Cucumber whole-course photosynthetic rate predicting model based on support vector machine, and establishing method

Abstract

The invention relates to a cucumber whole-course photosynthetic rate predicting model based on a support vector machine. Photosynthetic rate test data of cucumber seedlings is obtained by utilizing a multi-factor nested experiment; model training is carried out by adopting a LM training method; a cucumber whole-course photosynthetic rate model fused with multiple growing periods is established; the cucumber whole-course photosynthetic rate model is subjected to comparison verification with a single-growing-period photosynthetic rate model and a whole-growing-period cucumber photosynthetic rate model respectively by adopting an abnormal check manner; the result shows that the whole-course photosynthetic rate model, which is established by adding the growing period as the one-dimensional input quantity, can effectively cross a local flat region, have obvious superiority, and satisfy training requirements that the error is less than 0.0001; the determination coefficient of a predicted value and a practically measured value of the model is 0.993; the error is less than 6.253%; both the training effect and the model-fitting degree of the model are superior to the mixed-growing period model; the precision of the model is similar to that of the single-growing-period photosynthetic rate model; and thus, the model disclosed by the invention can provide theoretical basis and technical support for light environment adjustment and control of facility crops.

Description

technical field [0001] The invention belongs to the technical field of intelligent agriculture, and in particular relates to a support vector machine-based prediction model and establishment method of the whole-process photosynthetic rate of cucumber. Background technique [0002] Cucumber is one of the main vegetables cultivated in my country. The quality and yield of cucumber are inseparable from its photosynthetic ability. Photosynthetic rate and chlorophyll content, temperature, CO 2 Concentration, light intensity, relative humidity and other factors have a significant relationship. Among them, chloroplast is the basic organelle for photosynthesis of green plants, and chlorophyll is the basic component of chloroplast, which is very important in plant photosynthesis, and its content is an important indicator of plant photosynthesis ability, nutritional status and growth status. Temperature Affect the activity of Rubisco activase, stomatal conductance, CO 2 The concentr...

AI Technical Summary

Problems solved by technology

The complexity and nonlinearity of many factors that cause photosynthetic rate changes determine the nonlinear correlation between predictors and predictors. Therefore, traditional model prediction methods are difficult to solve the prediction problem that is essentially a nonlinear relationship. Rate prediction provides a viable and efficient way to

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