## 3 Results and Discussion
### Remote Sensing Applications
#### 3.4.2 Imagery Classification
Two DL models, long short-term memory (LSTM) and Conv1D, were compared with ML classifiers: Extreme Gradient Boosting (XGBoost), RF, SVM. LSTM had the lowest performance (82.41% OA), while XGBoost outperformed others (OA = 84.17%). The Conv1D-based model showed the best results, achieving an OA of 85.54%. Among the studies using only the MAV platform, only one study employing ML and DL models was considered. [PERSON] et al. [56] explored crop monitoring using UAVSAR data, emphasizing polarimetric signatures for crops like almonds, walnuts, alfalfa, winter wheat, corn, sunflowers, and tomatoes. They found that polarimetric decomposition parameters provided superior classification accuracy (up to 97.48% with SVM) compared to linear polarizations. The study underscores L-band SAR's efficacy in precise plant monitoring and classification. In studies employing UAV platforms, a study focused on the modification of the RF model was identified. [PERSON] et al. [57] classified tree species, including almond orchards, to map agricultural land cover. They adjusted the RF classifier in out-of-bag cross-validation using patch-based splits. The modified RF algorithm yielded accurate results without overestimation, offering a less biased accuracy estimate compared to images with a different approach. Regarding other studies using UAV and ML models, three studies were identified. In a study performed by [PERSON] et al. [58], the authors classified almond orchards using MSP UAV imagery and VIs. They considered 11 VIs and analyzed 593 data points. Among six ML algorithms, SVM, k-nearest neighbor (kNN), and linear discriminant analysis (LDA) were chosen. Results indicated that increasing the number of VIs initially improved accuracy, with SVM showing the best performance overall (96%). On the other hand, [PERSON] et al. [19] applied ML for almond cultivar classification. SVM and RF stood out with 76% and 74% OAs using VIs and spectral bands. Adding the canopy height model (CHM) improved results, yielding 88% and 84% OAs for RF and XGBoost. The best performance, a 99% OA, was achieved by RF and XGBoost using VIs, CHM, and tree crown area (TCA). This study emphasizes the importance of feature selection and the efficacy of ML classifiers with RS data for precise almond cultivar classification. [PERSON] et al. [59], in turn, developed a method for automated phenotyping of permanent crops, in order to increase the number of progeny that can be evaluated. The study used data normalization to reduce variance in a dataset, and a subset of the data was tested for classification accuracy using principal component analysis (PCA) and LDA (OA: 92%). The results showed that the method was effective in classifying almond varieties based on their reflectance spectra. Finally, among the studies using a UAV platform and DL models, the study by [PERSON] et al. [60] was identified. The authors proposed a methodological framework for detecting individual tree's properties using CNN and visible indices. The Mask R-CNN model was used for detecting and mapping each individual tree morphometrical properties, such as height and crown width. The results showed that the proposed methodology is stable and scalable across several zones around the globe. Looking at Figure 10a, satellite is the most used platform in 50% of the studies, followed by UAV in 37% and MAV in 13%. Figure 10b demonstrates consistently high OA results, regardless of whether ML or DL classification models were employed. Some studies even show cases where ML models performed equally well or even better than DL models [53; 56]. This observation is noteworthy given the resource-intensive nature of DL models, coupled with their often-complex operational mechanisms (black box models). In contrast, ML models, such as decision trees (DT) and RF, offer a high level of interpretability, allowing for a clear understanding of the decision-making process [61].