Enhancing strawberry maturity assessment using mid-infrared spectral analysis with advanced variable selection and supervised classification

Accurate and non-destructive assessment of fruit maturity is critical for sustainable agricultural practices. This study proposes a novel framework for evaluating strawberry ripeness using Mid-Infrared (MIR) spectroscopy combined with metaheuristic feature selection and supervised classification. A dataset of 443 strawberries spanning eight maturity stages was analyzed using six metaheuristic algorithms—Binary Grey Wolf Optimizer, Binary Particle Swarm Optimizer, Bee Colony Optimizer, Genetic Algorithm, Ant Colony Optimizer, and Gravitational Search Optimizer—integrated with four classifiers: Naïve Bayes, Decision Tree, Linear Discriminant Analysis, and Support Vector Machine. A new fitness function was designed to optimize classifier performance, and results were validated through Self-Organizing Map Neural Networks, cross-validation, and statistical significance testing. The Genetic Algorithm–Linear Discriminant Analysis combination achieved the highest and most stable accuracy (94.6–99%), outperforming existing image-based, deep learning, and conventional spectroscopic approaches while retaining interpretability. These findings demonstrate that metaheuristic-driven MIR analysis provides a robust, explainable, and efficient method for precise strawberry maturity assessment, offering significant potential for advancing eco-friendly and intelligent agricultural practices.