# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods
### Application of Machine Learning Regression Models
#### 2.4.2 Machine Learning Regression Model Selection  
Several ML regression models were applied to predict almond yield. Among these models, the random forest regressor (RFR) stands out as a prominent option due to its effectiveness in supervised learning [44], and it is used in many fields of study. The RFR algorithm generates an ensemble of decision trees, collectively called an RF. Each decision tree independently learns patterns and relationships within the data, contributing equally to the final prediction, improving the performance and efficacy of the model and dealing with potential overfitting problems [44]. The XGBRegressor (XGBR) was also implemented in this study and is a supervised learning algorithm that belongs to the gradient boosting family. It employs a boosting technique that sequentially improves decision trees, to create a powerful ensemble model [45]. XGBR optimizes the training objective through gradient descent, allowing it to effectively identify complex patterns and dependencies in the data. The model has shown remarkable performance in several areas, making it a valuable tool for almond yield prediction [45]. Regarding the gradient boosting regressor (GBR) algorithm, it is also a gradient boosting-based regression model. It iteratively builds an ensemble of decision trees, with each successive tree better than the previous ones, creating a strong predictive model. The GBR model is widely used in predictive analytics and has demonstrated its efficiency and potential for predicting almond yield [46]. The bagging regressor (BR) algorithm, on the other hand, uses a bagging technique similar to the RFR model. It generates an ensemble of decision trees by resampling the training data and fitting each sample to a separate tree, which are combined to form the final output. Its ability to handle high-dimensional data and complex relationships makes it a suitable candidate for almond yield prediction [47]. Lastly, the AdaBoost regressor (ABR) algorithm is a boosting-based regression model that iteratively adjusts the weights assigned to the training instances, placing more emphasis on the samples that are difficult to predict accurately. ABR is known for its adaptability to different data types and its ability to handle noisy or incomplete datasets [48]. The ML regression models were implemented using the Python library Scikit-learn [49].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Features** & **Selected Features** \\ \hline Irrig. & Irrigation \\ CD & DTJan.; DTAug.; DTNov. (LY); NTMar. \\ VI—PS & EVI2 Feb.; GRVIMar.; GRVIJul.; SAVIIec. (LY) \\ VI—S2 & EVI2 Aug.; GRVIJan.; NDVIAug.; SAVIAug. \\ VI—L8 & EVI2 May.; NDVIMay.; NDVIAug.; SAVSep. (LY) \\ Irrigation and CD & Irrigation; DTMar.; DTAug.; NTMar. \\ Irrigation and VI—PS & Irrigation; GRVIMay.; NDVIJan.; SAVIIMay. \\ Irrigation and VI—S2 & Irrigation; EVI2 Apr.; EVI2 Jul.; GRVIJan. \\ Irrigation and VI—L8 & Irrigation; SAVIMAug.; SAVISep. (LY) \\ CD and VI—PS & DTAug.; EVI2 Mar.; NDVIMar.; SAVIIoc. (LY) \\ CD and VI—S2 & DTJan.; DTOct. (LY); EVI2 Dec. (LY); NDVIAug. \\ CD and VI—L8 & DTMar.; DTAug.; EVI2 Jun.; SAVISep. (LY) \\ Irrigation, CD and VI—PS & Irrigation; NDVIJan.; SAVIMay.; DTMar. \\ Irrigation, CD and VI—S2 & Irrigation; GRVIMay; DTMar.; DTAug. \\ Irrigation, CD and VI—L8 & Irrigation; NDVIMay; DTMar.; NTFeb. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected features using _bestFeatures_ script, categorized by feature type. CD: climate data; DT: daytime temperature; Irrig.: irrigation; L8: Landsat 8; LY: last year; NT: nighttime temperature; PS: PlanetScope; S2: Sentinel-2; VI: vegetation indice.