## 4 Discussion
### Machine Learning Performance  
The RF algorithm emerged as the best-performing one in the prediction of the olive tree \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\). The results of RF in regression analyses, compared with other models, are generally in line with those in the literature [58]. RF has been successfully investigated and used to monitor and predict several important crop parameters. For instance, [PERSON] et al. [89] demonstrated the effectiveness of RF in estimating the net photosynthetic rate (Pn)--another important photosynthetic parameter related to the physiological state of plants--of rice (_Oryza sativa_, L) using UAV multispectral images. Their study showed that  
Figure 8: SHAP dependence plots illustrate the marginal contributions of the selected features according to their SHAP values. Each panel shows how a single feature’s SHAP value (\(y\)-axis) varies with the actual values of that feature (\(x\)-axis). Higher (or lower) SHAP values indicate a greater impact—positive or negative—on the Random Forest model’s predictions of \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\). RF, along with Gradient Boosting Decision Tree (GBDT), achieved the highest accuracy in predicting Pn. Moreover, [PERSON] et al. [90] used RF to predict the leaf chlorophyll content of wheat (_Triticum aestivum_, L.) from hyperspectral reflectance data. The RF's higher performance found in our study can be attributed to several key features of the RF algorithm; it is an ensemble learning method that builds multiple decision trees during the training phase and aggregates their predictions by averaging. This methodology reduces model variance and mitigates the risk of overfitting, which is particularly important when working with a small dataset. The aggregation approach allows the model to generalize better than individual DTs, which may fit the training data too closely. Moreover, RF excels at capturing complex interactions and nonlinear relationships between the predictor variables and the target variable, as could be the case in remote sensing issues [91]. The bootstrap aggregating (bagging) nature of RF means that each tree is trained on a random sample of the dataset, making the overall model less sensitive to outliers or random variations in the data. This contributes to more stable predictions, as evidenced by the slightly greater consistency in predictions for field A than for field B. On the other hand, in a previous study on \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) prediction of peach trees under similar soil and climate conditions, the SVR algorithm outperformed RF for the prediction of \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) from satellite data (Planet SuperDove) [27]. Considering that, in the present study, SVR performed comparably to RF, as well as for GBM, it is important to emphasize the potential of these algorithms for predicting crop variables. For example, [PERSON] et al. [92] successfully employed SVM to predict \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) in lettuce using proximal sensing data, demonstrating the algorithm's effectiveness in controlled environments. [PERSON] and [PERSON] [22], on the other hand, used XGBoost, a gradient boosting-based method, to predict \(\text{Fv}^{\prime}/\text{Fm}^{\prime}\) from Sentinel-2 images, highlighting the applicability of this approach to large-scale remote sensing data. Given the variability due to differences between crops, environmental conditions, and crop management techniques, conducting a comprehensive comparative analysis of models is essential in studies of this type [93]. Such comparative analyses are useful in ensuring the adaptability and accuracy of models in different agricultural settings. However, it is important to acknowledge the limitations associated with machine learning approaches. One significant limitation is the potential for overfitting, where the model performs exceptionally well on training data but poorly on unseen data.