Enhanced Rubber Yield Prediction in High-Density Plantation Areas Using a GIS and Machine Learning-Based Forest Classification and Regression Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Spatial Factors
2.1.1. Proximity to Streamlines (X1)
2.1.2. Proximity to Main River (X2)
2.1.3. Soil Drainage (X3)
2.1.4. Slope (X4)
2.1.5. Aspect (X5)
2.1.6. NDWI (X6)
2.1.7. NDVI (X7)
2.1.8. Precipitation (X8)
2.2. Clustering Analysis for Training Point Selection
2.3. Predicting Rubber Yield
- Pre-processing
- Dimensions of the dataset in a random forest
- Feature selection
3. Results
3.1. Correlation Analysis and Dendrogram
3.2. Predicting Rubber Yield Using a Machine Learning-Based FCR Model
4. Discussion
4.1. Focusing on the Areas Where the Projected Rubber Yield Results Were Implemented
4.2. Model Validation
4.3. Comparison of the Results Obtained with Previous Work
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Number of Trees | 100 |
---|---|
Leaf Size | 5 |
Tree Depth Range | 17–33 |
Mean Tree Depth | 22 |
% of Training Data Available per Tree | 100 |
Number of Randomly Sampled Variables | 2 |
% of Training Data Excluded for Validation | 10 |
Number of Trees | 50 | 100 |
---|---|---|
MSE | 758.876 | 702.985 |
% of Variation Explained | −25.169 | −15.951 |
Variable | Importance | % |
---|---|---|
X2 | 79,633.31 | 15 |
X5 | 78,805.16 | 15 |
X4 | 78,556.29 | 15 |
X8 | 77,602.85 | 15 |
X6 | 75,886.30 | 14 |
X7 | 73,357.62 | 14 |
X1 | 48,716.34 | 9 |
X3 | 18,135.91 | 3 |
Alternative Models | Factors of FCR and OLS Model | RMSE (kg./ha.) | R2 (FCR) | R2 (OLS) | Difference in R2 (FCR > OLS) |
---|---|---|---|---|---|
Model 1 | X4, X5, X8 | 19.15 | 0.787 | 0.642 | +18.42% |
Model 2 | X3, X6, X7 | 28.97 | 0.542 | 0.503 | +7.19% |
Model 3 | X1, X2, X6 | 30.26 | 0.730 | 0.586 | +19.72% |
Model 4 | X1, X2, X3, X4, X5, X6, X7, X8 | 15.87 | 0.821 | 0.678 | +17.41% |
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Littidej, P.; Kromkratoke, W.; Pumhirunroj, B.; Buasri, N.; Prasertsri, N.; Sangpradid, S.; Slack, D. Enhanced Rubber Yield Prediction in High-Density Plantation Areas Using a GIS and Machine Learning-Based Forest Classification and Regression Model. Forests 2024, 15, 1535. https://doi.org/10.3390/f15091535
Littidej P, Kromkratoke W, Pumhirunroj B, Buasri N, Prasertsri N, Sangpradid S, Slack D. Enhanced Rubber Yield Prediction in High-Density Plantation Areas Using a GIS and Machine Learning-Based Forest Classification and Regression Model. Forests. 2024; 15(9):1535. https://doi.org/10.3390/f15091535
Chicago/Turabian StyleLittidej, Patiwat, Winyoo Kromkratoke, Benjamabhorn Pumhirunroj, Nutchanat Buasri, Narueset Prasertsri, Satith Sangpradid, and Donald Slack. 2024. "Enhanced Rubber Yield Prediction in High-Density Plantation Areas Using a GIS and Machine Learning-Based Forest Classification and Regression Model" Forests 15, no. 9: 1535. https://doi.org/10.3390/f15091535