人工智能 (AI) 检测植物中的非生物压力:一篇评论
Anushree Matabber1, Lionel Lami-Ndame Rhuhanga1,2, Shinsuke Agehara3
1Department of Vegetable and Mushroom Growing, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
人工智能 (AI) 提供先进的,非侵入性的方法来检测作物中的非生物压力,这对全球粮食安全至关重要. 机器和深度学习算法,结合成像和物联网,提供超越传统技术的精确,可扩展的解决方案.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 气候驱动的非生物压力对全球农业和粮食安全构成重大威胁.
- 检测非生物压力的传统方法是劳动密集型,不精确,缺乏可扩展性.
- 在环境压力下监测作物健康的有效和可靠解决方案非常需要.
研究的目的:
- 审查人工智能 (AI) 的应用,特别是机器和深度学习,用于检测作物中的非生物压力.
- 探索AI如何与成像和物联网等协同技术集成,用于压力检测.
- 将基于人工智能的方法与传统方法进行比较,并确定农业中的采用挑战.
主要方法:
- 对专家评审期刊文章的系统审查,重点关注用于非生物应激检测的AI.
- 基于压力类别,感知模式和AI技术的文献选择.
- 对人工智能驱动的方法与传统技术进行比较分析.
主要成果:
- 人工智能,机器学习和深度学习算法,加上成像和物联网数据,可以识别非生物压力的独特特征.
- 基于人工智能的方法为传统检测方法提供了高度准确,非侵入性和可持续的替代方案.
- 人工智能展示了评估非生物压力对作物生长和生理性能影响的潜力.
结论:
- 人工智能为农业中非生物应激检测提供了一种变革性的方法,增强了作物监测和管理.
- 人工智能与先进的传感技术的整合为传统方法提供了显著的进步.
- 应对与人工智能采用相关的挑战对于实现其在保护全球粮食生产方面的全部潜力至关重要.
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