## 3 Results and Discussion
### Remote Sensing Applications
#### 3.4.2 Imagery Classification  
Remote sensing imagery classification is a crucial process for extracting meaningful insights from remotely sensed data. This involves categorizing pixels or regions within an image based on their spectral, spatial, and temporal characteristics. Advanced techniques, including maximum likelihood classification (MLC) and support vector machine (SVM), are commonly applied for supervised classification. Unsupervised methods like K-means clustering identify natural clusters in the absence of class labels. DL, particularly convolutional neural networks (CNN) and recurrent neural networks (RNN), have transformed the field, with CNN focusing on spatial pattern recognition and RNN addressing temporal dependencies in time-series images. Integration of data from various sensors and time points, along with robust evaluation metrics like OA and receiver operating characteristics curve (ROC), further enhances classification accuracy. This evolving field continues to benefit from technological advancements, promising increasingly refined and efficient classification processes in the future [46; 47]. In this subsection, several studies on the classification of RS imagery in almond orchards are analyzed. In the studies using only the satellite platform, it was found that machine learning (ML) models were implemented in two studies. [PERSON] et al. [48] used satellite images to study land cover changes. They found that almond orchards expanded in maquis areas due to increased demand, and terraces were developed on sloping lands. On the other hand, [PERSON] et al. [49] studied the use of fully polarimetric UAVSAR data for crop classification in California's Central Valley. They applied Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions, finding that polarimetric features and ML achieved accurate classification (random forest (RF) = 96% for almond). Regarding studies using only the satellite platform and DL models, three studies were identified. [PERSON] and [PERSON] [50] investigated the effectiveness of radar texturing and sensor fusion approaches using the maximum likelihood decision rule (MLD). The fusion of Landsat and radar textures resulted in an OA of 97%, illustrating the advantages of sensor integration. On the other hand, [PERSON] [51] efficiently mapped crops in U.S. farming regions using Google Street View (GSV) images and a CNN. Their approach demonstrated high reliability, achieving accurate crop image classification (92-97% OA, 95% for almond crops). [PERSON] [52], in turn, attempted to classify five different crop types in Fresno county, California, using RapidEye satellite images and the USDA/NASS reference data. The authors employed NNs, CNNs and RNNs to train the multitemporal satellite images and achieve high classification accuracy (NN: 89%, CNN: 94%, RNN: 91%). Considering studies using only the satellite platform and both ML and DL models, only one study was identified. [PERSON] et al. [53] used RS to identify nine summer crops from ASTER satellite imagery, combining OBIA with advanced ML. Woody crops (Almond, Walnut, Vineyard) and herbaceous crops were considered. Evaluating decision tree (DT), logistic regression (LR), SVM, and multilayer perceptron (MLP) methods, MLP and SVM stood out, achieving a high overall accuracy of 88%, surpassing LR (86%) and outperforming DT (79%). Regarding the studies combining the use of satellite and MAV platforms, only one study using DL models was reported. [PERSON] et al. [54] presented the iterative deep learning (IDL) framework for precise crop classification in high-resolution agricultural RS. Combining a region proposal network (RPN) and CNN, IDL categorizes low-level crop (LLC) and high-level crop (HLC). Experimental results showcase IDL effectiveness, achieving an average OA of 92% for the almond crop. On the other hand, when analyzing studies that integrate satellite and UAV platforms, only one was reported, including ML and DL models. [PERSON] et al. [55] developed a DL-based classification system for summer crops.