Temperature prediction model for crop greenhouse environment based on optimized wavelet neural network

doi:10.13733/j.jcam.issn.20955553.2024.12.012

Abstract

Abstract: The temperature of greenhouse environment is a key factor affecting the growth of greenhouse crops, and its evolution is comprehensively influenced by multiple factors due to obvious nonlinear and complex characteristics, and its prediction is very difficult. Based on this, an improved slime mold algorithm and optimized wavelet neural network model CASMA-WNN for crop greenhouse environmental temperature prediction was proposed. The improved slime mold algorithm was used to optimize the initial values of key parameters of the wavelet neural network, PWLCM chaotic mapping was introduced into the traditional slime mold algorithm to optimize the initial population diversity, and an adaptive hybrid mutation mechanism was designed to improve the search accuracy of the algorithm. At the same time, the principal component analysis method was used to extract the principal component characteristic factors that had the highest correlation with crop greenhouse temperature to achieve data dimensionality reduction. The principal component factors were input into the optimized and tuned wavelet neural network model to predict the environmental temperature of crop greenhouse. The results show that compared with BP neural network and standard wavelet neural network, the average absolute error of CASMA-WNN model can be reduced by 34.23% and 26.56% respectively, and its data fit is higher and the prediction error is smaller, which can provide decision support for the appropriate control of crop growth temperature in the greenhouse.

Key words: greenhouse temperature, wavelet neural network, principal component analysis, slime mold algorithm

摘要： 温室环境温度是影响大棚农作物生长的关键因素，其演变受多重因素的综合影响，具有明显的非线性复杂特征，预测难度较大。基于此，提出一种改进黏菌算法优化小波神经网络的作物温室环境温度预测模型CASMA-WNN。利用改进黏菌算法对小波神经网络的关键参数初值调优，在传统黏菌算法中引入PWLCM混沌映射优化初始种群多样性，设计自适应混合变异机制提高算法的搜索精度。同时，利用主成分分析法提取与作物温室环境温度相关性最高的主成分特征因子，实现数据降维。并将主成分因子输入优化调参后的小波神经网络模型，对作物温室环境温度进行预测。结果表明，与BP神经网络和标准小波神经网络相比，CASMA-WNN模型的平均绝对误差分别可以降低34.23%和26.56%，其数据拟合度更高，预测误差更小，可以为适宜的温室内作物生长温度调控提供决策支撑。

关键词: 温室温度, 小波神经网络, 主成分分析法, 黏菌算法

CLC Number:

S625
TV62

Ni Meiyu, Du Zitao, Cao Weigang. Temperature prediction model for crop greenhouse environment based on optimized wavelet neural network[J]. Journal of Chinese Agricultural Mechanization, 2024, 45(12): 73-80.

倪美玉, 杜子涛, 曹为刚. 基于优化小波神经网络的作物温室环境温度预测模型[J]. 中国农机化学报, 2024, 45(12): 73-80.

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