Burned Olive Trees Identification with a Deep Learning Approach in Unmanned Aerial Vehicle Images

Olive tree orchards are suffering from wildfires in many Mediterranean countries. Following a wildfire event, identifying damaged olive trees is crucial for developing effective management and restoration strategies, while rapid damage assessment can support potential compensation for producers. Moreover, the implementation of real-time health monitoring in olive groves allows producers to carry out targeted interventions, reducing production losses and preserving crop health. This research examines the use of deep learning methodologies in true-color images from Unmanned Aerial Vehicles (UAV) to detect damaged trees, including withering and desiccation of branches and leaf scorching. More specifically, the object detection and image classification computer vision techniques area applied and compared. In the object detection approach, the algorithm aims to localize and identify burned/dry and unburned/healthy olive trees, while in the image classification approach, the classifier categorizes an image showing a tree as burned/dry or unburned/healthy. Training data included true color UAV images of olive trees damaged by fire obtained by multiple cameras and multiple flight heights, resulting in various resolutions. For object detection, the Residual Neural Network was used as a backbone in an object detection approach with a Single-Shot Detector. In the image classification application, two approaches were evaluated. In the first approach, a new shallow network was developed, while in the second approach, transfer learning from pre-trained networks was applied. According to the results, the object detection approach managed to identify healthy trees with an average accuracy of 74%, while for trees with drying, the average accuracy was 69%. However, the optimal network identified olive trees (healthy or unhealthy) that the user did not detect during data collection. In the image classification approach, the application of convolutional neural networks achieved significantly better results with an F1-score above 0.94, either in the new network training approach or by applying transfer learning. In conclusion, the use of computer vision techniques in UAV images identified damaged olive trees, while the image classification approach performed significantly better than object detection.