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中期FTIR和机器学习用于预测无花果叶的宏观营养素含量

Lahcen Hssaini¹, Rachid Razouk²

¹Agro-Food Technology and Quality Laboratory, Regional Center of Agricultural Research of Meknes, National Institute of Agricultural Research, Rabat, Morocco. lahcen.hssaini@inra.ma.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry

|June 29, 2025

概括

这项研究使用富里埃变换红外光谱学与减弱总反射率 (FTIR-ATR) 和机器学习 (ML) 来预测无花果叶中的宏观营养素. 梯度增强 (GB) 证明了在可持续农业中快速,非破坏性的植物营养分析的卓越性能.

科学领域:

农业科学农业科学
频谱学是一种光谱学.
机器学习机器学习

背景情况:

准确预测叶子矿物质成分对于植物健康监测和农业优化至关重要.
传统的营养分析方法往往具有破坏性和耗时性.

研究的目的:

开发和评估一种快速,非破坏性的方法,用于预测无花果叶 (Ficus carica L.) 中的宏观营养素 (N,P,K,Ca,Mg) 含量.
为了比较不同机器学习模型的性能,包括随机森林 (RF),支持向量回归 (SVR) 和梯度增强 (GB),当与FTIR-ATR光谱学相结合时.

主要方法:

采用富里埃变换的红外光谱学与减弱总反射率 (FTIR-ATR) 来收集90个无花果叶的光谱数据.
使用基线校正和第二导数转换预处理了 Spectra.
三个机器学习模型 (RF,SVR,GB) 被训练并使用五倍交叉验证进行评估,评估RMSE,R2和RPD的性能.

主要成果:

与RF和SVR相比,渐变增强 (GB) 模型在所有宏观营养素中表现出优异的预测性能.
英国取得了 (Mg) 和 (P) 的最佳验证结果,R2值分别为0.7351和0.6873.
虽然观察到一些过度拟合 (训练R2 ≈ 0.999与验证R2 = 0.61-0.74),但GB的可靠性 (RPD> 1.5) 表明它对营养查的实用性.

结论:

与机器学习相结合的FTIR-ATR,特别是梯度增强模型,为传统植物组织分析提供了可行的,快速和非破坏性的替代方案.
该模型的性能为精确的营养管理提供了可操作的见解,为可持续的农业实践做出了贡献.
对于Mg和P的独特光谱特征可能有助于它们更高的预测准确度.

关键词:

在FTIR-ATR光谱学中使用FTIR-ATR光谱.无花果树的叶子无花果树的叶子梯度增强可以提高梯度.机器学习是机器学习.矿物质预测的预测

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