Exploring the Quality Characteristics of Peaches Through Data Visualization

Understanding the physical and biochemical properties of peaches is essential for improving agricultural practices, postharvest handling, and quality classification. In this study, data visualization techniques-including correlation analysis, principal component analysis (PCA), and clustering algorithms-were used to investigate patterns among key peach quality attributes. A dataset of 200 peach samples was analyzed based on physical, biochemical, electrical impedance, and CIELAB colorimetric features. The results revealed interpretable quality-based groupings using unsupervised k-means and hierarchical clustering. PCA biplots and trait-wise boxplots highlighted the discriminative roles of firmness, soluble solids content, skin color, and impedance. These findings support the development of datadriven classification systems for fruit quality assessment, with potential applications in postharvest grading and supply chain optimization.