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

Ming Yuan, Zilin Zhang, Gangao Li, Xiuhan He, Zongbao Huang, Zhiwei Li () and Huiling Du ()
Additional contact information
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
Huiling Du: Department of Basic Sciences, Shanxi Agricultural University, Jinzhong 030801, China

Agriculture, 2024, vol. 14, issue 8, 1-21

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.

Keywords: deep learning; solar greenhouse; intelligent agricultural greenhouse; environmental factor prediction; environmental acquisition system (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/8/1245/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/8/1245/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:8:p:1245-:d:1444525

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().