Image-Based Segregation of High-Quality Dragon Fruits Among Ripe Fruits
Coral Ortiz, Nikita Dapurkar, Vicente Alegre, Francisco Rovira-Más

TL;DR
This study uses image analysis to classify ripe dragon fruits by quality, offering a non-destructive and cost-effective method for the European market.
Contribution
A novel non-destructive image-based classification system for dragon fruit quality assessment using visible and ultraviolet imaging.
Findings
The classification system achieved 85% accuracy in segregating dragon fruits into three quality categories.
Very high-quality fruits were identified with 93% accuracy using non-destructive image parameters.
The method proved consistent across two sample sets and offers a reliable alternative to destructive testing.
Abstract
The increasing demand for high-quality dragon fruit in the European market requires efficient quality assessment methods. This study explores a non-destructive image analysis approach for classifying ripe dragon fruits based on fruit ripeness and weight. A low-cost system equipped with visible and ultraviolet lighting was employed to capture images of two sets of samples of 60 and 92 ripe dragon fruits, extracting non-destructive parameters such as visible and ultraviolet perimeter, maximum and minimum diameter and area, and RGB color coordinates. Fruit destructive characterization parameters were also measured. The first set of samples was used to develop a discriminant classification model. In a first step, the main characterization magnitudes were confirmed. A ripening index was calculated based on soluble solid content and acidity. Then, a cluster analysis was used to segregate the…
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Taxonomy
TopicsSpectroscopy and Chemometric Analyses · Botanical Research and Applications · Food Drying and Modeling
