Exploring phenotypic plasticity leaf trait relationships in fungal-resistant grapevines using linear regression: Implications of the genotype environment interaction

Accurate and non-destructive models for predicting leaf area (LA) are essential for monitoring vineyard growth and developing automated algorithms. In this study, we developed and compared the performance of eight linear regression models for predicting LA in eleven fungal-resistant grapevine genotypes. We also explored the phenotypic plasticity of leaf traits and their relationship with LA using kernel density estimation analysis. We found that genotype played a major role in defining leaf shape, and genotype-environment interaction was observed. The best models for LA estimation were identified for each genotype, and a leaf deformation index was proposed. Our results provide accurate and robust models for estimating LA in fungal-resistant grapevine genotypes and demonstrate the relationship between leaf traits and the environment. Additionally, we present a method for defining leaf asymmetry. Overall, this study contributes to the development of non-destructive and automated techniques for monitoring vineyard growth.