ResNet-18 Citrus Classification with Augmentation: Peel Color Associations with pH and Vitamin C
Abstract
Limes and lemons are widely consumed, but field quality assessment is often subjective. This study built a ResNet-18 citrus classifier using transfer learning and data augmentation, and evaluated it with stratified 5-fold cross-validation on 80 images (40 limes, 40 lemons). All analyses were conducted per fold to reduce optimistic bias. The model reached 98.75% mean validation accuracy, misclassifying one lime image while correctly recognizing all lemons. For interpretation, peel regions were quantified using NDYI and CIELab (L*, a*, b*), and related to pH and vitamin C using Spearman correlation. Uncertainty was quantified with bootstrap-based 95% confidence intervals for each correlation. Peel color features were more consistently associated with pH (especially in limes), whereas correlations with vitamin C were weak and non-significant for both fruits. Results indicate strong performance under controlled imaging, but using peel color as a vitamin C proxy requires broader data and external validation across cameras and lighting.
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