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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| Format: | Article |
| Language: | English |
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International Viticulture and Enology Society
2025-06-01
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| 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 |
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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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| format | Article |
| id | doaj-art-b706fd31c72c49dfa281da1361fd11db |
| institution | OA Journals |
| issn | 2494-1271 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | International Viticulture and Enology Society |
| record_format | Article |
| series | OENO One |
| 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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