Multi-Parameter Prediction of Solar Greenhouse Environment Based on Multi-Source Data Fusion and Deep Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Platform Construction and Data Acquisition
2.2. Overview of the Experimental Area
2.3. Sensor Layout Plan
2.4. Data Preprocessing
2.5. Analysis of Horizontal Distribution Differences in Key Environmental Parameters of the Greenhouse
2.6. Analysis of Vertical Distribution Differences in Key Environmental Parameters in the Greenhouse
3. Model Theory
3.1. Golden Jackal Optimization Algorithm
3.2. Convolutional Neural Networks
3.3. Bidirectional Gated Recurrent Unit Neural Network
3.4. Self-Attention Mechanism
3.5. GCBS Hybrid Network Model
3.6. Model Evaluation Metrics
4. Results and Analysis
4.1. Model Optimization and Training
4.2. Model Results and Analysis
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Forecasting Task | CO2 Concentration Prediction | |||||
---|---|---|---|---|---|---|
Model Number | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
Node 1 | R2 | 0.897 | 0.946 | 0.952 | 0.976 | 0.986 |
MAE/(mg/kg) | 68.500 | 49.100 | 44.700 | 30.100 | 21.700 | |
Node 2 | R2 | 0.873 | 0.937 | 0.967 | 0.979 | 0.988 |
MAE/(mg/kg) | 100.700 | 67.200 | 48.200 | 37.900 | 23.900 | |
Node 3 | R2 | 0.895 | 0.933 | 0.947 | 0.965 | 0.986 |
MAE/(mg/kg) | 85.500 | 48.600 | 42.800 | 33.900 | 23.700 | |
Node 4 | R2 | 0.874 | 0.939 | 0.954 | 0.974 | 0.990 |
MAE/(mg/kg) | 112.480 | 58.900 | 48.800 | 37.700 | 19.500 | |
Node 5 | R2 | 0.887 | 0.931 | 0.965 | 0.980 | 0.990 |
MAE/(mg/kg) | 105.400 | 58.800 | 42.400 | 32.400 | 20.400 | |
Average node | R2 | 0.885 | 0.937 | 0.957 | 0.975 | 0.988 |
MAE/(mg/kg) | 94.516 | 56.520 | 45.380 | 34.400 | 21.840 |
Forecasting Task | Air Humidity Prediction | |||||
---|---|---|---|---|---|---|
Model Number | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
Node 1 | R2 | 0.763 | 0.857 | 0.916 | 0.941 | 0.958 |
MAE/(%) | 1.300 | 0.880 | 0.740 | 0.750 | 0.460 | |
Node 2 | R2 | 0.801 | 0.873 | 0.903 | 0.932 | 0.951 |
MAE/(%) | 1.010 | 0.920 | 1.500 | 0.710 | 0.490 | |
Node 3 | R2 | 0.791 | 0.917 | 0.923 | 0.944 | 0.960 |
MAE/(%) | 2.940 | 1.080 | 1.050 | 0.880 | 0.560 | |
Node 4 | R2 | 0.773 | 0.858 | 0.921 | 0.939 | 0.960 |
MAE/(%) | 1.570 | 1.030 | 0.890 | 0.740 | 0.440 | |
Node 5 | R2 | 0.786 | 0.861 | 0.928 | 0.935 | 0.955 |
MAE/(%) | 1.310 | 1.160 | 0.830 | 0.790 | 0.540 | |
Average node | R2 | 0.783 | 0.873 | 0.918 | 0.938 | 0.957 |
MAE/(%) | 1.626 | 1.014 | 1.002 | 0.774 | 0.498 |
Forecasting Task | Air Temperature Prediction | |||||
---|---|---|---|---|---|---|
Model Number | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
Node 1 | R2 | 0.885 | 0.930 | 0.949 | 0.959 | 0.969 |
MAE/(°C) | 1.350 | 1.070 | 0.800 | 0.720 | 0.580 | |
Node 2 | R2 | 0.868 | 0.911 | 0.944 | 0.953 | 0.963 |
MAE/(°C) | 1.450 | 1.230 | 0.880 | 0.730 | 0.610 | |
Node 3 | R2 | 0.877 | 0.922 | 0.947 | 0.958 | 0.972 |
MAE/(°C) | 1.570 | 1.170 | 0.910 | 0.770 | 0.570 | |
Node 4 | R2 | 0.878 | 0.927 | 0.944 | 0.956 | 0.969 |
MAE/(°C) | 1.360 | 1.000 | 0.800 | 0.710 | 0.540 | |
Node 5 | R2 | 0.888 | 0.928 | 0.957 | 0.963 | 0.972 |
MAE/(°C) | 1.290 | 1.050 | 0.750 | 0.700 | 0.520 | |
Average node | R2 | 0.879 | 0.924 | 0.948 | 0.958 | 0.969 |
MAE/(°C) | 1.404 | 1.104 | 0.828 | 0.726 | 0.564 |
Forecasting Task | Soil Temperature Prediction | |||||
---|---|---|---|---|---|---|
Model Number | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
Node 1 | R2 | 0.906 | 0.925 | 0.937 | 0.960 | 0.970 |
MAE/(°C) | 0.078 | 0.068 | 0.060 | 0.057 | 0.043 | |
Node 2 | R2 | 0.765 | 0.917 | 0.933 | 0.958 | 0.970 |
MAE/(°C) | 0.171 | 0.079 | 0.061 | 0.059 | 0.041 | |
Node 3 | R2 | 0.922 | 0.937 | 0.951 | 0.971 | 0.975 |
MAE/(°C) | 0.089 | 0.079 | 0.067 | 0.058 | 0.048 | |
Node 4 | R2 | 0.869 | 0.911 | 0.929 | 0.970 | 0.983 |
MAE/(°C) | 0.088 | 0.071 | 0.064 | 0.057 | 0.046 | |
Node 5 | R2 | 0.927 | 0.921 | 0.945 | 0.958 | 0.968 |
MAE/(°C) | 0.080 | 0.070 | 0.073 | 0.054 | 0.041 | |
Average node | R2 | 0.878 | 0.922 | 0.939 | 0.963 | 0.973 |
MAE/(°C) | 0.101 | 0.073 | 0.065 | 0.057 | 0.044 |
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Share and Cite
Yuan, M.; Zhang, Z.; Li, G.; He, X.; Huang, Z.; Li, Z.; Du, H. Multi-Parameter Prediction of Solar Greenhouse Environment Based on Multi-Source Data Fusion and Deep Learning. Agriculture 2024, 14, 1245. https://doi.org/10.3390/agriculture14081245
Yuan M, Zhang Z, Li G, He X, Huang Z, Li Z, Du H. Multi-Parameter Prediction of Solar Greenhouse Environment Based on Multi-Source Data Fusion and Deep Learning. Agriculture. 2024; 14(8):1245. https://doi.org/10.3390/agriculture14081245
Chicago/Turabian StyleYuan, Ming, Zilin Zhang, Gangao Li, Xiuhan He, Zongbao Huang, Zhiwei Li, and Huiling Du. 2024. "Multi-Parameter Prediction of Solar Greenhouse Environment Based on Multi-Source Data Fusion and Deep Learning" Agriculture 14, no. 8: 1245. https://doi.org/10.3390/agriculture14081245
APA StyleYuan, M., Zhang, Z., Li, G., He, X., Huang, Z., Li, Z., & Du, H. (2024). Multi-Parameter Prediction of Solar Greenhouse Environment Based on Multi-Source Data Fusion and Deep Learning. Agriculture, 14(8), 1245. https://doi.org/10.3390/agriculture14081245