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使用机器学习算法和地理空间分析预测Nepeta persica中的nepetalactone积累

Emran Dastres¹, Ali Sonboli², Hassan Esmaeili¹

¹Department of Agriculture, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, Tehran, 1983969411, Iran.

Scientific reports

|August 27, 2025

概括

环境因素如海拔和温度对药用植物Nepeta persica中的nepetalactone产生重大影响. 机器学习模型确定了伊朗这个有价值的植物的最佳种植区.

科学领域:

药用植物研究
计算生态学
农业科学

背景情况:

由于含有高酸盐,Nepeta persica具有显著的药理价值.
对于有效的种植和保护来说,了解环境对酸盐生物合成的影响至关重要.
优化药用植物化合物的生产需要详细的生态和气候数据.

研究的目的:

调查气候,地形和地形因素对Nepeta persica中的nepetalactone度的影响.
开发和验证混合机器学习模型来预测nepetalactone积累.
通过地理空间分析确定适合种植Nepeta persica的息地.

主要方法:

使用的机器学习算法:随机森林 (RF),支持向量机 (SVM),梯度增强机 (GBM) 和混合RF-SVM-GBM组合.
使用统计方法,包括通用线性模型 (GLM) 和部分最小平方 (PLS).
综合地理空间分析 (GIS,遥感,息地适应性建模) 以评估环境驱动因素.

主要成果:

确定了海拔高度,面向南方的坡度和年平均温度是影响内酸盐度的关键因素.
混合RF-SVM-GBM模型实现了最高的预测准确性,超过单个模型.
马尔瓦达什特和希拉兹县被确定为高nepetalactoneNepeta persica种植的主要地点.

结论:

这项研究为精准农业和Nepeta persica的可持续种植提供了数据驱动的框架.
这些发现支持资源高效的种植和适应气候的药用植物保护策略.
在具有挑战性的环境中,生态建模与机器学习相结合,为优化二次代谢物生产提供了可扩展的方法.

关键词:

地理空间分析息地适应性建模机器学习组合尼佩塔拉克的生物合成精准农业

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