A Model-based Method Deployed on Smartphones for Evaluating Hardness and Anthocyanin Content of Blue Honeysuckle Berry

摘要

A machine learning-based APP may quickly and non-destructively evaluate the quality of parameters, such as hardness and anthocyanin content in blue honeysuckle berries(Lonicera caerulea L., BHB), based on changes in pericarp color characteristics. The color feature information of the BHB pericarp was extracted, and the corresponding hardness and anthocyanin content were determined at various growing stages. Correlation analysis of BHB quality indexes was conducted by single and combined components of BHB epidermal color features. The results showed that fruit hardness had a significantly negative correlation with color feature parameter R-G, and its anthocyanin content had a significantly positive correlation with color feature parameter R. Comparing the eight models, random forest(RF) was established to evaluate the hardness and anthocyanin content of BHB according to the correlation between pericarp color features and hardness and anthocyanin content on BHB quality evaluation APP on the We Chat platform. The credibility of APP embedding RF model for evaluating hardness and anthocyanin content in BHB was validated with the determination coefficient of 0.89 and 0.93 in practice. This approach could efficiently and conveniently evaluate the quality indexes of BHB in real time and serve as a technical reference for the detection of quality indicators of other berries using smartphones.

关键词

WeChat APP / blue honeysuckle berry / anthocyanins / quality evaluation / random forest model