## 3 Results and Discussion  
An experimental study at the investigated pasture was conducted on 21 April 2023. The UAV made a total of 39 overlapping images of parts of the pasture according to the trajectory shown in Figure 4a. Next, using the ArgGIS Pro v. 2.8.6 software, a combined HQ map of the pasture was generated (Figure 4b). According to Step 3 of the developed methodology, the HQ map was used to select a total of 280 regions of interest (ROI) for training and validation purposes (Figure 4c), covering a total of 4,004,502 pixels. Their distribution among the classes is summarized in Table 1. It can be seen that a high imbalance exists between the different classes. The used ROI from the \"Soil\" and \"Stones\" classes are 0.6% and 3.3% of the total number of pixels, respectively, and this mostly corresponds to the actual situation on the investigated pasture. Figure 4: The process of data acquisition and processing: collection of UAV images (**a**); generation of a HQ map of the investigated pasture (**b**); labeling of the training objects (**c**); and generation of a segmented map (**d**). Therefore, this characteristic of the training data should be considered when analyzing the obtained results. Next, steps 4, 5, and 6 of the methodology were repeated numerous times, in order to estimate the optimal segmentation parameters, which returned the highest accuracy results for the object-based classification. The highest results were obtained with the following segmentation characteristics: spectral detail--14, spatial detail--2, and minimum segment size in pixels--50. The generated optimal classification maps using the three object-based algorithms are presented in Figure 5. Similarly, the generated maps using the pixel-based algorithms are shown in Figure 6.  
The accuracy of the six classification maps has been assessed using 10,000 stratified random points, which are randomly distributed within each class, proportionally to their relative area. The overall accuracy of each classification method is presented in Table 2 and is expressed in terms of Cohen's Kappa. The Kappa coefficient is a statistical measure, used \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Number of ROI** & **Number of Pixels** & **Relative Area** \\ \hline Grass & 94 & 1,220,995 & 30.5\% \\ Shrubs & 92 & 2,626,642 & 65.6\% \\ Soil & 32 & 24,452 & 0.6\% \\ Stones & 62 & 132,413 & 3.3\% \\ \hline Total & 280 & 4,004,502 & 100.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: ROI and pixels for each class, that are used for training and validation purposes. Figure 5: Optimal object-based classification maps: MxL. (**a**), RT (**b**); SVM (**c**). Figure 6: Pixel-based classification maps: MxL. (**a**), RT (**b**), SVM (**c**). for evaluating the performance of the trained models. It could be interpreted as follows [47]: k \(\leq\) 0--no agreement; 0.01 \(\leq\) k \(\leq\) 0.20--none to slight agreement; 0.21 \(\leq\) k \(\leq\) 0.40--fair agreement; 0.41 \(\leq\) k \(\leq\) 0.60--moderate agreement; 0.61 \(\leq\) k \(\leq\) 0.80--substantial agreement; 0.81 \(\leq\) k \(\leq\) 1.00--almost perfect agreement. The obtained results show that the object-based RT classification is the best-performing model with Kappa 0.86, followed by the object-based SVM classification with Kappa 0.82. These values indicate that there is almost perfect agreement between the validation data and the predicted data. All other algorithms (pixel-based and object-based) returned worse results, which is indicated by their lower Kappa, ranging between 0.41 and 0.54, i.e., between \"moderate agreement\" and \"substantial agreement\". The obtained results can be compared with those obtained in similar studies, in order to evaluate the efficacy of the proposed approach. In [43], a decision tree model with validation Kappa 0.82 was obtained. These results are practically identical to the optimal SVM model in this study and slightly lower than the optimal RT model.