# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 3 Results
### Selected Features and Their Contribution to Almond Yield Prediction  
Considering the information from the previous subsection, optimal performance was achieved with the XGB model using the irrigation feature, climate data (specifically, the daytime temperature in March), and VIs (the NDVI in January and the SAVI in May) calculated using PlanetScope data. This subsection is intended to present the features that were important to almond yield prediction. Figure 5a shows that in the almond orchards where irrigation was applied (AG3 and AG4), higher yield values were recorded, about 913 kg/ha, while in the almond orchards where irrigation was not applied (AG1 and AG2), lower yield values were recorded, about 417 kg/ha. Comparing the NDVI in January and the yield (Figure 5b), it is possible to observe higher NDVI values related to a higher yield, although it is not a clear linear relationship. In the almond orchards of AG4 (Figure 1b), higher NDVI values were recorded in January from 2019 to 2021, which may have made a  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type of Features**} & \multicolumn{3}{c}{**RFR**} & \multicolumn{3}{c}{**XGBR**} & \multicolumn{3}{c}{**GBR**} & \multicolumn{3}{c}{**BR**} & \multicolumn{3}{c}{**ABR**} \\ \cline{2-11}  & _MAE_ & _RMSE_ & _MAE_ & _RMSE_ & _MAE_ & _RMSE_ & _MAE_ & _RMSE_ & _MAE_ & _RMSE_ \\ \hline Irigg. & 159 & 211 & 161 & 211 & 161 & 211 & 160 & 212 & 158 & 206 \\ CD & 184 & 237 & 210 & 255 & 178 & 211 & 184 & 238 & 179 & 223 \\ VI—PS & 154 & 200 & 173 & 220 & 148 & 194 & 152 & 201 & 143 & 186 \\ VI—S2 & 168 & 214 & 177 & 224 & 181 & 232 & 165 & 215 & 184 & 229 \\ VI—L8 & 144 & 189 & 158 & 250 & 133 & 193 & 145 & 191 & 142 & 202 \\ Irigg. and CD & 134 & 170 & 125 & 177 & 116 & 158 & 137 & 172 & 133 & 174 \\ Irigg. and VI—PS & 132 & 158 & 107 & 138 & 117 & 138 & 131 & 159 & 118 & 153 \\ Irigg. and VI—S2 & 147 & 190 & 130 & 168 & 130 & 168 & 149 & 192 & 120 & 163 \\ Irigg. and VI—L8 & 145 & 176 & 141 & 199 & 111 & 147 & 144 & 178 & 142 & 180 \\ CD and VI—PS & 157 & 193 & 151 & 189 & 152 & 210 & 160 & 193 & 143 & 177 \\ VI—S2 & 156 & 211 & 139 & 175 & 196 & 220 & 154 & 211 & 160 & 209 \\ CD and VI—S2 & 139 & 174 & 127 & 159 & 123 & 162 & 133 & 171 & 114 & 154 \\ VI—L8 & 126 & 153 & 95 & 119 & 95 & 125 & 129 & 153 & 113 & 151 \\ Irigg. CD and VI—S2 & 135 & 173 & 126 & 175 & 124 & 171 & 135 & 174 & 123 & 173 \\ Irigg., CD and VI—S2 & 126 & 163 & 137 & 178 & 154 & 194 & 123 & 163 & 108 & 152 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of regression models by feature type and remote sensing platform, assessed using mean absolute error (_MAE_) and root mean square error (_RMSE_). Units in kg/ha. CD: climate data; Irrig: irrigation; L8: Landsat 8; PS: PlanetScope; S2: Sentinel-2; VI: vegetation indice.  
positive contribution to achieving higher production values compared to other growers. Similarly, regarding the SAVI in May (Figure 5c), it does not seem to be linearly associated with the yield, highlighting the importance of ML models, as non-linear patterns can be identified by these models. On the other hand, regarding the daytime temperature in March (Figure 5d), it is evident that lower values were associated with lower yield values. The lowest daytime temperature value in March (12.2 \({}^{\circ}\)C) was recorded in 2018 in the almond orchards of AG2, which coincided with the lowest yield value (82 kg/ha) compared to the other growers (Figure 1b). As for the highest values recorded for the daytime temperature in March, the maximum value reached was 22.5 \({}^{\circ}\)C in 2019 by AG4, who obtained higher yield values (1203 kg/ha).  
Figure 4: Regression model performance based on feature type and remote sensing platform, using the coefficient of determination (\(R^{2}\)). In (**a**) vegetation indices were computed using PlanetScope data; in (**b**) vegetation indices were computed using Sentinel-2 data; and in (**c**) vegetation indices were computed using Landsat 8 data.