Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Methods
2.3.1. Artificial Neural Network
2.3.2. SVM
2.3.3. Methodologies for Model Evaluation
- The Kappa coefficient can measure the accuracy of the multi-class classification problem when it is used in the consistency test, and its calculation method is based on a confusion matrix.According to the previous experience, K usually falls between 0–1, which can be divided into five groups to represent the consistency of different levels, and generally when it falls between 0.61 and 0.80, it is considered to have a high degree of consistency [44].
- Accuracy. This is the ratio of the number of correct samples to the total number of samples.
- Average accuracy. This is the average accuracy of each sample. For imbalanced data, for n classes, the accuracy of each class is calculated respectively, and then the average value is calculated.
2.3.4. Meteorological Yield
2.3.5. Selection of Variables
3. Results
3.1. Model Parameter Adjustment
3.2. Classification Results
3.3. Actual Prediction of the Models
3.4. Factor Importance Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | Min | Max | VIF |
---|---|---|---|
Latitude | 118.156 | 30.737 | 1.684 |
Slope | 0 | 50.306 | 1.047 |
Aspect | −1 | 359.963 | 2.134 |
Elevation | −57 | 1801 | 1.038 |
Curvature | −0.210 | 0.231 | 1.1 |
Minimum temperature | −1.856 | 7.647 | 1.187 |
Relative humidity | 31.323 | 69.710 | 1.881 |
Sunshine hours | 4.420 | 9.692 | 2.082 |
Wind velocity | 0.843 | 4.232 | 1.239 |
Accuracy | Average Accuracy | Kappa Coefficient | |
---|---|---|---|
SVM | 0.8375 | 0.7929 | 0.791 |
ANN | 0.75 | 0.7129 | 0.6737 |
Area | Meteorological Yield | M | Area | Meteorological Yield | M |
---|---|---|---|---|---|
Hang zhou | −0.017 | 3.000 | Ji an | 0.005 | 3.071 |
Sheng zhou | −0.055 | 3.560 | Xin chang | −0.086 | 3.241 |
Hu zhou | 0.029 | 3.067 | Ning bo | 0.045 | 2.667 |
Wu yi | −0.040 | 3.385 | Yu yao | 0.045 | 2.429 |
Yu hang | 0.062 | 2.760 | Ning hai | −0.018 | 3.027 |
Zhu ji | 0.149 | 2.696 | Long you | −0.049 | 3.360 |
Fu yang | −0.124 | 3.529 | Jian de | −0.012 | 3.094 |
Chun an | −0.012 | 2.777 |
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Xu, J.; Guga, S.; Rong, G.; Riao, D.; Liu, X.; Li, K.; Zhang, J. Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning. Agriculture 2021, 11, 607. https://doi.org/10.3390/agriculture11070607
Xu J, Guga S, Rong G, Riao D, Liu X, Li K, Zhang J. Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning. Agriculture. 2021; 11(7):607. https://doi.org/10.3390/agriculture11070607
Chicago/Turabian StyleXu, Jie, Suri Guga, Guangzhi Rong, Dao Riao, Xingpeng Liu, Kaiwei Li, and Jiquan Zhang. 2021. "Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning" Agriculture 11, no. 7: 607. https://doi.org/10.3390/agriculture11070607