单 位：
中国农业大学现代精细农业系统集成研究教育部重点实验室
关键词：
番茄;温室;无线传感器网络;光合速率预测模型;CO2增施;土壤水分
摘 要：
为了实现不同土壤水分管理下的CO_2气肥精细控制,建立了番茄作物不同生长阶段的光合速率预测模型。实验设置了4个CO_2浓度与3个土壤水分条件的交互处理,利用无线传感器网络长期实时监测温室内环境信息,采用LI-6400XT型光合速率仪定时采集作物净光合速率信息;并用BP神经网络分别建立了番茄苗期、花期和果期的光合速率预测模型。预测模型的验证结果表明,对于苗期预测模型,预测值与实测值之间的决定系数R2为0.925;花期预测模型的决定系数R2为0.920,果期预测模型的决定系数R2为0.958;番茄各生长期的光合速率预测模型均具有较高的预测精度。在不同土壤水分条件下改变CO_2浓度,得到的CO_2浓度与光合速率预测曲线与实测值相近,可反映实际土壤水分管理下的CO_2浓度最优值,对指导不同土壤水分条件下CO_2气肥的精细调控具有重要意义。
译 名：
Tomato Photosynthetic Rate Prediction Models under Interaction of CO_2 Enrichments and Soil Moistures
作 者：
Li Ting;Ji Yuhan;Zhang Man;Sha Sha;Jiang Yiqiong;Key Laboratory of Modern Precision Agriculture System Integration Research,Ministry of Education,China Agricultural University;
关键词：
Tomato;;Greenhouse;;Wireless sensor network;;Photosynthetic rate prediction model;;CO2 enrichment;;Soil moisture
摘 要：
Photosynthesis is the basis of crop growth and metabolism. CO_2 concentration and soil moisture are the important environmental factors affecting plant's photosynthetic rate under controlled temperature and light intensity in greenhouse. To effectively evaluate the effect on plant's photosynthesis,reasonably elevating CO_2 concentration under different soil moisture conditions is of great significance to achieve precision regulation of CO_2 concentration. To achieve the requirements,the photosynthetic rate prediction models based on back-propagation( BP) neural network were proposed at different growth stages of tomato plants. The two-factors interaction experiment was designed,in which the CO_2 concentration was set to four different levels(( 700 ± 50)( C1),( 1 000 ± 50)( C2),( 1 300 ± 50) μmol / mol( C3),and ambient CO_2 concentration in greenhouse( 450 μmol / mol,CK)) combined with three different soil moisture levels( 35% ~ 45%( low),55% ~ 65%( moderate),75% ~ 85% of saturated water content( high)). The sensor nodes of WSN were used to realize the real-time monitoring of greenhouse environmental factors,including air temperature and humidity,light intensity,CO_2 concentration and soil moisture. An LI-6400 XT photosynthesis analyzer was used to measure net photosynthetic rate of tomato leaf. The environmental factors were used as input variables of models after processed by normalization,and the photosynthetic rate was taken as the output variable. The model verification test was conducted by comparing and analyzing the observed values and predicted data. The results indicated that the trainingdetermination coefficient( R2) of photosynthesis prediction model was 0. 953,and root mean square error( RMSE) was 1. 019 μmol/( m~2·s); testing R2 of the model was 0. 925,RMSE was 1. 224 μmol /( m~2·s)at seedling stage. At flowering stage, the training R2 of the model was 0. 958 and RMSE was0. 939 μmol /( m~2·s); testing R2 of the model was 0. 920 and RMSE was 1. 276 μmol /( m~2·s). At fruiting stage,the training R2 of the model was 0. 980 and RMSE was 0. 439 μmol /( m~2·s); testing R2 of the model was 0. 958 and RMSE was 0. 722 μmol /( m2·s). It was concluded that the model based on BP neural network reached high accuracy. Furthermore,the relationship between CO_2 concentration and photosynthetic rate was described by the established BP neural network model aiming at CO_2 saturation points under different soil moisture conditions at different growth stages. The observed and predicted results showed the same trend. The results can provide a theoretical basis for quantitative regulation of CO_2 enrichments to tomato plants in greenhouse.
