Morphological image analysis for estimating grape bunch weight under different irrigation regimes in Cabernet-Sauvignon

Morphological image analysis has emerged as a powerful tool for assessing physical bunch characteristics in viticulture, particularly for estimating grape bunch weight, a key factor affecting vineyard yield and wine quality. Traditional manual sampling methods are labour-intensive, destructive, and...

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Main Authors: Carlos Poblete-Echeverria, Anke Berry, Talitha Venter, Sergio Velez, Maria Ignacia González Pavez, Rubén Iñiguez
Format: Article
Language:English
Published: International Viticulture and Enology Society 2025-06-01
Series:OENO One
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Online Access:https://oeno-one.eu/article/view/9309
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author Carlos Poblete-Echeverria
Anke Berry
Talitha Venter
Sergio Velez
Maria Ignacia González Pavez
Rubén Iñiguez
author_facet Carlos Poblete-Echeverria
Anke Berry
Talitha Venter
Sergio Velez
Maria Ignacia González Pavez
Rubén Iñiguez
author_sort Carlos Poblete-Echeverria
collection DOAJ
description Morphological image analysis has emerged as a powerful tool for assessing physical bunch characteristics in viticulture, particularly for estimating grape bunch weight, a key factor affecting vineyard yield and wine quality. Traditional manual sampling methods are labour-intensive, destructive, and prone to significant errors due to vineyard variability and environmental stresses such as water deficit. To address these challenges, this study investigates the potential of two-dimensional (2D) image analysis for non-destructive grape bunch weight estimation across varying levels of water stress. Images of 359 bunches from Cabernet-Sauvignon vines grown under different irrigation regimes, were analysed to extract 13 morphological features. A stepwise multiple regression model was developed to predict bunch weight based on key image-derived features, demonstrating strong explanatory power (adjusted R2 of the prediction = 0.824). The results indicate that features such as area, perimeter, and circularity are strong predictors of bunch weight. While the model demonstrated high accuracy overall, some deviations were observed in large weight categories indicating opportunities for further refinement. These findings demonstrate that image-based phenotyping can reliably estimate bunch weight across a range of water availability scenarios, supporting more precise and efficient vineyard management practices. Future research should focus on enhancing model robustness by integrating additional morphological descriptors and evaluating broader cultivar variability under field conditions.
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issn 2494-1271
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publishDate 2025-06-01
publisher International Viticulture and Enology Society
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spelling doaj-art-b706fd31c72c49dfa281da1361fd11db2025-08-20T02:09:24ZengInternational Viticulture and Enology SocietyOENO One2494-12712025-06-0159210.20870/oeno-one.2025.59.2.9309Morphological image analysis for estimating grape bunch weight under different irrigation regimes in Cabernet-SauvignonCarlos Poblete-Echeverria0https://orcid.org/0000-0001-8025-5879Anke Berry1Talitha Venter2Sergio Velez3Maria Ignacia González Pavez4Rubén Iñiguez5South African Grape and Wine Research Institute (SAGWRI), Stellenbosch University, Private Bag X1, Matieland 7602, South AfricaSouth African Grape and Wine Research Institute (SAGWRI), Stellenbosch University, Private Bag X1, Matieland 7602, South AfricaSouth African Grape and Wine Research Institute (SAGWRI), Stellenbosch University, Private Bag X1, Matieland 7602, South AfricaJRU Drone Technology, Department of Architectural Constructions and I.C.T., University of Burgos, Burgos, 09001, SpainSouth African Grape and Wine Research Institute (SAGWRI), Stellenbosch University, Private Bag X1, Matieland 7602, South Africa/Research and Extension Center for Irrigation and Agroclimatology (CITRA), Faculty of Agricultural Sciences, Universidad de Talca, Campus Talca, ChileInstitute of Grapevine and Wine Sciences (University of La Rioja, Consejo Superior de Investigaciones Científicas, Gobierno de La Rioja), 26007 Logroño, Spain/Televitis Research Group, University of La Rioja, 26006 Logroño, Spain Morphological image analysis has emerged as a powerful tool for assessing physical bunch characteristics in viticulture, particularly for estimating grape bunch weight, a key factor affecting vineyard yield and wine quality. Traditional manual sampling methods are labour-intensive, destructive, and prone to significant errors due to vineyard variability and environmental stresses such as water deficit. To address these challenges, this study investigates the potential of two-dimensional (2D) image analysis for non-destructive grape bunch weight estimation across varying levels of water stress. Images of 359 bunches from Cabernet-Sauvignon vines grown under different irrigation regimes, were analysed to extract 13 morphological features. A stepwise multiple regression model was developed to predict bunch weight based on key image-derived features, demonstrating strong explanatory power (adjusted R2 of the prediction = 0.824). The results indicate that features such as area, perimeter, and circularity are strong predictors of bunch weight. While the model demonstrated high accuracy overall, some deviations were observed in large weight categories indicating opportunities for further refinement. These findings demonstrate that image-based phenotyping can reliably estimate bunch weight across a range of water availability scenarios, supporting more precise and efficient vineyard management practices. Future research should focus on enhancing model robustness by integrating additional morphological descriptors and evaluating broader cultivar variability under field conditions. https://oeno-one.eu/article/view/9309grape bunch weightprecision viticulturewater stressmorphological image analysisRGB imagesGiESCO 2025
spellingShingle Carlos Poblete-Echeverria
Anke Berry
Talitha Venter
Sergio Velez
Maria Ignacia González Pavez
Rubén Iñiguez
Morphological image analysis for estimating grape bunch weight under different irrigation regimes in Cabernet-Sauvignon
OENO One
grape bunch weight
precision viticulture
water stress
morphological image analysis
RGB images
GiESCO 2025
title Morphological image analysis for estimating grape bunch weight under different irrigation regimes in Cabernet-Sauvignon
title_full Morphological image analysis for estimating grape bunch weight under different irrigation regimes in Cabernet-Sauvignon
title_fullStr Morphological image analysis for estimating grape bunch weight under different irrigation regimes in Cabernet-Sauvignon
title_full_unstemmed Morphological image analysis for estimating grape bunch weight under different irrigation regimes in Cabernet-Sauvignon
title_short Morphological image analysis for estimating grape bunch weight under different irrigation regimes in Cabernet-Sauvignon
title_sort morphological image analysis for estimating grape bunch weight under different irrigation regimes in cabernet sauvignon
topic grape bunch weight
precision viticulture
water stress
morphological image analysis
RGB images
GiESCO 2025
url https://oeno-one.eu/article/view/9309
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AT ankeberry morphologicalimageanalysisforestimatinggrapebunchweightunderdifferentirrigationregimesincabernetsauvignon
AT talithaventer morphologicalimageanalysisforestimatinggrapebunchweightunderdifferentirrigationregimesincabernetsauvignon
AT sergiovelez morphologicalimageanalysisforestimatinggrapebunchweightunderdifferentirrigationregimesincabernetsauvignon
AT mariaignaciagonzalezpavez morphologicalimageanalysisforestimatinggrapebunchweightunderdifferentirrigationregimesincabernetsauvignon
AT rubeniniguez morphologicalimageanalysisforestimatinggrapebunchweightunderdifferentirrigationregimesincabernetsauvignon