Optimization and control of the light environment for greenhouse crop production

Pingping Xin,Haihui Zhang,Bin Li,Jin Hu

doi:10.1038/s41598-019-44980-z

Abstract

Optimization and control of the greenhouse light environment is key to increasing crop yield and quality. However, the light saturation point impacts the efficient use of light. Therefore, the dynamic acquisition of the light saturation point that is influenced by changes in temperature and CO2 concentration is an important challenge for the development of greenhouse light environment control system. In view of this challenge, this paper describes a light environment optimization and control model based on a crop growth model for predicting cucumber photosynthesis. The photosynthetic rate values for different photosynthetic photon flux densities (PPFD), CO2 concentration, and temperature conditions provided to cucumber seedlings were obtained by using an LI-6400XT portable photosynthesis system during multi-factorial experiments. Based on the measured data, photosynthetic rate predictions were determined. Next, a support vector machine(SVM) photosynthetic rate prediction model was used to obtain the light response curve under other temperatures and CO2 conditions. The light saturation point was used to establish the light environment optimization and control model and to perform model validation. The slope of the fitting straight line comparing the measured and predicted light saturation point was 0.99, the intercept was 23.46 and the coefficient of determination was 0.98. The light control model was able to perform dynamic acquisition of the light saturation point and provide a theoretical basis for the efficient and accurate control of the greenhouse light environment.

Highlights

Light provides energy for photosynthesis, and it is one of the most important conditions affecting the growth and development of the external environment crops[1]
The objectives of this study were as follow: (1) establish photosynthetic rate prediction model based on the support vector machine (SVM), radial basis function (RBF) and back propagation (BP) algorithms, the prediction model with the best performance is used as the optimization objective function to obtain the light saturation point; (2) predict the light saturation point under different temperatures and CO2 concentrations using the ant colony optimization (ACO) algorithm and genetic algorithm (GA), compare the predicted light saturation point and measured light saturation point; (3) establish a light environment optimal control model, with temperature and CO2 as the input, light saturation point as the output, and provide a theoretical basis for the precise control of a greenhouse light environment
The SVM photosynthetic rate model could accurately predict the photosynthetic rate under different environmental parameters and it provided a reliable optimization objective function for calculating the light saturation point

Summary

Introduction

Light provides energy for photosynthesis, and it is one of the most important conditions affecting the growth and development of the external environment crops[1]. Studying the optimization method of multiple function flat areas in the crop light environment regulation system to achieve precise and fast acquisition of the light saturation point under different conditions has become the key scientific problem involved in. To achieve precise and fast acquisition of the light saturation point under different conditions, the optimization method for maximum value in flat region needs to be studied. This method is the key to build a high-precision light environment dynamic control model. The objectives of this study were as follow: (1) establish photosynthetic rate prediction model based on the support vector machine (SVM), radial basis function (RBF) and back propagation (BP) algorithms, the prediction model with the best performance is used as the optimization objective function to obtain the light saturation point; (2) predict the light saturation point under different temperatures and CO2 concentrations using the ACO algorithm and GA, compare the predicted light saturation point and measured light saturation point; (3) establish a light environment optimal control model, with temperature and CO2 as the input, light saturation point as the output, and provide a theoretical basis for the precise control of a greenhouse light environment

Objectives

Methods

Results

Discussion

Conclusion