茶叶科学2025，Vol.45Issue(3)：393-401,9.
AI茶树育种技术:以黄化性状预测为例
AI in Tea Breeding:A Case Study on Prediction of the Yellowing Trait
摘要
Abstract
Tea plant(Camellia sinensis),as a crucial economic crop,faces core challenges in quality improvement through breeding.To address the prolonged traditional breeding cycle(≥2 years)and inefficient phenotypic identification,this study utilized 90 progeny from natural hybridization of a chlorotic cultivar'Anjihuangye',integrating genotypic data from 40 326 core single nucleotide polymorphism(SNP)loci with biennial phenotypic observations(chlorotic∶non-chlorotic=54∶36).We systematically compared three machine learning models(logistic regression,random forest,and support vector machine)for predictive performance.The results demonstrate that the random forest model achieved the best performance in the 10-fold cross-validation,and its accuracy was 78.96%,which was significantly better than other models(P＜0.05).Feature importance analysis identifies two critical genetic markers:Chr8_142477650(encoding the chloroplast-localized pyruvate dehydrogenase E1 beta subunit)and Chr8_126475215(involved in RNA processing regulation).However,independent validation using 109 germplasms with diverse yellowing trait reveals that the prediction accuracy of the model decreased to 21.10%,and the feature weight deviations caused by genetic background heterogeneity was the main limiting factor.In this study,a machine learning prediction framework for tea yellowing trait was established,which shortened the phenotypic identification cycle from 24 months to real-time genotype analysis,and realized the prediction of traits in the early stage of breeding.Although cross-cultivar generalizability requires improvement,the developed SNP-phenotype association model provided an extensible paradigm for deciphering genotype-phenotype complexity in tea plants,representing an innovative application of artificial intelligence in predicting complex traits of woody perennials.关键词
茶树育种/机器学习/黄化性状/性状预测Key words
tea breeding/machine learning/leaf yellowing trait/trait prediction分类
农业科学引用本文复制引用
徐歆,李亚奇,杨亦扬,徐琪,钱雪飞,马春雷,梅菊芬..AI茶树育种技术:以黄化性状预测为例[J].茶叶科学,2025,45(3):393-401,9.基金项目
国家重点研发计划(2024YFD1200504)、江苏省种业振兴"揭榜挂帅"项目(JBGS[2021]085)、"太湖之光"科技攻关项目(N20231002) （2024YFD1200504）
