Mid-FTIR and machine learning for predicting fig leaf macronutrients content

Predicting leaf mineral composition is critical for monitoring plant health and optimizing agricultural practices. This study combines Fourier-transform infrared spectroscopy with attenuated total reflectance (FTIR-ATR) and machine learning (ML) to specific macronutrients, namely nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg), in fig leaves (Ficus carica L.). A dataset of 90 leaves was analyzed, with FTIR spectra (450-4000 cm⁻¹) preprocessed via baseline correction and second-derivative transformations. Three ML models were evaluated using fivefold cross-validation including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (GB), with performance assessed via root mean square error (RMSE), coefficient of determination (R²), and ratio of performance to deviation (RPD). GB outperformed other models, achieving validation RMSE/RPD values of 0.133/1.60 (nitrogen, N), 0.0107/1.79 (phosphorus, P), 0.1328/1.65 (potassium, K), 0.0636/1.96 (magnesium, Mg), and 0.2657/1.60 (calcium, Ca). Predictions for Mg (validation R² = 0.7351) and P (validation R² = 0.6873) exhibited the highest accuracy, potentially attributed to their stronger or more distinct spectral features (e.g., Mg-O stretching around 1050- 1150 cm⁻¹; P-O vibrations around 1240 cm⁻¹). Cross-validation revealed robust generalization for GB; while mean training RMSE was very low (< 0.01 for P and Mg), validation RMSE remained relatively low, underscoring the model's utility for screening (RPD > 1.5). Despite evidence of overfitting (training R² ≈ 0.999 vs. validation R² = 0.61-0.74), GB's performance evaluated using both RMSE and RPD confirmed its superiority over RF and SVR, which showed higher errors (e.g., SVR for Ca: RMSE = 0.4574, RPD = 1.07). This study demonstrates that FTIR-ATR coupled with ML is a rapid, non-destructive alternative to conventional destructive chemical analysis and that GB's reliability, as indicated by RPD values > 1.5, offers actionable insights for precision nutrient management in sustainable agriculture.