Smart non-destructive prediction of antioxidant, mineral, and essential oil traits in Persian parsley (Petroselinum crispum Mill.) landraces.

An integrative principal component analysis-artificial neural network (PCA-ANN) framework was developed to characterize ecotypic variation among parsley landraces and predict quality-related traits in medicinal and aromatic plants. Fifteen Iranian parsley landraces collected from diverse agro-ecological regions, together with two commercial cultivars as reference genotypes, were analyzed to establish predictive links between easily measurable morphological traits and key biochemical, mineral, and essential-oil (EO) characteristics. Twenty-one independent morphological variables were recorded and used as model inputs. To minimize redundancy and multicollinearity, PCA was applied exclusively to the morphological dataset, reducing it to a smaller set of uncorrelated components that preserved most of the variance. These components served as input features for optimized ANN architectures developed to predict antioxidant properties, EO yield and composition, and mineral nutrient content. The resulting PCA-ANN framework achieved strong predictive performance, with R² up to 0.94. It accurately predicted antioxidant, mineral, and compositional profiles from morphological traits alone, demonstrating the potential of morphological phenotyping as a rapid, non-destructive proxy for complex chemical analyses. This integrative modeling approach reduces reliance on time-consuming and costly procedures such as GC-MS and offers a practical decision-support tool for genotype selection, breeding, and quality evaluation in medicinal and aromatic crops. The proposed framework provides a scalable, data-driven strategy for advancing precision agriculture and sustainable management of herbal plant resources.