# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 3 Results  
### Comparative Analysis of Regression Models for Almond Yield Prediction  
This study intends to investigate and compare the performance of several ML regression models in predicting almond tree yield, based on different features extracted from RS platforms and different agronomic parameters. The features considered were irrigation, temperature, precipitation, and VIs. The regression models evaluated included the RFR, XGBR, GBR, BR, and ABR models. Figure 4 shows the performance of the regression models, using different types of features, considering the VIs computed by PlanetScope (Figure 4a), Sentinel-2 (Figure 4b), and Landsat 8 (Figure 4c). When focusing on the irrigation feature alone, none of the models demonstrated exceptional performance as a standalone feature across all three platforms. The \(R^{2}\) scores for all the models ranged from 0.32 to 0.38, suggesting limited predictive capabilities. When considering only the climatic features, the GBR model and BR model consistently exhibited higher performance, with \(R^{2}\) scores of around 0.40. For the VIs only, the ABR model outperformed the others, using PlanetScope data (\(R^{2}\) = 0.59). On the other hand, considering the VIs calculated using Sentinel-2 data, the BR model performed better (\(R^{2}\) = 0.49). For the VIs from Landsat 8, the best performance was achieved by the RFR model (\(R^{2}\) = 0.61). When combining features, particularly irrigation and climate data, the RFR model consistently demonstrated strong performance across all three platforms (\(R^{2}\) = 0.68), indicating its superior predictive capabilities for this feature combination. Considering the combination of irrigation and VIs, the XGBR model obtained the best performance using PlanetScope (\(R^{2}\) = 0.76) and Sentinel-2 (\(R^{2}\) = 0.66) data. In contrast, the GBR model produced the best result (\(R^{2}\) = 0.73) when the Landsat 8 data were used. Combining the climate data and VIs, the ABR model performed well using PlanetScope data (\(R^{2}\) = 0.61). On the other hand, the XGBR model performed better using the Sentinel-2 (\(R^{2}\) = 0.62) and Landsat 8 data (\(R^{2}\) = 0.69). Finally, considering all three groups of features combined (irrigation, climate data, and VIs), the XGBR model showed the best performance using PlanetScope data (\(R^{2}\) = 0.80), while the RFR model showed the best performance using Sentinel-2 data (\(R^{2}\) = 0.67) and the ABR model using Landsat 8 data (\(R^{2}\) = 0.72). The results presented showed that the XGBR and RFR models proved to be the most appropriate models for predicting the target feature using the PlanetScope data across different feature combinations. However, when using Landsat 8 data, the ABR model also provided remarkable results. The performance of several regression models was also evaluated based on \(MAE\) and \(RMSE\) metrics, considering the different combinations of feature types (Table 5). Notably, the best combination of features that yielded optimal results varied across the models. When focusing on the irrigation feature, all the models (RFR, XGBR, GRR, BR, and ABR) achieved similar performance, with \(MAE\) values ranging from 158 to 161 kg/ha and \(RMSE\) values ranging from 206 to 212 kg/ha. For the climate data features, the XGBR (\(MAE\) = 210 kg/ha; \(RMSE\) = 255 kg/ha) and BR (\(MAE\) = 184 kg/ha; \(RMSE\) = 238 kg/ha)models exhibited slightly higher values compared to the other models. Among the VIs, when the PlanetScope data were used, the best performance was achieved with the ABR model (_MAE_ = 143 kg/ha; _RMSE_ = 186 kg/ha). For the Sentinel-2 data, the lowest _MAE_ (165 kg/ha) was obtained with the BR model and the lowest _RMSE_ (214 kg/ha) was obtained with the RFR model. Considering the VIs from Landsat 8, the lowest _MAE_ (133 kg/ha) was obtained with the GBR model and the lowest _RMSE_ (189 kg/ha) with the RFR model.