Ghaf Tree Detection from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Wang, Guoxu; Leonce, Andrew; Edirisinghe, E.A.; Khafaga, Tamer; Simkins, Gregory; Yahya, Umar; Sher Shah, Moayyed

Abstract

The Ghaf is a drought-resilient tree native to some parts of Asia and the Indian Subcontinent, including the United Arab Emirates (UAE). To the UAE, the Ghaf is a national tree, and it is regarded as a symbol of stability and peace due to its historical and cultural importance. Due to increased urbanization and infrastructure development in the UAE, the Ghaf is currently considered an endangered tree, requiring protection. Utilization of modern-day aerial surveillance technologies in combination with Artificial Intelligence (AI) can particularly be useful in keeping count of the Ghaf trees in a particular area, as well as continuously monitoring unauthorized use to feed animals and to monitor their health status, thereby aiding in their preservation. In this paper, we utilize one of the best Convolutional Neural Networks (CNN), YOLO-V5, based model to effectively detect Ghaf trees in images taken by cameras onboard light-weight, Unmanned Aircraft Vehicles (UAV), i.e. drones, in some areas of the UAE. We utilize a dataset of over 3200 drone captured images partitioned into data-subsets to be used for training (60%), validation (20%), and testing (20%). Four versions of YOLO-V5 CNN architecture are trained using the training data subset. The validation data subset was used to fine tune the trained models in order to realize the best Ghaf tree detection accuracy. The trained models are finally evaluated on the reserved test data subset not utilized during training. The object detection results of the Ghaf tree detection models obtained by the use of four different sub-versions of YOLO-V5 are compared quantitatively and qualitatively. YOLO-V5x model produced the highest average detection accuracy of 81.1%. In addition, YOLO-V5x can detect and locate Ghaf trees of different sizes moreover in complex natural environments and in areas with sparse distributions of Ghaf trees. The promising results presented in this work offer fundamental grounds for AI-driven UAV applications to be used for monitoring the Ghaf tree in real-time, and thus aiding in its preservation.

• Conference Name: 2023 International Symposium on Networks, Computers and Communications (ISNCC)
• Conference Location: Doha, Qatar
• Start Date: Oct 23, 2023
• End Date: Oct 26, 2023
• Acceptance Date: Oct 23, 2023
• Online Publication Date: Nov 27, 2023
• Publication Date: Oct 23, 2023
• Deposit Date: Dec 11, 2023
• Publicly Available Date: Dec 11, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• ISBN: 979-8-3503-3560-6
• DOI: https://doi.org/10.1109/isncc58260.2023.10323713