Pomelo maturity classification from field-acquired images using oil-gland morphology and a rule-based image-processing pipeline - Sopapun Suwansawang; Harutai Dinsakul; Wirot Buangam; Jiraroj Tosasukul (Institute of Advanced Engineering and Science, 2026)
Pomelo maturity assessment in commercial orchards relies predominantly on vi sual inspection and harvest age records, which introduce inconsistency in post harvest grading. Non-destructive alternatives such as near-infrared spectroscopy and acoustic sensing have been reported, but typically require specialised instruments and controlled acquisition conditions. This study investigates the feasibility of oil-gland morphology as an interpretable maturity indicator, implemented as a rule-based image-processing pipeline executable on standard CPU hardware without model training. A hierarchical rule-based framework was developed to classify pomelo maturity from gland count features extracted under natural outdoor illumination. Thirty-three Citrus maxima samples (Khao Yai cultivar) representing three maturity stages were analysed in this proof-of-concept study (n = 11 per stage). The pipeline integrates adaptive thresholding, subregion segmentation, multi-scale morphological detection, and threshold-based classification. Detection reliability was verified on synthetic dot-pattern images prior to real-sample evaluation. On the collected dataset, the framework achieved an overall accuracy of 78.8% with a macro-averaged F1-score of 0.784. No mis classification occurred between the immature and mature groups; errors arose exclusively between adjacent stages. Mean processing time was 57 seconds per image on a consumer-grade laptop. Given the limited sample size and single cultivar scope, these results represent methodological feasibility rather than validated generalisation, and establish a baseline for morphology-based maturity assessment in pomelo.