## 5 Discussion
Nevertheless, as presented in Figure 10, the period of June appears to be the most important in the detection of the plant species in our experiment. The reason for that is probably that the flowering, fruit development, and vegetation growth (Figure 2) appear for different plant species in that period. It is foreseen that the period of harvesting and sowing can also be the most important, since this is different for different plant species. Based on the graphical representation of the classification results in Figure 10 and the PA and UA presented in Appendix A, it can be observed that in most cases the classification of corn and wheat fields is highly accurate. The reason for this may be the characteristics and unique late sowing time of corn in the study area. Sunflower, potato, and rye are characterized by a significantly lower detection accuracy. The reason for this may be the concentration of these plant fragments in the total investigated study area and unbalanced data. In 2020, the investigated research area was significantly dominated by cereal crops. When the samples were randomly generated and split into training and testing datasets, it may have turned out that most of these points came from beetroot, maize, and winter wheat; thus, the accuracy of the automatic detection of those species was higher. It is possible that these indexes could have shown greater efficiency in an area where more training samples could have been created for other plant species for a more balanced representation. Additionally, it is worth considering that the contribution of each crop type in the training samples will be similarly large. Furthermore, when observing the classification results in Figure 11, we can see that a more accurate classification was presented in the area of training field location, whereas for the areas located further away from the training samples the classification results were noisier. This is mostly associated with the autocorrelation aspect introduced by the location of training within the training fields. This aspect should also be considered, because this procedure of splitting ground truths into training and testing data is more practical than a random sampling design (training fields/points evenly distributed across the whole study area). This is because it is easier to capture training samples during the field trips or farmers' declarations in one concise study area rather than random and evenly distributed samples across the investigated study area (e.g., whole province or commune). This issue should also be taken into account when crop mapping is performed for large areas. For  
Figure 12: Time series of the NDVI and MCARI for different types of crops, calculated as average values of all fields with harvesting dates marked with circles. such analysis, it will be valuable to have training samples in various locations to properly describe remote sensing signals associated with various plant species. In this context, it is also worth considering that object-based image analysis (OBIA) does limit single false classification result or pixels located inside the field. Investigation of the field as a whole will be more advantageous. Taking the classification accuracy measures into account, three indexes were the most satisfactory: MCARI, NDI45, and WDVI. These indices allowed for the accurate detection of fields not only of wheat and maize, but also of other cultivated plant species such as sunflower, potato, and betroot (Appendix A). The difference between the behavior of these indexes and the others mentioned may concern their sensitivity to various crop species, the period of the multitemporal Sentinel-2A data, and local characteristics. Classification performed on the basis of different combinations of PCA components has not provided higher accuracies. PCA was performed using the twelve vegetation indices; therefore, imperfections in the improper adjustment of the indices may have outweighed the advantages, resulting in lower accuracy in the detection of different plant species.