Prediction of Tomato Yield in Chinese-Style Solar Greenhouses Based on Wavelet Neural Networks and Genetic Algorithms

Yield prediction for tomatoes in greenhouses is an important basis for making production plans, and yield prediction accuracy directly affects economic benefits. To improve the prediction accuracy in Chinese-style solar greenhouses (CSGs), a wavelet neural network (WNN) model optimized by a genetic algorithm (GA-WNN) is applied. Eight variables are selected as input parameters, such as the CO2 concentration and ambient humidity. The tomato yield is the prediction output. The GA is used to optimize the initial weights, thresholds, and translation factors of the WNN. The experiment results show that the mean relative errors (MREs) of the GA-WNN model, WNN model, and backpropagation BP neural network model are 0.0067, 0.0104, and 0.0242, respectively. The results root mean square errors (RMSEs) are 1.725, 2.520, and 5.548, respectively. The EC values are 0.9960, 0.9935, and 0.9868, respectively. Therefore, the GA-WNN model has a higher prediction precision and a better fitting ability when compared with the BP and the WNN prediction models. The GA-WNN model can overcome the shortcomings of slow convergence and finding local optimums with typical WNN models. The research of this paper is useful from both theoretical and technical perspectives for quantitative tomato yield prediction in the CSGs.