# Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery
## 3 Related Work  
Unmanned aerial vehicles and machine learning have been a game changer in tree image data collection and analysis that used to rely solely on satellites until recently. More \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**YOLOv5** & \multirow{2}{*}{**Depth\_Multiple**} & \multirow{2}{*}{**Width\_Multiple**} & **ResNet in** & **Convolution** \\
**Architecture** & & & **CSPNet** & **Kernel** \\ \hline Medium & 0.67 & 0.75 & 24 & 768 \\ Small & 0.33 & 0.50 & 12 & 512 \\ Nano & 0.33 & 0.25 & 12 & 256 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of the different YOLOv5 architectures. Figure 1: YOLO version 5 architecture. specifically, in the case of tree image collection and analysis, it has been shown [5] that the two technologies, when paired together, could provide a means to analyse data with significantly lowered costs and at a much greater speed, thus enabling the accurate and timely detection of damages, both caused by weather conditions or pests and disease. The YOLO algorithm is ideal for tree image data detection and classification tasks since it provides a unified solution for those two most common agricultural problems at scale, with the added advantage of fast inference time; something that makes it suitable for real-time applications. The combination of YOLO on UAV image data has been tested on multiple scenarios, with one of the most general of them being the simple detection of fruit trees regardless of species in an orchard. In that research effort, the researchers deployed an improved version of the YOLOv4 algorithm and managed to successfully build a fast and accurate model with 1380 RGB UAV images of varying resolutions that were augmented through standard augmentation methods (change in orientation, brightness and by adding noise) to a total of 3000 images. Their model achieved 98.21% mAP, and 0.936 f1-score for canopy detection, showing that a model like that can be an effective way to address tree detection tasks [27]. Moving to more specific situations, the implementation of YOLO on UAV tree image data has been used to detect only trees of a specific genus [28] or species [29; 30; 31; 32; 33]. In all of those cases, the detection was not anymore a matter of just detecting trees but the task had to be narrowed down to the correct type of tree; success was achieved, indicated by high classification scores with multiple metrics (precision, recall, f1-score) in the above cases. The parameters of the experiments are shown below, in Table 2.  
As seen from the table, the research efforts had a high level of experimental variance, with the number of images used ranging from 125 [30] to 17,343 [33], the UAV flight altitude ranging from 50 m [28] to 200 [33], while the versions of the YOLO algorithm used were v3 [31; 33], v4 [33], and v5 [28; 29; 30; 32; 33], with the last version having many different architectural variants. Therefore, it can be concluded that YOLO is more than capable to tackle problems of detecting very specific objects from UAV images, thus allowing finer detection tasks to be undertaken. Still, the two applications of the YOLO-UAV combination described above only show the algorithm's capacity to detect one class, whether it is trees in general or a specific species. Research has been conducted to highlight the ability of YOLO to perform simultaneous detection of multiple classes. In [6], researchers deployed the YOLO algorithm on UAV images to detect predominantly spruce trees damaged by the bark beetle. In that case, the classes that the algorithm was called to classify the trees into were four: green attack, yellow attack, red attack and grey attack; essentially different levels of tree damage from the bark beetle. YOLO versions 2, 3 and 4 were tested on 400 images taken 120 m from ground level. There was significant class imbalance on the training set (green: 312, yellow: 622, red: 76, grey: 188) and the validation set (green: 202, yellow: 400, red: 20, grey: 61) [6], with the two sets having different class imbalances.