## 4 Conclusions  
This study investigates the effectiveness of several machine learning methods for the identification of different types of pasture degradation using UAV-obtained visual spectrum images. The data were obtained from a pasture, located in the Rhodopa mountain near the village of Obichnik, Kardzhali region, Bulgaria. Unlike previous studies, where the degradation was estimated in terms of severity, in this paper it was assessed by classifying the pasture's surface into four categories (grass, stones, soil, and shrubs) and by obtaining their shares. Three pixel-based and three object-based models were trained, and their performance was assessed using Cohen's Kappa. The obtained results indicate that the object-based RT and SVM models perform better, which is indicated by their Kappa coefficients--0.86 and 0.82, respectively. The comparison of their confusion matrices showed that the two models returned very accurate results for the \"Grass\" and \"Shrubs\" classes, with F1 scores ranging between 0.926 and 0.943. The RT model gave better results when identifying \"Stones\" (F1 = 0.660); however, SVM performed better with the identification of \"Soil\" (F1 = 0.788). In general, both algorithms showed that approximately 61% of the investigated pasture's surface can be classified as \"grass\" and approximately 35% can be classified as \"shrubs\", which indicates that, currently, it has light to medium degradation. It is important to continue the monitoring of the pasture's degradation and identify appropriate measures for improving its state. The obtained results also prove that the application of machine learning with visual spectrum data for the identification of pasture degradation can be as effective as other approaches, based on multispectral data and vegetation indices, with or without machine learning. This makes it applicable to small and medium-sized farmers using low-cost UAVs. Such an approach would allow them to optimize pastures and grasslands management and increase their financial results. It is important to verify if the accuracies of the ML algorithms would be influenced by the annual time of UAV inspections and compare them with the results in this study. It is also of interest to investigate the application of satellite-obtained RGB images with machine learning for identification of pasture degradation, in order to compare the results with the UAV-obtained ones. Such comparisons were not performed in this study and are an object for future research. Conceptualization, B.E., A.S., T.Z. and D.S. ; methodology, B.E., I.V., T.K. and N.V.; software, T.K. ; validation, G.S., G.K., M.G., M.T., N.G. and I.D. ; formal analysis, B.E., A.S. and T.Z. ; investigation, D.S., I.D., T.Z., M.G., M.T., N.G., N.V., G.S. and G.K.; resources, A.S., D.S., T.Z. and B.E. ; data curation, A.S., T.Z. and B.E. ; writing--original draft preparation, B.E., I.V., T.K., A.S. and D.S. ; writing--review and editing, B.E., A.S., T.Z. and D.S. ; visualization, B.E. ; supervision, A.S., B.E., T.Z. and D.S. ; project administration, A.S.; funding acquisition, B.E. and A.S. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Area, m\({}^{2}\)** & **Relative Area, \%** \\ \hline \multicolumn{3}{c}{Optimal object-based RT classification} \\ Grass & 6065 & 61.50 \\ Stones & 102 & 1.03 \\ Soil & 290 & 2.94 \\ Shrubs & 3405 & 34.53 \\ \hline \multicolumn{3}{c}{Optimal object-based SVM classification} \\ Grass & 6037 & 61.21 \\ Stones & 221 & 2.24 \\ Soil & 111 & 1.13 \\ Shrubs & 3493 & 35.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of the pasture’s degradation using the selected optimal models. **Funding:** This research was funded by the Ministry of Education and Science of Bulgaria under the National Research Program \"Intelligent Animal Husbandry\", grant number \(J\)01-62/18.03.2021. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.