Comparison of photosynthesis prediction methods with BPNN and PLSR in different growth stages of tomato

To add the appropriate amount of CO₂ based on plant requirements, single-leaf photosynthesis was studied under interaction influence of other environmental factors.Two types of methods were employed to predict the photosynthetic rate in different growth stage of tomato, namely, a back-propagation neural network(BPNN) and partial least squares regression(PLSR).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.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 prediction results were compared by evaluation indices.The 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.