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一种基于DBO-SVR方法的近红外光谱分析策略,用于检测土壤营养素

Kangyuan Zhong¹, Yane Li¹, Weiwei Huan²

¹College of Mathematics and Computer science, Zhejiang A&F University, Hangzhou, 311300, China; Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou, 311300, China; Key Laboratory of Forestry Perception Technology and Intelligent Equipment State Forestry Administration, Hangzhou, 311300, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|April 18, 2024

概括

这项研究引入了一个新的模型,将近红外光谱学与虫优化算法和支向量机 (DBO-SVR) 结合起来,用于准确的土壤营养分析. DBO-SVR模型显著改善了对土壤pH值,,,和有机物质含量的预测.

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:

传统的土壤分析方法昂贵且污染.
光谱分析为土壤性质预测提供了一个快速,非破坏性的替代方案.
由于优化算法和机器学习的有限集成,现有的模型往往缺乏最佳准确性和融合速度.

研究的目的:

开发和评估一种用于预测土壤营养成分的新型评估模型.
通过将先进的优化算法与机器学习相结合,提高模型准确度和融合速度.
将拟议的虫优化算法 (DBO) 与其他主流优化算法的性能进行比较.

主要方法:

从中国江省收集了184个土壤样本.
将9种预处理方法及其组合应用于光谱数据.
开发了一种虫优化算法支持矢量机器 (DBO-SVR) 模型,用于预测土壤pH值,性水解 (SAN),可用的 (SAP),可用的 (SAK) 和土壤有机物质 (SOM).

主要成果:

对于所有测试的土壤参数,DBO-SVR模型显示了高预测准确度.
获得的R_p值为pH的0.9842 ,SAN的0.8802 ,SAP的0.9790 ,SAK的0.8677和SOM的0.9273 .
在模型性能方面,DBO算法超过了粒子群优化 (PSO),鱼优化算法 (WOA) 和灰狼优化器 (GWO).

结论:

接近红外 (NIR) 光谱与DBO-SVR算法的集成为土壤营养预测提供了一个高度准确和高效的方法.
这种方法克服了传统方法的局限性,并改进了现有的光谱和机器学习模型.
DBO-SVR模型显示了推进精准农业和可持续土壤管理的巨大潜力.

关键词:

在DBO-SVR中使用.近红外光谱学近红外光谱学预测模型的预测模型.土壤元素土壤元素

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