对作物应激检测的全面审查:破坏性,非破坏性和基于ML的方法
Aman Muhammad1,2, Zahid Ullah Khan3, Javed Khan4
1College of Agricultural Engineering, Shanxi Agricultural University, Taigu, Jinzhong, China.
Frontiers in plant science
|September 22, 2025
概括
本综述探讨了植物压力因素及其检测. 机器学习与非破坏性方法相结合,为可持续农业提供先进的实时作物压力监测.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 计算生物学 计算生物学
背景情况:
- 农业对社会发展至关重要,并提供必要的资源.
- 保护农业的可持续性对于后代和环境平衡至关重要.
- 植物面临着各种生物和非生物压力因素,影响它们的生理功能.
研究的目的:
- 审查干扰植物和作物的生理功能 (压力因素) 的因素.
- 列出植物应激指标和评估技术.
- 通过机器学习 (ML) 突出植物应激检测方面的进展.
主要方法:
- 对生物和非生物植物压力因素的系统审查.
- 编目生理和生化压力指标.
- 机器学习与非破坏性传感 (超光谱,热成像,叶绿素光) 的整合.
主要成果:
- 确定了主要的植物应激类别和相关指标.
- 通过ML证明了压力检测的增强精度,可扩展性和实时能力.
- 机器学习算法能够早期识别压力特征,并主动减轻产量损失.
结论:
- 将ML与非破坏性方法的整合代表了自动作物监测的重大进步.
- 用ML增强的精准农业支持作物管理的数据驱动决策.
- 某些压力条件可能会对植物的弹性和生产力产生积极影响.
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