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, L2, and W2) 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 W2 showed a good fitting (R2basil = 0.948; R2mint = 0.963; R2sage = 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 (R2 = 0.992; RMSE = 0.327 cm2), mint (R2 = 0.998; RMSE = 0.222 cm2), and sage (R2 = 0.998; RMSE = 0.426 cm2).
Highlights
In modern horticulture, growers need to optimize plant development and yield [1] in order to meet the food demand of increasing populations [2], especially in developing countries [3], and Plants 2020, 9, 13; doi:10.3390/plants9010013 www.mdpi.com/journal/plantsPlants 2020, 9, 13 contribute towards food security and social stability
Data analysis for the three aromatic species facilitated the characterization of variability in the main leaf phenotypic traits observed on different cultivars (Table 1)
As the ability to intercept light is clearly dependent on the two-dimensional leaf structure [27], the characterization of leaf L and W in several species has a large value within the broad fields of botany, plant physiology, and crop science
Summary
Plants 2020, 9, 13 contribute towards food security and social stability Within this scope, applied research on innovative horticultural practices can make effective use of dynamic crop growth models [4] under conditions optimal for plant growth and for eliciting plant response to abiotic stresses [5], allowing a more rational use of resources, such as water and nutrients [4]. The characterization of leaf morphology and quantification of leaf area (LA) and/or leaf area index (LAI) is of paramount importance to horticultural crop science. In this respect, there is an increasing interest in using computer-assisted imaging systems [8] for producing reliable biometric measurements [9] and analyzing phenotypic traits related to plant architecture and leaf characteristics [10]. Data on leaf characteristics can be incorporated into databases [11,12] and employed to validate time-series quantification of leaf morphology (e.g., [13,14]) and to determine the performance of computer-assisted imaging systems and machine learning algorithms used to classify/recognize phenotypic traits of specific genotypes [15]
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