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Nontargeted metabolomics-based multiple machine learning modeling boosts early accurate detection for citrus Huanglongbing

代谢组学 生物 人工智能 机器学习 决策树 逻辑回归 鉴定(生物学) 计算机科学 模式识别(心理学) 生物信息学 植物
作者
Zhixin Wang,Yue Niu,Tripti Vashisth,Jingwen Li,Robert Madden,Taylor Shea Livingston,Yu Wang
出处
期刊:Horticulture research [Springer Nature]
被引量:1
标识
DOI:10.1093/hr/uhac145
摘要

Abstract Early accurate detection of crop disease is extremely important for timely disease management. Huanglongbing (HLB), one of the most destructive citrus diseases, has brought about severe economic losses for the global citrus industry. The direct strategies for HLB identification, such as quantitative real-time polymerase chain reaction (qPCR) and chemical staining, are robust for the symptomatic plants but powerless for the asymptomatic ones at the early stage of affection. Thus, it is very necessary to develop a practical method used for the early detection of HLB. In this study, a novel method combining ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS)-based nontargeted metabolomics and machine learning (ML) was developed for conducting the early detection of HLB for the first time. Six ML algorithms were selected to build the classifiers. Regularized logistic regression (LR-L2) and gradient-boosted decision tree (GBDT) outperformed with the highest average accuracy of 95.83% to not only classify healthy and infected plants but identify significant features. The proposed method proved to be practical for early detection of HLB, which tackled the shortcomings of low sensitivity in the conventional methods and avoid the problems such as lighting condition interference in spectrum/image recognition-based ML methods. Additionally, the discovered biomarkers were verified by the metabolic pathway analysis and content change analysis, which was remarkably consistent with the previous reports.
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