Coffee is one of Peru’s most important agricultural commodities, and its productivity is highly influenced by environmental variability. This study aimed to evaluate agro-morphological traits, proximate bean composition, and the phenotypic plasticity index (PPI) of Coffea arabica (Catimor variety) cultivated in three neighboring provinces of Piura: Ayabaca, Huancabamba, and Morropón. Unlike previous studies that broadly compare distant regions, this research focuses on geographically close yet climatically contrasting environments, providing new insight into how microclimatic and edaphic variability shape both morphological and chemical traits. A total of 300 plants were sampled, and 12 morphological descriptors were recorded alongside proximate composition analyses of moisture, crude protein, fiber, ash, fat, and carbohydrates. Multivariate approaches, including cluster analysis, multiple correspondence analysis, and Pearson correlations, were applied to determine groupings and trait associations. Results indicated that 12 morphological variables consistently reflected species-specific descriptors, forming two statistical groups, with Morropón showing the greatest homogeneity. Significant differences (p ≤ 0.05) were observed in the proximate composition of most variables, except for crude fiber and carbohydrates. Morropón beans showed the highest fat and moisture values, while Huancabamba had elevated protein and ash levels. Morphological traits exhibited higher plasticity (PPI = 0.70) compared with proximate traits (PPI = 0.21). These findings reveal that even within short spatial distances, coffee plants exhibit marked phenotypic differentiation driven by local environmental factors, offering a novel, fine-scale perspective on trait variability relevant to breeding and adaptation studies under changing climatic conditions.

This study evaluated the integration of non-invasive remote sensing and colorimetry to classify the maturity stages of Coffea arabica fruits across four varieties: Caturra Amarillo, Excelencia, Milenio, and Típica. Multispectral signatures were captured using a Parrot Sequoia camera at wavelengths of 550 nm, 660 nm, 735 nm, and 790 nm, while colorimetric parameters L*, a*, and b* were measured with a high-precision colorimeter. We conducted multivariate analyses, including Principal Component Analysis (PCA) and multiple linear regression (MLR), to identify color patterns and develop predictors for fruit maturity. Spectral curve analysis revealed consistent changes related to ripening: a decrease in reflectance in the green band (550 nm), a progressive increase in the red band (660 nm), and relative stability in the RedEdge and near-infrared regions (735–790 nm). Colorimetric analysis confirmed systematic trends, indicating that the a* component (green to red) was the most reliable indicator of ripeness. Additionally, L* (lightness) decreased with maturity, and the b* component (yellowness to blue) showed varying importance depending on the variety. PCA accounted for over 98% of the variability across all varieties, demonstrating that these three parameters effectively characterize maturity. MLR models exhibited strong predictive performance, with adjusted R² values ranging between 0.789 and 0.877. Excelencia achieved the highest predictive accuracy, while Milenio demonstrated the lowest, highlighting varietal differences in pigmentation dynamics. These findings show that combining multispectral imaging, colorimetry, and statistical modeling offers a non-destructive, accessible, and cost-effective method for objectively classifying coffee maturity. Integrating this approach into computer vision or remote sensing systems could enhance harvest planning, reduce variability in specialty coffee lots, and improve competitiveness by ensuring greater consistency in cup quality.