Maize and Soybean Moisture Prediction Based on Spectral Sparse Modeling Method Triggered by KKT Optimality Conditions

As important crops for ensuring food security, maize and soybeans require precise moisture content detection, which is crucial for the safety of grain storage and quality control. Traditional methods like oven drying are slow and destructive, failing real-time monitoring needs. Near-infrared (NIR) spectroscopy offers non-destructive rapid analysis but faces challenges with high-dimensional data: traditional LASSO suffers from unstable feature selection due to collinearity and inefficient parameter optimization. This study introduces KKT-LASSO with random perturbation to address these issues, enabling efficient feature selection and parameter tuning. The method uses Karush-Kuhn-Tucker (KKT) conditions to track regularization paths dynamically, reducing computation while stabilizing collinear data handling. Combined with multiplicative scatter correction and partial least squares regression (PLSR), it processes preprocessed spectra to build moisture prediction models. Experimental results show strong performance: corn achieves R² 0.9988 with 40 wavelengths in 351 iterations, and soybeans R² 0.9847 with 25 wavelengths in 129 iterations. The approach efficiently selects relevant features, outperforming conventional methods in accuracy and interpretability. This research provides a reliable solution for real-time moisture monitoring, enhancing smart agricultural management and food security through efficient spectral analysis.