摘要
叶水势是作物水分状况的最佳度量,是灌溉决策的重要依据。依据Penman-Monteith蒸腾算式计算或依据遥感数据反演的方法因机理算式复杂、待定参数多、可移植性差、测量成本高等原因,难以推广应用。因此,选取易于获取的作物微环境因子作为辅助变量,建立了基于RBF网络的夏玉米叶水势软测量模型,并进行了仿真研究。仿真结果表明,该方法简单实用,估算精度较高,是一种在线估算田间作物水分状况的有效措施。
Leaf water potential is the best parameter of estimating plant water status and is the important basis for irrigating decision.Evaluation from Penman-Monteith transpiration formula or retrieval from remote sensing data has complex calculations,too many parameters,poor transplantations,high costs and too many difficulties to widen it.This paper selects accessible micro-environment factors of plant as auxiliary variables,and establishes a leaf water potential soft-sensing model with RBF neural network.Simulation result shows that this model is simple and practical,and has higher accuracy.It is one of effective methods estimating field plant water status on line.
出处
《节水灌溉》
北大核心
2010年第9期19-23,共5页
Water Saving Irrigation
基金
国家自然科学基金资助项目(60772167)
关键词
SPAC
叶水势
环境因子
精准灌溉
软测量
RBF网络
SPAC
leaf water potential
environment factors
precision irrigation
soft-sensing
RBF network
