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Multi-Parameter Prediction of Solar Greenhouse Environment Based on Multi-Source Data Fusion and Deep Learning

Author

Listed:
  • Ming Yuan

    (College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China)

  • Zilin Zhang

    (College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China)

  • Gangao Li

    (College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China)

  • Xiuhan He

    (College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China)

  • Zongbao Huang

    (College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China)

  • Zhiwei Li

    (College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
    College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China)

  • Huiling Du

    (Department of Basic Sciences, Shanxi Agricultural University, Jinzhong 030801, China)

Abstract

In the process of agricultural production in solar greenhouses, the key to the healthy growth of greenhouse crops lies in accurately predicting environmental conditions. However, there are complex couplings and nonlinear relationships among greenhouse environmental parameters. This study independently developed a greenhouse environmental acquisition system to achieve a comprehensive method for the monitoring of the greenhouse environment. Additionally, it proposed a multi-parameter and multi-node environmental prediction model for solar greenhouses based on the Golden Jackal Optimization-Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Self-Attention Mechanism (GCBS). The GCBS model successfully captures the complex nonlinear relationships in the greenhouse environment and accurately predicts changes in carbon dioxide concentration, air temperature and humidity, and soil temperature at different location nodes. To validate the performance of this model, we employed multiple evaluation metrics and conducted a comparative analysis with four baseline models. The results indicate that, while the GCBS model exhibits slightly higher computational time compared to the traditional Long Short-Term Memory (LSTM) network for time series prediction, it significantly outperforms the LSTM in terms of prediction accuracy for four key parameters, achieving improvements of 76.89%, 69.37%, 59.83%, and 56.72%, respectively, as measured by the Mean Absolute Error (MAE) metric.

Suggested Citation

  • Ming Yuan & Zilin Zhang & Gangao Li & Xiuhan He & Zongbao Huang & Zhiwei Li & Huiling Du, 2024. "Multi-Parameter Prediction of Solar Greenhouse Environment Based on Multi-Source Data Fusion and Deep Learning," Agriculture, MDPI, vol. 14(8), pages 1-21, July.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:8:p:1245-:d:1444525
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    References listed on IDEAS

    as
    1. Lin, Dong & Zhang, Lijun & Xia, Xiaohua, 2021. "Model predictive control of a Venlo-type greenhouse system considering electrical energy, water and carbon dioxide consumption," Applied Energy, Elsevier, vol. 298(C).
    2. Wu, Xiaoyang & Li, Yiming & Jiang, Lingling & Wang, Yang & Liu, Xingan & Li, Tianlai, 2023. "A systematic analysis of multiple structural parameters of Chinese solar greenhouse based on the thermal performance," Energy, Elsevier, vol. 273(C).
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