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 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.