Open Access
ARTICLE
Research on WNN Greenhouse Temperature Prediction Method Based on GA
Wenbin Dai1, Lina Wang1,2,*, Binrui Wang1, Xiaohong Cui1, Xue Li1
1
College of Mechanical and Electronic Engineering, China Jiliang University, Hangzhou, 310018, China
2
Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University,
Hangzhou, 310018, China
* Corresponding Author: Lina Wang. Email:
(This article belongs to the Special Issue: Integrating Agronomy and Plant Physiology for Improving Crop Production)
Phyton-International Journal of Experimental Botany 2022, 91(10), 2283-2296. https://doi.org/10.32604/phyton.2022.021096
Received 27 December 2021; Accepted 03 February 2022; Issue published 30 May 2022
Abstract
Temperature in agricultural production has a direct impact on the growth of crops. The emergence of greenhouses has improved the impact of the original unpredictable changes in temperature, but the temperature modeling of greenhouses is still the main direction at present. Neural network modeling relies on sufficient actual data
to model greenhouses, but there is a widening gap in the application of different neural networks. This paper
proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm
(GA-WNN). With the simple network structure and the nonlinear adaptability of the wavelet basis function,
wavelet neural network (WNN) improved model training speed and accuracy of prediction results compared with
back propagation neural networks (BPNN), which was conducive to the prediction and control of short-term
greenhouse temperature fluctuations. At the same time, the genetic algorithm (GA) was introduced to globally
optimize the initial weights of the original model, which improved the insensitivity of the model to the initial
weights and thresholds, and improved the training speed and stability of the model. Finally, simulation results
for the greenhouse showed that the model training speed, prediction results accuracy and model stability of
the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN
in the greenhouse.
Keywords
Cite This Article
APA Style
Dai, W., Wang, L., Wang, B., Cui, X., Li, X. (2022). Research on WNN greenhouse temperature prediction method based on GA. Phyton-International Journal of Experimental Botany, 91(10), 2283-2296. https://doi.org/10.32604/phyton.2022.021096
Vancouver Style
Dai W, Wang L, Wang B, Cui X, Li X. Research on WNN greenhouse temperature prediction method based on GA. Phyton-Int J Exp Bot. 2022;91(10):2283-2296 https://doi.org/10.32604/phyton.2022.021096
IEEE Style
W. Dai, L. Wang, B. Wang, X. Cui, and X. Li "Research on WNN Greenhouse Temperature Prediction Method Based on GA," Phyton-Int. J. Exp. Bot., vol. 91, no. 10, pp. 2283-2296. 2022. https://doi.org/10.32604/phyton.2022.021096