ASHS 2025 Conference

Disaster events such as hurricanes strongly affect pecan crop production due to fallen trees, which generate a significant impact on production areas. An appropriate method to monitor these effects is by counting the fallen trees. This monitoring is commonly performed by a field team. Although accurate, this approach is time-consuming, costly, and non-scalable. Consequently, an image-based approach emerges as a timely opportunity. For instance, aerial images captured by unmanned aerial vehicles (UAVs) present high spatial resolution and coverage, contributing to tree identification. However, relying solely on images remains a laborious and time-consuming task. Consequently, the development of object detection models to automatically identify and count fallen trees emerges as an unprecedented and dynamic method. Therefore, we aimed to integrate UAV image technology and the YOLOv12 detection model for detecting fallen trees in pecan fields. On September 27th, 2024, the hurricane Helene crossed the State of Georgia, USA. On October 7th, 2024, we captured RGB images using a multirotor UAV. We captured high-resolution (~3 cm/px) in eight pecan fields. The images were processed to generate one orthomosaic for each field. Consequently, we counted the fallen trees using assisted image processing for ground truth data. Together, the pecan fields presented more than 200 fallen trees. Subsequently, we trained the YOLOv12 model to automatically identify the fallen trees. Initially, we manually labeled all the fallen trees as “Fallen”. Afterward, we split our dataset into training (70%), validation (15%), and testing (15%). To enhance model performance and generalizability, we implemented data augmentation for the training dataset. As a result, the model achieved identification metrics of fallen trees higher than 70%. Certainly, this approach presents a great opportunity to measure the effects caused by hurricanes, giving farmers the ability to make faster and better decisions regarding their fields. Ultimately, these findings support precision agriculture practices and introduce pecan crops into this context, further strengthening the introduction of technologies into the world of specialty crops.