# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 4 Applications of Ensemble Learning Algorithms
### Yield Prediction  
Machine learning algorithms have been widely used in crop yield prediction [98]. [PERSON] et al. [99] analyzed 50 machine learning papers and 30 deep learning papers concerning crop yield prediction. A neural network was the most frequently used machine learning technique in the machine learning papers; RF was used in 12 studies, and GBRT was used four times. [PERSON] et al. [89] included eleven statistical and machine learning techniques for predicting field-scale wheat yield and found that the two ensemble learning models RF and XGBoost were most accurate in prediction, with R\({}^{2}\) in the range 0.74-0.78 and RMSE in the range 0.78-0.85 t/ha. [PERSON] et al. [66] adopted MLR, XGBoost, RF, and SVR algorithms and three datasets including MODIS EVI, climate data from the climatic research unit, and the subseasonal-to-seasonal (S2S) atmospheric prediction data, to estimate winter wheat yield at the grid level. The results showed that among the four models, XGBoost reached the highest skill with the S2S prediction as inputs, with an R\({}^{2}\) of 0.85 and RMSE of 0.78 t/ha. [PERSON] et al. [61] employed the RF and AdaBoost algorithms to estimate oil palm yield prediction from multi-temporal remote sensing data. Results of the study revealed that the RF model (RMSE = 0.384; MSE = 0.148; MAE = 0.147) outperformed the AdaBoost model (RMSE = 0.410; MSE = 0.168; MAE = 0.176). [PERSON] et al. [82] used nine base learners and two ensemble (average ensemble and Bayesian fusion) methods to estimate wheat yields across the Australian wheat belt from climate data, and satellite image time series. The results showed that SVR with radial basis function merged as the single best learner with the R2 of 0.77 and RMSE of 0.55 t/ha at the pixel level, and the ensemble techniques did not show a significant advantage over the single best model.  
Stacking models are also increasingly used in crop yield prediction. [PERSON] et al. [74] predicted alfalfa yield from unmanned aerial vehicle (UAV)-based hyperspectral images using RF, SVR, k-nearest neighbors (kNN) and stacking ensemble algorithms. Comparison of the results indicated that stacking performed the best among all the base learners, with R\({}^{2}\) = 0.874. [PERSON] et al. [75] combined four base learners (RF, SVM, Gaussian process (GP) and ridge regression (RR)) to predict grain yield across growth stages and found that stacking improved prediction accuracy in both full and limited irrigation scenarios with respective R\({}^{2}\) values of 0.625 and 0.628 at the mid grain filling stage. [PERSON] et al. [84] developed four base models (RF, SVM, GP, and RR) as well as stacking model, to predict winter wheat yields from UAV-based hyperspectral image data. They found that SVM outperformed the other learners and, compared with each base model, the stacking model was more accurate.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Most-Used Ensemble Learning Algorithms** & **Number of Times Used** \\ \hline RF & 30 \\ \hline SVR & 19 \\ \hline stacking & 13 \\ \hline MLP & 10 \\ \hline KNN & 8 \\ \hline XGBoost & 7 \\ \hline GBRT & 4 \\ \hline Adaboost & 4 \\ \hline CART & 3 \\ \hline CatBoost & 2 \\ \hline ERT & 2 \\ \hline SGB & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Most-used ensemble learning algorithms.