中国农业科技导报2024，Vol.26Issue(7)：103-110,8.DOI:10.13304/j.nykjdb.2023.0164
基于RGB和CIELab预测紫苏叶片花青素含量
Prediction of Anthocyanin Content in Perilla frutescens Leaves Based on RGB and CIELab
摘要
Abstract
In order to promote the breeding of Perilla frutescens varieties with high anthocyanin and guide the production management of Perilla frutescens under the stress of adversity,Perilla frutescens was as the research object,the field leaves were collected and taken photos with a digital camera.The image color of photo was analyzed by red green blue color space(RGB)and CIELab color space.And the relationship between color parameters and leaf anthocyanin content was analyzed for screening out color parameters with high correlation coefficient.Univariate regression inversion model was established,and finally the best predictive model of anthocyanin content in the leaves of Perilla frutescens was obtained.The results showed that in RGB color space,the normalized redness intensity(NRI)and normalized greenness intensity(NGI)had significant correlations with anthocyanin content,and the correlation coefficient of NGI was greater than that of NRI.When the contribution ratio of leaves front and leaves back was 2∶1,the correlation between NGI and anthocyanin content was the highest with the correlation coefficient 0.853 2.Compared with different models,it was found that the exponential model established with NGI as the independent variable had the best fitting effect with the correlation coefficient 0.838 1 and the coefficient of determination(R2)0.755 0.In the CIELab color space,a* had the best correlation with anthocyanin content,and the correlation coefficient reached the maximum(0.735 6)when the contribution ratio of leaf front and leaf back was 2∶1.The power model based on a* had the best fitting effect,and the correlation coefficient and R2 were 0.743 8 and 0.679 8,respectively.The NGI model and a* model were respectively used to estimate the content of anthocyanin in leaves.After verification,it was found that the prediction effect of the a* model was better with higher accuracy and stability.Therefore,the model of a* was used as the best model to predict the content of anthocyanins in Perilla frutescens leaves.关键词
紫苏/RGB/CIELab/花青素/数码相机Key words
Perilla frutescens/RGB/CIELab/anthocyanin/digital camera分类
农业科技引用本文复制引用
刘徐冬雨,郭潇潇,付晨青,韩蕊,李国辉,王秀萍..基于RGB和CIELab预测紫苏叶片花青素含量[J].中国农业科技导报,2024,26(7):103-110,8.基金项目
河南省农业科学院"四优四化"科技支撑行动计划项目(20220901005). （20220901005）
