A Hybrid Analytical Framework for Cracking and Some Fruit Quality Features in Sweet Cherries

Abstract

This study investigates the relationships among fruit quality traits, cracking susceptibility, and bioactive compounds in sweet cherries. Eleven genotypes collected from Northern Anatolia and two standard cultivars were evaluated. Key fruit characteristics were measured, and novel machine learning algorithms were applied to identify associations between variables. A negative correlation was found between the cracking index and fruit weight (r = -0.23), while a positive correlation was observed with total soluble solids (TSS) (r = 0.29). Furthermore, TSS was positively correlated with stem thickness (r = 0.67). Machine learning analyses indicated that DPPH and anthocyanin content were the most influential factors associated with the cracking index. A hybrid analytical pipeline was developed by integrating Principal Component Analysis (PCA) for dimensionality reduction, Random Forest regression for nonlinear prediction and Shapley Additive Explanations (SHAP) for interpretability. This triad offers a robust and replicable framework for trait-dissection studies in horticultural phenotyping, enabling deeper insights into complex trait interactions. These findings suggest that, beyond their recognized health benefits, bioactive compounds also positively contribute to fruit quality parameters. The results provide valuable insights for future sweet cherry breeding programs aimed at improving both nutritional and physical fruit traits.