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混合机器学习和基于物理的模型,用于估计 (Lactuca sativa) 的生长和航空系统中的资源消耗
Benedetta Fasciolo1, Nicolò Grasso2, Giulia Bruno2
1Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy. benedetta.fasciolo@polito.it.
Scientific reports
|July 2, 2025
概括
这项研究开发了一种用于飞机生长的混合模型,有效预测新鲜重量和叶面积. 这种方法提高了受控环境农业中的资源管理和产品质量.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 全球人口增长需要优化农业生产和资源管理.
- 气候变化和环境退化对作物生产率和粮食安全构成重大威胁.
- 传统农业在营养/水资源管理方面面临挑战,导致废物和安全问题.
研究的目的:
- 开发一种混合模型,将机器学习和基于物理的技术结合起来.
- 预测机器培养的的主要生长参数 (新鲜重量,叶面积,酸盐,水消耗).
- 加强资源管理和产品质量在受控环境农业.
主要方法:
- 基于物理的模型与机器学习算法的集成.
- 开发用于预测建模的动态混合框架.
- 使用飞机式培养系统的实时数据进行验证.
主要成果:
- 混合模型表现出新鲜重量和总叶面积的良好预测性能.
- 预测酸盐含量和水消耗的准确性较低.
- 在无土壤条件下较小的数据集和物理模型的局限性导致了预测变化.
结论:
- 混合模型为优化受控环境农业提供了一个有希望的解决方案.
- 这种方法通过提高效率和可持续性来解决现代农业面临的关键挑战.
- 为了更准确地预测营养和水动态,需要进一步的细化.
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