摘要
目前,温室CO2施肥主要采用试验定性分析确定适合范围,难以实现高精度温室产业生产控制。根据光合作用对温室环境因子的非线性,结合BP神经网络对非线性的良好辨识能力,研究出一种CO2施肥技术。结合温室光照、CO2浓度变化规律以及温室生菜生长规律,运用BP神经网络建立温室生菜光合速率与二者的量化模型,预测出在不同温室环境条件下,通过生菜的光合作用速率来衡量生菜生长状况,在温室小气候条件下实现对生菜产量的量化控制。
Presently the main method of greenhouse CO2 fertilization is experimentations used for determining the scope by qualitative analysis, which is difficult to achieve high - precision control of the production of greenhouse industry. According to the nonlinear of greenhouse photosynthesis to environmental factors, combined with BP network＇ s excellent ability identifying non -linear models, a new co2 fertilization technique was brought forward. Combining the diversification rule of greenhouse illumination and CO2 concentrations, as well as the growth of greenhouse lettuce, using BP neural network, a quantitative model between greenhouse lettuce photosynthetic rate and the two environment factors was built. That can predict lettuce photosynthetic rates with different environmental conditions to measure the growth rate of greenhouse lettuce, accordingly to realize the control of the production of lettuce quantitative in the microclimate.
出处
《农机化研究》
北大核心
2008年第12期17-20,共4页
Journal of Agricultural Mechanization Research
基金
国家"863"计划项目(2006AA10Z258)
关键词
生菜
光合作用
BP神经网络
CO2
控制
lettuce
photosynthesis
back prorogation neural network
carbon dioxide
adjustment
