人工智能和机器学习用于土壤分析:对可持续农业实践的评估
Muhammad Awais1,2, Syed Muhammad Zaigham Abbas Naqvi1,2, Hao Zhang1,2
1College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China.
Bioresources and bioprocessing
|April 22, 2024
概括
人工智能 (AI),深度学习 (DL) 和机器学习 (ML) 为高效的资源管理提供了强大的,准确的土壤分析. 这些先进的方法克服了传统土壤水含量和质地统计的局限性,使得农业决策更好.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 可持续农业需要有效地管理自然资源.
- 全球气候和土地设计导致土壤属性的显著变化,例如土壤水含量 (SWC) 和质地.
- 传统的统计分析很慢,可能会推迟土壤分析中的关键决策.
研究的目的:
- 审查人工智能,DL和ML的应用,以进行强大和快速的土壤分析.
- 为了提高对SWC和土壤纹理的预测建模的理解.
- 将人工智能驱动的数据处理与传统的统计方法进行比较.
主要方法:
- 对用于预测建模的机器学习算法 (随机森林,支持矢量机器,神经网络) 的审查.
- 整合地理统计技术 (kriging,co-kriging) 用于空间数据的插值.
- 评估人工智能在处理地理空间非数字数据方面的优势.
主要成果:
- 人工智能,DL和ML平台提供强大,准确和快速的土壤分析,特别是SWC和纹理.
- 机器学习模型可以使用土壤数据和环境变量预测土壤特性.
- 人工智能处理比一般的统计分析具有优势,产生无噪声的结果.
结论:
- 传统的统计工具对于现代的,人体工程学的土地管理是不够的.
- 人工智能非常适合处理广泛的地理空间非数字数据进行土壤分析.
- 人工智能具有开发全球SWC数据库和改进智能灌的潜力.
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