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Greenhouse temperature prediction model based on ISGA-AMSCNN-DD
Yuqiang Yang1, Kun Song1, Huanzhi Luo1
1Research Center of Guangdong Smart Oceans Sensor Networks and Equipment Engineering, Guangdong Ocean University, Zhanjiang, People's Republic of China.
Environmental Technology
|August 11, 2025
Summary
Accurate greenhouse temperature prediction is vital for advanced control. A new hybrid model, ISGA-AMSCNN-DD, improves forecasting by integrating optimization algorithms and deep learning for reliable environmental management.
Area of Science:
- Agricultural Engineering
- Artificial Intelligence
- Environmental Science
Background:
- Accurate short-term greenhouse temperature prediction is essential for advanced control strategies like model predictive control.
- Nonlinear interactions between weather, crop transpiration, and regulation devices challenge forecasting accuracy.
Purpose of the Study:
- To develop a novel hybrid prediction model, ISGA-AMSCNN-DD, for enhanced greenhouse temperature forecasting.
- To address the challenges posed by complex environmental dynamics in greenhouses.
Main Methods:
- Integration of Improved Snow Goose Algorithm (ISGA) for parameter optimization.
- Utilizing an attention-enhanced Multi-Scale Convolutional Neural Network (AMSCNN) for feature extraction.
- Employing a dendritic network architecture (DD) to improve model generalization.
Main Results:
- The ISGA-AMSCNN-DD model demonstrated superior performance in real-world greenhouse data.
- Achieved high accuracy with R² of 0.9796, PBIAS of 0.1218%, NSE of 0.9849, RMSE of 0.6232°C, and MAPE of 2.8623%.
Conclusions:
- The proposed ISGA-AMSCNN-DD model is accurate and reliable for short-term greenhouse temperature prediction.
- The model provides a robust foundation for intelligent greenhouse management and precise temperature control.
Keywords:
AMSCNN-DDImproved snow goose algorithmattention mechanismshort-term greenhouse temperature predictionsmart agricultureMore Related Videos
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