Increasing the photosynthetic rate of crops is one of the reliable methods for high yield breeding. The main method for measuring photosynthesis rate is infrared gas analysis, which owns dependable axiom and mature technology. However, the infrared light source is easily affected by the complex working environment in the field, especially the change in ambient temperature. Therefore, the measurement error is significant in the task of quantitative analysis, and the detection precision of gas with deficient concentration or weak concentration change is not exact. Based on the above questions, first of all, the tunable diode laser absorption spectroscopy (TDLAS) is applied to the measurement of plant photosynthetic rate in this paper, which employs the second harmonic peak difference to represent the relative variation of trace concentration of photosynthetic gas CO₂ in unit sampling time. Secondly, we established an environment compensation model of a broad learning system based on firefly algorithm optimization (FA-BLS). The position information of each firefly in the model corresponds to a set of feasible solutions representing the weights and thresholds of the BLS. Through the continuous iteration and update optimization of firefly position to find the firefly with the highest brightness, that is to generate the weights and thresholds that make the model perform the best. Ultimately, the compensation value generated by FA-BLS is used to compensate for the original second harmonic peak difference with environmental impact, and the net photosynthetic rate per unit sampling time was obtained from the compensated second harmonic peak difference. The experimental results indicate that the firefly population size and the number of nodes in the enhancement layer of BLS are significant considerations affecting the output error of TDLAS-FA-BLS, which commendably inherits the advantages of BLS, such as fast training speed and short iteration time. It is worth mentioning that the average measurement time of FA-BLS is merely 0.81 s, and the chi square distance between model prediction output and test set data is only 0.29×10^-4, which indicates its output error is similarly small. At the same time, the sample variance and sample standard deviation of the output error of FA-BLS are lower than those of BLS, which illustrates that FA-BLS overcomes the shortcomings of BLS, such as unstable network output and low generalization due to random selection of parameters. Consequently, the method based on TDLAS-FA-BLS for the determination of plant net photosynthetic rate can nicely meet the needs of high precision, real-time, stability and reliability in the complex field working environment and actual agricultural production.