Carbon dioxide (CO2), as an important nutrient for plant growth in greenhouses, is one of the main factors that affect photosynthesis.To add the appropriate amount of CO2 based on plant requirements, single-leaf photosynthesis was studied under different artificial environments by regulating and controlling CO2 concentration.A series of experiments on predicting the photosynthetic rate of tomato plants was performed in a greenhouse based on environmental information.A wireless sensor network system was developed to monitor environmental information automatically, including CO2 concentration, photosynthetically active radiation, air temperature, and relative humidity.An LI-6400XT photosynthetic rate instrument was used to obtain the net photosynthetic rate of a functional leaf.Two types of methods were employed to predict the photosynthetic rate, namely, a back-propagation neural network (BPNN) and partial least squares regression (PLSR).Before building the prediction models, input data should be preprocessed to improve accuracy.The preprocessing procedure comprised two steps.First,singular points were deleted by analyzing the variable box plot.The data were then normalized.After comprehensively considering CO2 concentration in the greenhouse, the prediction models of tomato single-leaf photosynthesis were established via PLSR and BPNN during different growing stages.The relationship between CO2 concentration and the photosynthetic rate was analyzed using the aforementioned two models.The prediction results were compared by evaluation indices.Analyzed results showed that the correlation coefficients between the simulated and observed data sets were 0.94, 0.96, and 0.97 in the three growing stages using BPNN, and 0.74, 0.88, and 0.85 using PLSR.The results proved that the BPNN model exhibited higher prediction accuracy than the PLSR model and could be used to control CO2 air fertilizer precisely in a greenhouse.