中国农业机械化科学研究院集团有限公司 主管

北京卓众出版有限公司 主办

叶面饱和水汽压差驱动的日光温室智能环控系统优化与验证

Optimization and validation of solar greenhouse intelligent environmental control system driven by leaf vapor pressure deficit

  • 摘要: 为解决传统温室环境调控系统滞后性强、参数波动大等问题,提出一种基于叶面饱和水汽压差(Leaf VPD)的智能环控系统。构建Leaf VPD与温室环境参数的动态耦合模型,结合模糊控制理论与物联网技术,实现从环境参数阈值控制向作物生理状态优化的转变,并采用室外累积温度4阶段模型与多设备协同策略提升调控效率。试验结果表明,该系统在温湿度控制方面表现优异:日间温度与湿度达标率分别达到92%和89%,温湿度波动范围控制在±1.2 °C和±8个百分点;作物生长方面,番茄平均株高158 cm,坐果率达95%,病害发生率仅3%,显著优于传统人工调控温室。该系统有效提升环境稳定性与作物生理性能,可为设施农业的智能化发展提供技术支撑。

     

    Abstract: To address issues such as strong hysteresis and significant parameter fluctuations in traditional greenhouse environmental control systems, an intelligent environmental control system based on leaf vapor pressure deficit(Leaf VPD)was proposed.A dynamic coupling model between Leaf VPD and greenhouse environmental parameters was established, integrating fuzzy control theory with IoT technology to transition from threshold-based environmental parameter control to crop physiological state optimization.An outdoor cumulative temperature four-stage model and a multi-device coordination strategy were adopted to improve regulation efficiency.Experimental results demonstrated system's outstanding performance in temperature and humidity control.Daytime temperature and humidity compliance rates reached 92% and 89%, respectively, with fluctuations controlled within ±1.2 °C and ±8 percentage points.Regarding crop growth, tomatoes achieved an average plant height of 158 cm, a fruit setting rate of 95%, and a disease incidence rate of only 3%, significantly outperforming traditional manually controlled greenhouses.This system effectively enhanced environmental stability and crop physiological performance, offering a reliable technical support for facility agriculture intelligent development.

     

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