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

Abstract

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.

Plant phenotyping relevance

イチゴ果実の成熟度という植物器官の状態を、MIR分光と特徴選択・分類器で非破壊推定する方法を開発し、交差検証や統計検定で性能評価しており、フェノタイピング手法が中心である。

Code and data availability

The paper's analysis code is explicitly stated to be publicly available at the authors' GitHub release URL. The spectral dataset itself is not public and is available only from the corresponding author on request.