摘要
提出一种融合水分利用率(Water use efficiency,WUE)和光合速率的温室作物需水模型构建方法。在获取不同温度、光量子通量密度、CO2浓度和土壤含水率嵌套条件下番茄净光合速率和WUE的基础上,基于径向基神经网络(Radial basis function,RBF)构建光合速率和WUE预测模型;继而获取不同环境嵌套条件下的光合速率对土壤含水率的响应曲线,利用U弦长曲率法获取光合速率约束下的土壤含水率调控适宜区间;在此区间内,基于WUE预测模型,以水分利用率最大为目标,利用粒子群算法(Particle swarm optimization,PSO)获取土壤含水率调控目标值;最后,利用支持向量机回归算法(Support vector regression,SVR)建立作物需水模型。结果表明,需水模型的训练精度为0.9969,测试精度为0.9788,均方根误差为0.23%,拟合效果良好。与单一考虑光合效率最优的模型相比,本模型WUE平均提高15.22%,土壤含水率平均下降12.76%,光合速率平均下降4.05%。说明融合WUE光合速率的需水模型能兼顾作物需求和经济效益,可为温室土壤含水率的精准调控提供理论依据。
A method for building a water demand model that considered both water use efficiency(WUE)and photosynthetic rate for greenhouse crops was proposed.Firstly,a nested experiment was performed to measure the photosynthetic rate and WUE of tomato under different combinations of temperatures,photosynthetic photon flux densities(PPFD),CO2 concentrations and soil moisture.Secondly,the photosynthetic rate prediction model and WUE prediction model were established by using the radial basis function(RBF)algorithm.On this basis,the response curve of photosynthetic rate to soil moisture was obtained.Then,the optimal soil moisture ranges under certain environmental conditions were found by applying the U-chord discrete curvature algorithm and particle swarm optimization(PSO)algorithm.At last,the water demand model was established based on the support vector machine regression(SVR)algorithm.The results showed that the model was of high accuracy,with determination coefficient of 0.9969,and mean square error of 0.23%.Compared with the water demand model that only considered photosynthetic rate,this model increased the WUE by 15.22%on average,while the soil moisture and the photosynthetic rate were decreased by 12.76%and 4.05%on average,respectively.These results proved that the crop water demand model proposed can take good account of both crop demand and agricultural water consumption,and provide a theoretical basis for the dynamic and efficient soil moisture regulation of greenhouse crops.
作者
胡瑾
隆星月
邓一飞
完香蓓
李斌
吴华瑞
HU Jin;LONG Xingyue;DENG Yifei;WAN Xiangbei;LI Bin;WU Huarui(College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China;Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling,Shaanxi 712100,China;National Agricultural Information Technology Research Center,Beijing 100097,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2020年第10期362-370,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(31671587)
陕西省重点研发计划项目(2018TSCXL-NY-05-02)
国家大宗蔬菜产业技术体系岗位专家项目(CARS-23-C06)
北京市科技计划项目(Z191100004019007)
西安市科技计划项目(201806117YF05NC13(4))
国家级大学生创新创业训练计划项目(201910712103)。
关键词
温室
作物需水模型
水分利用率
光合速率
土壤含水率
greenhouse
water demand model
water use efficiency
photosynthetic rate
soil moisture
