Analysis of Cultivar-Specific Variability in Size-Related Leaf Traits and Modeling of Single Leaf Area in Three Medicinal and Aromatic Plants: Ocimum basilicum L., Mentha Spp., and Salvia Spp.

Abstract

In this study, five allometric models were used to estimate the single leaf area of three well-known medicinal and aromatic plants (MAPs) species, namely basil ( Ocimum basilicum L.), mint ( Mentha spp.), and sage ( Salvia spp.). MAPs world production is expected to rise up to 5 trillion US$ by 2050 and, therefore, there is a high interest in developing research related to this horticultural sector. Calibration of the models was obtained separately for three selected species by analyzing (a) the cultivar variability—i.e., 5 cultivars of basil (1094 leaves), 4 of mint (901 leaves), and 5 of sage (1103 leaves)—in the main two traits related to leaf size (leaf length, L, and leaf width, W) and (b) the relationship between these traits and single leaf area (LA). Validation of the chosen models was obtained for each species using an independent dataset, i.e., 487, 441, and 418 leaves, respectively, for basil (cv. ‘Lettuce Leaf’), mint (cv. ‘Comune’), and sage (cv. ‘Comune’). Model calibration based on fast-track methodologies, such as those using one measured parameter (one-regressor models: L, W, L ², and W ²) or on more accurate two-regressors models (L × W), allowed to achieve different levels of accuracy. This approach highlighted the importance of considering intra-specific variability before applying any models to a certain cultivar to predict single LA. Eventually, during the validation phase, although modeling of single LA based on W ² showed a good fitting (R ² _basil = 0.948; R ² _mint = 0.963; R ² _sage = 0.925), the distribution of the residuals was always unsatisfactory. On the other hand, two-regressor models (based on the product L × W) provided the best fitting and accuracy for basil (R ² = 0.992; RMSE = 0.327 cm ²), mint (R ² = 0.998; RMSE = 0.222 cm ²), and sage (R ² = 0.998; RMSE = 0.426 cm ²).