Machine Learning-Based Non-Destructive Prediction of Juice Sac Granulation in Guanxi Honey Pomelo.

Abstract

The juice sac granulation of citrus fruits is a biological disorder that commonly occurs during the stages of growth, mature, and post-harvest, which severely affects the quality and reduces consumer acceptance of fruits. To explore the correlation between granulation and both external morphological characteristics and internal quality characteristics, 11 external and internal quality characteristics of Guanxi honey pomelo were collected and systematically analyzed by principal component analysis and linear regression. Then seven external quality characteristics and one critical characteristics, GR% were applied in machine learning modeling. The results indicated that several characteristics such as single fruit weight, single fruit volume, longitudinal diameter, and transverse diameter showed positive correlations with juice sac granulation rate (GR%), and were subsequently incorporated into classification model development. Among the five models evaluated, support vector machine demonstrated superior performance with a precision and recall rate of 100.00% and 100.00%, respectively, verifying its favorable accuracy and robustness. This research combined traditional statistical approaches with modern computational techniques, offering a reliable screening solution for juice sac granulation degree of Guanxi honey pomelo, which provided potential applicability in citrus processing industries and a theoretical foundation for non-destructive quality assessment.

Plant phenotyping relevance

果実の外観・内部特性から果肉粒化率を非破壊予測する機械学習モデルを開発・評価しており、植物状態の取得・推定手法が研究の中心です。

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