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整合微生物组和机器学习,精确诊断大米巴卡纳病

Sishi Chen¹, Fahui Yuan¹, Hongda Fang²

¹College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.

|December 30, 2025

概括

这项研究将植物微生物组数据与机器学习 (ML) 整合在一起,以诊断大米中的真菌巴卡纳病. 随机森林模型实现了高精度,为精准农业提供了新的策略.

科学领域:

农业科学农业科学
微生物学微生物学
计算生物学计算生物学

背景情况:

巴卡纳病对全球大米生产构成重大威胁.
植物微生物组在抗压力方面的作用未得到充分利用.
高维微生物组数据需要先进的分析方法.

研究的目的:

整合微生物组分析和机器学习 (ML) 来诊断巴卡纳病.
为了确定与疾病严重程度相关的微生物生物标志物.
开发和评估ML模型,以准确检测疾病和严重程度分类.

主要方法:

分析大米植物微生物组的组成.
使用随机森林 (RF),支向量机 (SVM) 和卷积神经网络 (CNN) 开发诊断模型.
应用基于Bray-Curtis不相似性的提取方法和K-means集群用于严重程度分类.

主要成果:

在Gammaproteobacteria/Bacteroidia和bakanae疾病严重程度之间发现了显著的相关性.
射频模型实现了88.9%的准确性和94.1%的F1评分来诊断疾病.
一个基于家庭级分类学数据的模型准确地预测了感染严重程度 (77.8%的准确率).

结论:

将微生物组数据与ML数据相结合,为诊断巴卡纳病提供了一个强大的策略.
开发的ML模型显示了在米种植中推进精准农业的潜力.
基于微生物组的诊断可以改善疾病管理,减少产量损失.

关键词:

富萨里乌姆 (Fusarium) 是一个富士基罗伊 (fujikuroi) 的植物.巴卡纳病是巴卡纳病的一种疾病.疾病的诊断疾病的诊断植物微生物组是植物的微生物组.随机的森林随机的森林

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