Light Environment Regulation Target Model of Tomato Based on Improved Fish Swarm Algorithm

Abstract:

To struggle with the problems of hard to acquire the optimum light value for tomato planting rapidly and precisely, a model was developed to control the light staying around the optimum value in the environment. In order to evaluate the optimum light saturation points under different temperatures, a novel light and temperature coupling optimizing method based on improved fish swarm algorithm was proposed. This new method effectively improved the optimum speed of traditional fish swarm algorithm through adjusting the vision and step dynamically. In addition, the method could avoid trapping into local optimum, and get more accurate optimal solution than genetic algorithm. Based on the light saturation points by optimizing this method, the light environment regulation target model was established with nonlinear regression. For verifying the accuracy of the method, a set of light and temperature coupling photosynthetic rate test was performed. The results showed that the model determination coefficient can reach 0.9999, the squared error term was 1543, and the root mean square error was 0.712. A comparison between simulation results and testing results was made, which showed the highly linear correlate relation with a value of 0.988 between them. In addition, the maximum relative error was less than ±2%, which is obviously better than the results of genetic algorithm. At last, a positive conclusion was obtained that the proposed light and temperature coupling optimizing method in this study can acquire the optimum light saturation points rapidly and dynamically, and has great significance to the precise control of light environment in facility.