Rational management of CO_(2) can improve the net photosynthetic rate of plants,thereby improving crop yield and quality.In order to precisely manage CO_(2) in a greenhouse,a wireless sensor network(WSN)system was dev...Rational management of CO_(2) can improve the net photosynthetic rate of plants,thereby improving crop yield and quality.In order to precisely manage CO_(2) in a greenhouse,a wireless sensor network(WSN)system was developed to monitor greenhouse environmental parameters in real time,including air temperature,humidity,CO_(2) concentration,soil temperature,soil moisture,and light intensity.The WSN system includes several sensor nodes,a gateway node,and remote management software.The sensor nodes can collect 0-5 V and 4-20 mA analog signals and universal asynchronous receiver/transmitter(UART)data.The gateway node can process and transmit the data and commands between sensor nodes and remote management software.The remote management software provides a friendly interface between user and machine.Users can inquire about real-time data,and set the parameters of the WSN.The photosynthetic rate of tomato plants were studied in the flowering stage.A LI-6400XT portable photosynthesis analyzer was used to measure the photosynthetic rates of the tomato plants,and the environmental parameters of leaves were controlled according to the presetting rule.The photosynthetic rate prediction model of a single leaf was established based on a back propagation neural network(BPNN).The environmental parameters were used as input neurons after being processed by principal component analysis(PCA),and the photosynthetic rate was taken as the output neuron.The performance of the prediction model was evaluated,and the results showed that the correlation coefficient between the simulated and observed data sets was 0.9899,and root-mean-square error(RMSE)was 1.4686.Furthermore,when different CO_(2) concentrations were selected as the input to predict the photosynthetic rate,the simulated and observed data showed the same trend.According to the above analysis,it was concluded that the model can be used for quantitative regulation of CO_(2) for tomato plants in greenhouses.展开更多
基金The authors acknowledge that this work was supported by the National Natural Science Fund(Grant No.31271619)the Doctoral Program of Higher Education of China(Grant No.20110008130006).
文摘Rational management of CO_(2) can improve the net photosynthetic rate of plants,thereby improving crop yield and quality.In order to precisely manage CO_(2) in a greenhouse,a wireless sensor network(WSN)system was developed to monitor greenhouse environmental parameters in real time,including air temperature,humidity,CO_(2) concentration,soil temperature,soil moisture,and light intensity.The WSN system includes several sensor nodes,a gateway node,and remote management software.The sensor nodes can collect 0-5 V and 4-20 mA analog signals and universal asynchronous receiver/transmitter(UART)data.The gateway node can process and transmit the data and commands between sensor nodes and remote management software.The remote management software provides a friendly interface between user and machine.Users can inquire about real-time data,and set the parameters of the WSN.The photosynthetic rate of tomato plants were studied in the flowering stage.A LI-6400XT portable photosynthesis analyzer was used to measure the photosynthetic rates of the tomato plants,and the environmental parameters of leaves were controlled according to the presetting rule.The photosynthetic rate prediction model of a single leaf was established based on a back propagation neural network(BPNN).The environmental parameters were used as input neurons after being processed by principal component analysis(PCA),and the photosynthetic rate was taken as the output neuron.The performance of the prediction model was evaluated,and the results showed that the correlation coefficient between the simulated and observed data sets was 0.9899,and root-mean-square error(RMSE)was 1.4686.Furthermore,when different CO_(2) concentrations were selected as the input to predict the photosynthetic rate,the simulated and observed data showed the same trend.According to the above analysis,it was concluded that the model can be used for quantitative regulation of CO_(2) for tomato plants in greenhouses.
