# Monitoring Safron Crops with UAVs
## 4 Results
### Pixel Based Image Analysis  
In this subsection, we present the results of pixel-based analysis. Initially, the images collected from the UAV flights (performed at 120 m height) were processed with the help of the photogrammetry techniques presented in Section 3.4 to produce (a) RGB orthophotos, (b) orthophotos depicting NDVI, and (c) ground-truth images. The machine learning model was trained based on the pictures demonstrated in Figure 6, where every instance represents a pixel of the cultivation. Finally, we can calculate the number and position of the flowers of saffron and weeds and detect mouse traces. This cultivation was preprocessed with photogrammetry techniques to produce the desired products. Figure 5(a) depicts the flourished Field 1 in the spectrum of RGB, and it was developed from the collected shots of the multiple flights of UAV, as presented in Table 1. Figure 5(b) depicts Field 1 in the spectrum of NDVI, produced from multispectral images, where the green color represents a higher NDVI value, followed by the yellow color. The soil has a mostly yellow color, and certain points in the image with a slightly red color depict the existence of vegetation. Finally, Figure 5(c) represents the actual category in which a pixel is classified. This picture was magnified so as to highlight the different categories of the cultivation. The data were summarized in one  
Figure 6: (a) RGB Orthophoto, (b) NDVI, and (c) ground-truth image produced for Field 1. Section 3.5, that was used to train the algorithms. The different values of RGB, NDVI, and the class of each pixel were joined in it. The results after applying the two algorithms show that the Random Forest algorithm is the most efficient method, presenting an overall accuracy approaching 100% (Table 3). In parallel, the Multilayer Perceptron algorithm has an overall accuracy of 95%. Consequentially, the models are able to detect the differences between the soil, the weed, and the Saffron flowers, as well as the traces left by the mammals. Another important performance metric that can be used to evaluate the applied algorithms is the average recall of the predicted values. That is due to the fact that, in the analysis, we evidently use imbalanced datasets (the cultivation presents large instances of soil data compared to instances depicting, for example, weeds or the existence of mammals). As a result, the dataset contains a large volume of soil class data compared to the other categories. In that case, Random Forest has shown a much more efficient performance for the categories we are interested in, such as saffron flowers, weeds, and mouse traces. On the contrary, the MLP classifier overlooks classes such as weeds or mammal traces by automatically classifying them in the field category, which is in the majority. A more analytic report of the behavior of the two algorithms is presented in Table 4, where each row presents the accuracy, the precision, and the recall of the estimation models derived to predict animal intrusions, weeds, and saffron flowers. These results demonstrate not only the efficiency and performance of the algorithms in this type of cultivation but, nevertheless, the perspective of machine learning in such data. To be more detailed, Random Forest is able to give distinct results in every category, so as to, in future implementations, predict the appearance of weeds or even spots on the crop with the highest production. Moreover, the MLP algorithm gives clear results regarding the further preprocessing of the images, in order to make more distinct the data that the algorithm will accept as input. Its inability to locate traces of mammals and weeds leads us to the conclusion that there is room for even more detailed study at that stage. This can be seen in Figure 7b, where the RF which is the most accurate locates more weed and mouse points in the same area opposed to MLP.