Spectroscopy Analysis with Machine Learning Regression for the Quantification of Carbon and Nitrogen Contents in Inceptisol and Oxisol Soil Types: Comparing Different Preprocessing and Validation methods as well as Feature Importance

📄 arXiv: 2607.00834v1 📥 PDF

作者: Vinicius Herique Kieling, Guilherme Macedo Baggio, Felipe Augusto Bueno Rossi, Marco Antonio de Castro Barbosa, Dalcimar Casanova, Larissa Macedo dos Santos Tonial, Jefferson Tales Oliva

分类: cs.LG

发布日期: 2026-07-01

💡 一句话要点

提出机器学习回归方法以量化土壤中碳氮含量

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)

关键词: 近红外光谱 机器学习 土壤分析 碳氮量化 数据预处理 集成学习 可持续农业

📋 核心要点

  1. 现有的土壤分析方法通常耗时长、成本高且破坏性强，难以满足快速和环保的需求。
  2. 本文提出利用近红外光谱数据结合机器学习方法，优化土壤中碳氮含量的预测模型，提升分析效率。
  3. 实验结果表明，所提模型在不同土壤类型中均表现出良好的预测能力，RPD值超过2.0，显示出低过拟合现象。

📝 摘要（中文）

近红外(NIR)光谱分析作为传统土壤分析方法的替代方案，具有快速、低成本和非破坏性测试的优点。本文提出了一种机器学习方法，通过便携式MyNIR设备获取的NIR光谱数据，校准Oxisols和Inceptisols土壤中碳(C)和氮(N)含量的预测模型。评估了多种预处理方法，其中Savitzky-Golay(SG)滤波器和基于非线性迭代偏最小二乘(NIPALS)算法的稳健异常值去除方法效果最佳。比较了多种验证策略，模型使用R2、均方根误差(RMSE)、平均绝对误差(MAE)和性能偏差比(RPD)等指标进行评估，结果显示土壤类型间的性能差距反映了土壤特征的影响。模型实现了RPD > 2.0，验证了该方法在快速量化C和N方面的潜力，促进了可持续农业实践的优化。

🔬 方法详解

问题定义：本文旨在解决传统土壤分析方法的局限性，包括时间长、成本高和破坏性强的问题。现有方法难以快速、准确地量化土壤中的碳氮含量。

核心思路：通过结合近红外光谱(NIR)数据和机器学习技术，提出了一种新的预测模型，旨在提高土壤分析的效率和准确性。

技术框架：整体流程包括数据采集、预处理、模型训练和验证。首先使用便携式MyNIR设备获取NIR光谱数据，然后应用Savitzky-Golay滤波器和NIPALS算法进行数据预处理，最后采用堆叠集成学习模型进行预测。

关键创新：本研究的创新点在于结合了多种预处理方法和集成学习技术，特别是使用了基于Huber损失函数的异常值去除方法，显著提升了模型的预测性能。

关键设计：在模型设计中，采用了部分最小二乘(PLS)、支持向量回归(SVR)和岭回归作为基础模型，并使用线性回归作为元模型，评估指标包括R2、RMSE、MAE和RPD。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，所提出的模型在不同土壤类型中的RPD值均超过2.0，表明模型具有良好的预测能力且低过拟合。这一成果为快速量化土壤中碳氮含量提供了有效的解决方案，具有重要的实际应用价值。

🎯 应用场景

该研究的潜在应用领域包括农业土壤管理、环境监测和可持续发展等。通过快速准确地量化土壤中的碳氮含量，农民和顾问可以更有效地进行土壤改良和施肥决策，从而提高农业生产效率和生态环境保护。

📄 摘要（原文）

Near-Infrared (NIR) spectroscopy has emerged as a promising alternative to traditional soil analysis methods, offering advantages such as speed, low cost, and non-destructive testing. This work proposes a machine learning (ML) approach to calibrate predictive models for carbon (C) and nitrogen (N) content in Oxisols and Inceptisols, utilizing NIR spectral data acquired with a portable MyNIR device. Various preprocessing methods were evaluated, with the most effective being the Savitzky-Golay (SG) filter and a robust outlier removal method based on the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm coupled with a Huber loss function. Multiple validation strategies were compared, including 10-fold cross-validation, leave-one-out, and holdout via the Kennard-Stone method, followed by standardization. Stacking ensemble learning models were employed, using Partial Least Squares (PLS), Support Vector Regression (SVR), and Ridge as base models, with linear regression as the meta-model. The models were evaluated using R2, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Ratio of Performance Deviation (RPD) metrics. The performance gap between soil types suggests the influence of pedological characteristics. Furthermore, the models achieved an RPD > 2.0 with low overfitting, validating the potential of this approach for rapid C and N quantification. This study contributes to the optimization of sustainable agricultural practices, aligning with the demand for efficient and environmentally friendly analytical methods. The developed technique enables faster decision-making for producers and consultants based on organic matter content, fertility indicators, and nutrient availability.