languedoc-Roussillon in the recent past: a multi-level perspective

The Languedoc-Roussillon wineries are facing a decline in wine yields particularly PGI yields due to many factors. Climate change is just ones, but is expected to increase in the future. There is also structurally a large heterogeneity of yield profiles among terroirs, varieties and strategies. This work investigates the link between yield, competitiveness and resilience to explore how resilient winegrowers have been in the recent past. To this end two approaches have been combined; (i) an accountancy database analysis at estate scale and (ii) municipality level competitiveness analysis. A new resilience indicator that characterizes the capacity of an estate to absorb yield variation is also defined. The FADN database between 2000 and 2018 of ex-Languedoc-Roussillon (France) and other data are used to analyse the current situation and the past evolution of competitiveness and resilience by type of estate (type of farm: PGI and/or PDO & type of commercialization: bulk and/or bottles). The net margin, which defines competitiveness, is not correlated to yield for all types but depends on the type of commercialization and the level of specialisation. The resilience indicator shows that the net margin of estates specialized in PGI is particularly sensitive to yield declines. We also show that price evolutions seem to compensate the effect of yield losses for the majority of types. Municipality scale analysis shows the links between local pedoclimate, yield, commercialization strategies and price. Overlapping a PDO with a PGI does not always increase a municipality’s PGI competitiveness. It is difficult to make links between causes and effects due to the complexity of the wine production system. Production diversification may be a solution. Resorting to the two level of analysis helps resolving the data gap that is necessary to explore the links between yield and economic performance of the wine estates in the long term.