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提高土壤有机碳估计与生成AI和尼克斯颜色传感器

Rachna Singh¹, Mriganka De², Rintu Banerjee¹

¹Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India.

Scientific reports

|November 18, 2025

概括

本研究介绍了一种快速,负担得起的方法,用于测量土壤有机碳 (SOC),使用色彩传感器和人工智能 (AI). 人工智能生成的合成数据显著提高了土壤健康评估的预测准确性.

科学领域:

土壤科学土壤科学
农业技术农业技术
数据科学数据科学数据科学

背景情况:

土壤有机碳 (SOC) 对土壤健康至关重要,但很难通过传统方法进行测量.
传统的土壤分析是劳动密集型和昂贵的,阻碍了广泛的土壤健康监测.

研究的目的:

通过手持色彩传感器和数据驱动模型,研究一种快速,低成本的SOC预测替代方案.
通过使用合成数据增强技术来提高预测准确性,特别是对于代表性不足的SOC范围.

主要方法:

使用Nix Spectro 2色彩传感器从641个样本中捕获土壤色彩数据.
使用随机森林 (RF),梯度增强回归 (GBR),极端梯度增强 (XGBoost) 和人工神经网络 (ANN) 预测引擎.
应用生成型人工智能 (AI) 技术 (GAN,GMM) 和统计方法 (KNN,引导) 用于合成数据增强.

主要成果:

基线随机森林模型实现了R2 = 0.71.
通过增加44个GMM生成样本的校准集,将射频性能提高到R2 = 0.77,减少偏差并改善SOC分布覆盖范围.
合成数据集成增强了模型的概括性和预测准确性,减轻了数据集之间的差异差异问题.

结论:

将Nix色彩传感器与人工智能驱动的合成数据增强集成,为现场土壤有机碳评估提供了快速,经济高效的解决方案.
这种方法显示了精准农业和可持续土壤管理的巨大潜力.
未来的工作应该将这些人工智能驱动的方法扩展到多参数土壤分析和数字土壤绘图.

关键词:

人工智能的人工智能是人工智能.转基因转基因转基因转基因尼克斯的颜色传感器随机的森林随机的森林土壤的颜色土壤的颜色土壤有机碳土壤有机碳

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