In order to improve the efficiency of CO2 fertilizer and promote high quality and yield,it is necessary to precisely control CO2 fertilizer by wireless sensor network based on a model of photosynthetic rate prediction in greenhouse.An experiment was carried out on tomato plants in greenhouse for photosynthetic rate prediction modeling combined rough set and BP neural network.In data acquiring phase,plants growth information and greenhouse environmental information that may have influences on photosynthetic rate,including plant height,stem diameter,the number of leaves and chlorophyll content of functional leaves,air temperature,air humidity,light intensity,CO2 concentration and soil moisture,which were measured.And LI-6400XT photosynthetic rate instrument was used for obtaining net photosynthetic rate of functional leaf.After preliminary processing,135 sets of data were obtained.And twelve of them were used for model test of neural network,while the others were used for modeling.All of the data were normalized before modeling.Two models were built to predict photosynthetic rate based on BP neural network.One had total nine input parameters.The other had six input parameters,chlorophyll content,air temperature,air humidity,light intensity,CO2 concentration,and soil moisture,which were reducted from original nine based on attributes reduction theory of rough set.Both two models have one output parameter,the net photosynthetic rate of single leaf.The genetic algorithm was adopted to reduct attributes.Since continuous data cannot be processed by rough set,the K-mean cluster method was used to discretize the data of nine input parameters before attributes reduction.The prediction results of two models showed that the model with six input parameters had a mean absolute error of 0.6958,an average relative error of 7.28%,a root-mean-square error of 0.7428,and a correlation coefficient of 0.9964,while the other model respectively had 0.4026,4.53%,0.3245 and 0.9965,which proved that the model with minimum attributes had hi
Ji YuhanJiang YiqiongLi TingZhang ManSha ShaLi Minzan
CO_(2)concentration is an environmental factor affecting photosynthesis and consequently the yield and quality of tomatoes.In this study,a photosynthesis prediction model for the entire growth stage of tomatoes was constructed to elevate CO_(2)level on the basis of crop requirements and to evaluate the effect of CO_(2)elevation on leaf photosynthesis.The effect of CO_(2)enrichment on tomato photosynthesis was investigated using two CO_(2)enrichment treatments at the entire growth stage.A wireless sensor network-based environmental monitoring system was used for the real-time monitoring of environmental factors,and the LI-6400XT portable photosynthesis system was used to measure the net photosynthetic rate of tomato leaf.As input variables for the model,environmental factors were uniformly preprocessed using independent component analysis.Moreover,the photosynthesis prediction model for the entire growth stage was established on the basis of the support vector machine(SVM)model.Improved particle swarm optimization(PSO)was also used to search for the best parameters c and g of SVM.Furthermore,the relationship between CO_(2)concentration and photosynthetic rate under varying light intensities was predicted using the established model,which can determine CO_(2)saturation points at the various growth stages.The determination coefficients between the simulated and observed data sets for the three growth stages were 0.96,0.96,and 0.94 with the improved PSO-SVM and 0.89,0.87,and 0.86 with the original PSO-SVM.The results indicate that the improved PSO-SVM exhibits a high prediction accuracy.The study provides a basis for the precise regulation of CO_(2)enrichment in greenhouses.
