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对多模式分析进行系统审查,以加强植物应激评估

Abdolrahim Zandi¹, Seyedali Hosseinirad², Hossein Kashani Zadeh³

¹Biomedical Engineering Department, College of Engineering and Mines, University of North Dakota, Grand Forks, ND, United States.

Frontiers in plant science

|May 15, 2025

概括

多模式分析 (MMA) 通过整合光谱成像和机器学习,提供卓越的植物应激检测. 这种方法增强了早期干预策略,提高了作物弹性和农业生产率,以提高粮食安全.

科学领域:

农业科学农业科学
植物生物学植物生物学
数据分析数据分析数据分析.

背景情况:

早期检测植物压力对农业生产力和弹性至关重要.
传统的单模方法难以捕捉复杂的植物健康压力因素.

研究的目的:

审查用于植物应力检测的多模式分析 (MMA) 的进展.
突出MMA对农业传统方法的优势.

主要方法:

整合光谱成像,基于图像的表型化和自适应计算技术.
机器学习,数据融合和超频谱技术的应用.
对MMA用于植物压力分析的最新研究进行审查.

主要成果:

MMA显著提高了早期植物应激检测的准确性和可靠性.
MMA有效地捕捉了非生物压力因素之间的复杂相互作用,优于传统方法.
增强的预测能力促进了及时有效的干预策略.

结论:

在检测和管理植物压力方面,MMA提供了实质性的优势,特别是在具有挑战性的环境中.
先进的分析方法支持精准农业和积极的压力管理.
这些创新对于陆地和太空农业的粮食安全至关重要.

关键词:

- - 超光谱光成像 (HFI) 技术 - - 映射索引的映射索引.- - 压力路径这是一种超谱的超光谱.超光谱反射成像 (HRI) 是一种超光谱反射成像技术.多种方式光谱学.植物压力因子植物压力因子压力模式压力模式

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