Visible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling
Abstract
:1. Introduction
2. Materials and Methods
2.1. Datasets
2.1.1. Sample Collection and Measurement
2.1.2. Hyperspectral Data Measurement and Preprocessing
2.2. Construction of the Model
2.2.1. Single-Prediction Model Construction
2.2.2. Combinatorial Predictive Modeling
2.2.3. Calculation of Accuracy Validation Metrics
- (1)
- Calculate the combined predicted value of
- (2)
- Calculate the relative error and the prediction accuracy of the combination prediction for the tth sample
- (3)
- Calculate the combination-prediction validity M
- (4)
- Approximate solution of the combined prediction model. Since the objective function is not derivable, i.e., the model is non-frivolous nonlinear programming, coupled with the fact that the computational complexity of the model is larger when n and m are larger, the non-frivolous nonlinear programming is transformed into frivolous nonlinear programming to solve the problem. The combined prediction model is equivalent to the following model (Equation (15)):
2.2.4. The Planning and Solving Algorithm for the Combinatorial Predictive Modeling
3. Results
3.1. L1-Paradigm Hyperspectral Feature Selection
3.2. Results of Single-Prediction Model
3.3. Combined Prediction Model
3.4. Residual Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Organic Matter Content (g/kg) | Number of Samples | Minimum Value (g/kg) | Maximum Value (g/kg) | Mean Value (g/kg) | Standard Deviation (g/kg) | Coefficient of Variation (%) |
---|---|---|---|---|---|---|
(0.000, 5.000] | 31 | 1.976 | 4.831 | 3.676 | 0.892 | 24.266 |
(5.000, 10.000] | 67 | 5.051 | 9.882 | 7.901 | 1.335 | 16.897 |
(10.000, 15.000] | 101 | 10.102 | 14.933 | 12.897 | 1.433 | 11.111 |
(15.000, 20.000] | 92 | 15.153 | 19.984 | 17.068 | 1.335 | 7.822 |
(20.000, 32.228] | 21 | 20.087 | 32.228 | 23.989 | 3.289 | 13.710 |
(0.000, 32.228] | 312 | 1.976 | 32.228 | 12.885 | 5.441 | 42.227 |
Model | Train Set | Validation Set | Test Set | DataSet | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
E | σ | M | E | σ | M | E | σ | M | E | σ | M | |
LASSO | 0.865 | 0.155 | 0.731 | 0.836 | 0.153 | 0.708 | 0.802 | 0.220 | 0.626 | 0.853 | 0.163 | 0.714 |
MLP | 0.908 | 0.179 | 0.746 | 0.777 | 0.201 | 0.621 | 0.773 | 0.163 | 0.647 | 0.869 | 0.192 | 0.702 |
RF | 0.865 | 0.201 | 0.691 | 0.774 | 0.230 | 0.595 | 0.715 | 0.305 | 0.497 | 0.832 | 0.225 | 0.645 |
GKR | 0.914 | 0.134 | 0.791 | 0.827 | 0.159 | 0.695 | 0.780 | 0.244 | 0.590 | 0.883 | 0.161 | 0.741 |
Ridge | 0.865 | 0.155 | 0.731 | 0.839 | 0.152 | 0.712 | 0.805 | 0.218 | 0.630 | 0.854 | 0.163 | 0.715 |
LSTM | 0.901 | 0.166 | 0.751 | 0.814 | 0.137 | 0.703 | 0.785 | 0.288 | 0.559 | 0.872 | 0.182 | 0.713 |
CNN | 0.881 | 0.158 | 0.742 | 0.756 | 0.211 | 0.597 | 0.768 | 0.275 | 0.556 | 0.845 | 0.192 | 0.683 |
SVR | 0.847 | 0.162 | 0.710 | 0.839 | 0.130 | 0.730 | 0.817 | 0.229 | 0.630 | 0.843 | 0.164 | 0.704 |
Evaluating Indicator | LASSO | MLP | RF | GKR | Ridge | LSTM | CNN | SVR | Combining Model |
---|---|---|---|---|---|---|---|---|---|
E | 0.853 | 0.869 | 0.832 | 0.883 | 0.854 | 0.872 | 0.845 | 0.843 | 0.893 |
σ | 0.163 | 0.192 | 0.225 | 0.161 | 0.163 | 0.182 | 0.192 | 0.164 | 0.129 |
M | 0.714 | 0.702 | 0.645 | 0.741 | 0.715 | 0.713 | 0.683 | 0.704 | 0.778 |
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Zhang, X.; Liu, D.; Ma, J.; Wang, X.; Li, Z.; Zheng, D. Visible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling. Agronomy 2024, 14, 789. https://doi.org/10.3390/agronomy14040789
Zhang X, Liu D, Ma J, Wang X, Li Z, Zheng D. Visible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling. Agronomy. 2024; 14(4):789. https://doi.org/10.3390/agronomy14040789
Chicago/Turabian StyleZhang, Xiuquan, Dequan Liu, Junwei Ma, Xiaolei Wang, Zhiwei Li, and Decong Zheng. 2024. "Visible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling" Agronomy 14, no. 4: 789. https://doi.org/10.3390/agronomy14040789