Greenhouse crop photosynthetic rate prediction model construction method and system

Abstract

The invention discloses a greenhouse crop photosynthetic rate prediction model construction method and a system, and the method comprises the steps: firstly obtaining sample data, and carrying out the normalization operation of the sample data, and obtaining a training sample set; then, constructing a photosynthetic rate prediction model based on SOPSO-LSSVM, training the photosynthetic rate prediction model through the training sample set, wherein the training process comprises the steps that the training sample set is input into the LSSVM model for training, and a predicted value of the photosynthetic rate in the current training batch is obtained; according to the predicted value, obtaining a parameter optimal value of the current training batch through an SOPSO algorithm, and updating parameters in the LSSVM model by using the parameter optimal value; and finally, when the training batch reaches a preset condition, obtaining a greenhouse crop photosynthetic rate prediction model. It can be seen that the model can accurately predict the photosynthetic rate under different temperatures, CO2 concentrations and PPFD conditions, a good model basis can be provided for greenhouse environment optimization control, and compared with an existing method, the method has higher prediction precision and robustness.

Description

technical field [0001] The invention relates to the field of biological information, in particular to a method and system for constructing a greenhouse crop photosynthetic rate prediction model. Background technique [0002] Crop photosynthesis refers to the biochemical process of converting carbon dioxide and water into organic matter under a certain photon flux density to achieve material accumulation, which determines the yield and quality of crops. Therefore, creating a good microclimate environment to meet the photosynthetic needs of crops is the key to increasing photosynthetic rate, accelerating material accumulation, and improving crop yield and quality. [0003] In the prior art, support vector machine (SVM) technology is usually used to simulate the relationship between these environmental factors and the photosynthetic rate. However, when the number of input variables increases, the prior art has disadvantages such as high fitting complexity and large errors. . ...

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Problems solved by technology

[0003] In the prior art, support vector machine (SVM) technology is usually used to simulate the relationship between these environmental factors and the photosynthetic rate. However, when the number of input variables increases, the prior art has disadvantages such as high fitting complexity and large errors.

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