# Monitoring Safron Crops with UAVs
## 4 Results
### Object-Based Image Analysis  
In this section, the results of the object-based image analysis are presented. This method uses, in addition to the image types shown in Figure 4, the data from the raster file generated by the segmentation algorithm applied to the RGB image of the crop (Figure 9). This image shows the different objects that the segmentation algorithm (3.5-RSGISLib) detects in the RGB orthophoto. In this way, Figure 9 shows all the alternative objects found on the cropping area. The result of applying the OBIA algorithm is a new dataset where each instance corresponds to an object recognized by the segmentation algorithm. This approach is recognized to be much more efficient and less time-consuming. The difference in the data set is that each object recognized by the Shepherd algorithm groups all the pixels in bins of the same category. From a set of millions of pixels, the algorithm gives us a few thousand different objects which we categorized into the four respective categories of interest (i.e., traces of animals, weeds, saffron flowers, and soil). At the same time, we calculated the average of RGB and NDVI values of all the pixels that belong to the same category. In this way, a smaller set of data that was much more goal-oriented was created. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Soil** & **Weed** & **Saffron** & **Mammal** \\ \hline RF & 83.6\% & 0.4\% & 8\% & 8\% \\ \hline
**MLP** & 91.65\% & 0.2\% & 8\% & 0.15\% \\ \hline
**Real Data** & 83.6\% & 0.4\% & 8\% & 8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Rate of each category. Figure 7: Difference between Random Forest and Multilayer Perception. (a) MLP predicted magnified image, (b) RF predicted image. The results of the object-based image analysis, as described in Section 3.5, showed an overall accuracy of 80% for RF and 76% for MLP (Table 6). Nevertheless, both models had low average recall rates. Specifically, the average recall of RF was 40% and that of MLP was 35%. This low performance of the two models is probably due to an incorrect configuration of the segmentation algorithm to detect the small flowers of saffron. Figure 8 shows the predicted images produced by both algorithms. Table 7 presents the evaluation metrics for every estimation model produced by the two algorithms applied. We can observe that both algorithms, in the context of object-based analysis, present limited estimation power. This is a consequence of the small size of our data and the fact that the original pictures where not precisely annotated by the experts (saffron cultivators). Figure 8: Predicted images. (**a**) RGB original image, (**b**) Ground-truth data, (**c**) MLP prediction image, (**d**) RF prediction image. At the same time, we must keep in mind that saffron flowers, weeds, and even mammal tracks are small objects for this dataset, so a segmentation method such as this one may not be able to detect them, since it has not yet been applied to plants with such a small number of pixels; instead, it has been applied to buildings, trees, and geographic areas that objectively span more pixels [30; 31; 32; 33]. However, we can conclude that the RF classifier was much more efficient than the MLP. This is even more evident from the predicted image, as shown in Figure 10. The MLP classifier fails to classify the important categories, resulting in a blackened image showing a non-flowering crop. Yet another piece of proof is presented in Table 8, where the rate of each category of RF and MLP deviates from the real data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** \\ \hline
**RF** & 80\% & 61\% & 65\% \\ \hline
**MLP** & 76\% & 56\% & 57\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Object-based image analysis results.