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微气候控制的智能生长柜用于高通量植物表型化.
Michael Vernon1,2, Ghazanfar Abbas Khan2,3, Lawrence D Webb2,3
1School of Engineering, Deakin University, Geelong, VIC 3216, Australia.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
一个新的微气候控制的智能生长柜 (MCSGC) 平台可实现动态气候模拟用于植物表型. 这种具有成本效益,可扩展的系统支持人工智能驱动的作物改进,以实现气候适应性.
科学领域:
- 植物科学 植物科学
- 农业技术 农业技术
- 气候变化研究 气候变化研究
背景情况:
- 气候变化需要适应极端条件的作物品种.
- 在受控的动态环境下进行系统的植物表型鉴定对于识别弹性作物至关重要.
- 现有的增长柜 (GC) 受到成本,静态环境和可扩展性的限制,阻碍了大规模的气候变化研究.
研究的目的:
- 推出一种新的微气候控制智能增长柜 (MCSGC) 平台.
- 解决当前增长柜在成本,可扩展性和环境活力的局限性.
- 为了促进气候适应性研究的先进植物表型.
主要方法:
- 开发一个模块化,具有成本效益 (<10,000澳元) 的MCSGC平台.
- 实现可编程的环境"食谱",用于动态微气候模拟.
- 集成相互连接,可扩展的多柜系统,用于并行实验.
- 自动数据收集和同步,用于人工智能驱动的分析.
主要成果:
- 实现了精确的气候控制,在动态模拟过程中97.42%的数据在±2°C范围内.
- 在各种作物中表现出适应性,包括*Cannabis sativa*,*Beta vulgaris*和*Lactuca sativa*.
- 在没有人工干预的情况下,每次实验产生456张图像和164,160个传感器读数的高通量数据生成.
- 验证适用于广泛作物种植的环境参数 (14.6-31.04 °C,0-1241 μmol·m-2 PAR).
结论:
- 该MCSGC平台克服了现有的增长柜的关键局限性.
- 它可以进行经济高效的,大规模的,动态的气候模拟,用于植物表型.
- 该系统支持农作物改进,人工智能应用和气候弹性研究的进步.


