Trait-based machine learning modeling of soluble carbohydrate content in Trachyspermum ammi exposed to organic and chemical fertilization and salicylic acid.

Accurate assessment of biochemical traits in medicinal plants is essential for supporting environmentally responsible agriculture, improving crop quality, and enhancing the nutritional and pharmacological value of plant-derived products. Although medicinal plants are rich in bioactive compounds, conventional methods for measuring key biochemical components, such as soluble carbohydrates, are often time-consuming, destructive, and resource-intensive. Trachyspermum ammi L. (Ajwain) is valued for its antioxidant, antimicrobial, and digestive properties, highlighting the need for rapid, reliable, and non-destructive evaluation methods. Despite previous studies on fertilization effects on growth and bioactive compounds in T. ammi, research integrating morpho-physiological data with machine learning to predict key biochemical traits remains limited. In this study, we applied Multilayer Perceptron (MLP) and Gaussian Process Regression (GPR) models to estimate soluble carbohydrate content in a non-invasive and efficient manner. A dataset including morphological, biochemical, physiological, and macronutrient traits was used as input variables. Fertilization regimes and salicylic acid (SA) treatments were applied to induce variability in plant traits but were not directly included as model features, ensuring that predictions were trait-based. Models were trained and evaluated on n = 45 samples using five-fold cross-validation. Among the tested models, MLP and GPR achieved the highest predictive accuracy, particularly when the full feature set was used. Predictions based solely on biochemical and physiological traits were nearly as accurate as those using all variables, suggesting that these traits provide reliable and cost-effective estimates. Considering the limited dataset, results should be interpreted with caution, and future studies using larger, independent datasets are recommended to further assess model robustness and generalizability. These findings demonstrate the practical potential of the proposed machine learning approach for rapid, non-destructive assessment of biochemical traits in medicinal plants and may inform the development of GUI-based decision-support tools for precision agriculture and phytopharmaceutical research.