Ripeness Evaluation Using Near-Infrared (NIR) Spectroscopy and NLP for Interpretability
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Abstract
This study develops a non-destructive avocado ripeness classification system using a low-cost, portable near-infrared (NIR) scanner and machine learning. Traditional ripeness assessment methods are often destructive and subjective, limiting their efficiency in agricultural practices. To address this issue, we developed a custom NIR scanner capable of capturing spectral information across 18 discrete wavelength bands for avocado ripeness classification. The research focuses on Buccaneer avocados sourced from the Royal Project, with samples collected from both the Royal Project Gardens and Sorting Plant. A total of 120 kg of avocados were systematically sampled and categorized by agricultural experts into three ripeness stages: raw, ripe, and aged. This study applies Multiplicative Scatter Correction (MSC) to preprocess NIR spectra, enhancing feature separation before machine learning model training. This study evaluates three classification models: Random Forest, XGBoost, and the Gaussian Mixture Model (GMM). Random Forest achieved the highest classification accuracy (78%) with an AUC score of 0.93, followed by XGBoost (74% accuracy, AUC 0.91). GMM performed worse, with 42% accuracy and an AUC of 0.58. Additionally, Natural Language Processing (NLP) was applied to convert model predictions into human-readable ripeness descriptions, assisting farmers in decision-making. This study demonstrates that low-cost NIR technology combined with AI-driven analysis enables efficient, non-destructive classification of avocado ripeness.
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