Optimal Regulation Of CO₂ Concentration In Greenhouse Based On Wireless Sensor Network

Carbon dioxide (CO2) as the important raw materials of plant growth in greenhouse, it is also one of the main factors that infects the plant photosynthesis. It is of great significance to determine the demand of the optimum CO2 concentration for different plant growth stages. Tomato crop was selected as the research object, the net photosynthetic rate prediction model of tomato single leaf was built in different enrichment of CO2 gas fertilizer, and CO2 optimization control model was established in different tomato growth stages. Canopy photosynthetic rate measurement and analysis methods were studied. CO2 concentration control management platform was designed and developed.In order to analyze the effect of CO2 enrichment on the growth and development of tomato leaf photosynthetic rate and other growth indexes, the experiment of different concentration CO2 enrichment was conducted. The results showed that CO2 enrichment raised the net photosynthetic rate significantly and increased crop biomass index. The environmental parameters affecting the growth of tomato, including CO2 concentration, photosynthetic photon flux density (PPFD), air temperature, relative humidity, were monitored in real time by wireless sensor nodes as the input variables, and the net photosynthetic rate of tomato was measured by LI-6400XT as the output variable, the BP neural network optimized by genetic algorithm was used to establish the photosynthetic rate prediction model. The diurnal dynamics of photosynthesis under different CO2 concentration levels was simulated based on established model and the CO2 concentration saturation point was obtained. The simulation results indicated that the predicted values consistent with measured value, and relative error is smaller. Therefore, the prediction model had a certain universality, which could provide a theoretical basis for performance prediction of photosynthetic diurnal change in greenhouse.To explore CO2 concentration optimization control model under different tomato growth stages in greenhouse, the experiments of CO2 enrichment in different growth stages were carried out. The environmental information was real-time monitored by wireless sensor nodes and the crop growth indexes were obtained manually on a regular basis. The SVM model was used to establish the photosynthetic rate model in the late seedling stage, the full flowering stage and the early fruiting stage of tomato, respectively. Rough set theory was used to analyze the correlation and dependence between datasets and reduce data redundancy before modeling. To improve the accuracy of the photosynthesis prediction model based on SVM, improved particle swarm optimization was designed to optimize the parameters of the SVM model. The optimal CO2 concentration under different environmental conditions was obtained by the model, and an optimization control model of CO2 concentration was built by PLS according to the corresponding environmental conditions. The prediction model had a high precision, which could provide a theoretical basis for the precisely control CO2 gas fertilizer and optimal regulation of photosynthetic rate of tomato plants in greenhouse.Assimilation box device was designed for measuring the tomato crop canopy photosynthetic rate. The device was subjected to the leak detection. The result shown that air leakage rate of the divice was 0.12μmol-(mol-min)-1, the equipment has a good air tightness. Crop canopy photosynthetic rate was measured by the assimilation box. The result indicated that elevated CO2 concentration could increase leaf area of plants and canopy photosynthetic rate can be increased by 34%-46%. Therefore, the CO2 gas fertilizer increasing short-term is helpful to improve crop photosynthetic rate.The remote management platform could provide a friendly interface for users, could store and display real time CO2 concentration in greenhouse, could automatically control CO2 concentration, and also could set WSN parameters. The photosynthetic rate prediction model or CO2 concentration optimization control model had stability and high precision were used in the platform to achieve the intelligent decision of CO2 concentration.