Cucumber whole-course photosynthetic rate prediction model based on neural network, and establishment method

Abstract

The invention discloses a cucumber whole-course photosynthetic rate prediction model based on a neural network. A multifactor nesting experiment is utilized for obtaining cucumber seedling photosynthetic rate test data, an LM (Levenberg-Marquardt) training method is adopted to carry out model training, and a cucumber whole-course photosynthetic rate model which combines a growing stage is established and is subjected to model performance parameter comparison and accuracy verification with a photosynthetic rate model of a single growing period and a whole-course photosynthetic rate model which does not combines a growing period stage parameter. A training result indicates that the whole-course photosynthetic rate model established in a way that the growing period is added to serve as a one-dimensional input quantity can effectively pass over local flat areas, and the whole-course photosynthetic rate model has an obvious superiority, meets a training requirement that errors are smaller than 0.0001, and is verified in an xor checkout way, so that a determination coefficient of a model prediction value and an actual measurement value is 0.9897, an error is smaller than 6.559%, and a theoretical basis and technical support can be provided for facility and crop luminous environment regulation and control.

Description

technical field [0001] The invention belongs to the technical field of intelligent agriculture, and in particular relates to a neural network-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 concentration di...

AI Technical Summary

Problems solved by technology

Recently, the photosynthetic rate model based on Hopfield network, the stomatal conductance model of greenhouse tomato leaves based on BP neural network, and the net photosynthetic rate prediction model of single leaf in tomato flowering period based on WSN have appeared. The above studies have applied neural networks to photosynthetic rate from different angles. Modeling, but the impact of different growth periods on crops has not been considered, and a full-range cucumber photosynthetic rate prediction model has not been established, and there are shortcomings in the slow training process and large differences in training errors

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