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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: 19A0102172@cjlu.edu.cn
(This article belongs to this Special Issue: Integrating Agronomy and Plant Physiology for Improving Crop Production)

Phyton-International Journal of Experimental Botany https://doi.org/10.32604/phyton.2022.021096

Received 27 December 2021; Accepted 03 February 2022; Published online 28 April 2022


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.


Greenhouse temperature; greenhouse modeling; wavelet neural network; genetic algorithm
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