부산대학교 도서관

Maintaining the health of forests is crucial, and early detection of plant diseases like Pine Wilt Disease (PWD) is vital for this purpose. Recently, research in applying deep learning-based algorithms to forestry and agriculture has been gaining momentum. However, constructing a dataset is an essential prerequisite for utilizing these technologies. This study aims to detect PWD using deep learning-based object detection algorithms and synthetic data generation techniques. We use 3D rendering software, Blender, and Unreal Engine 5, to create synthetic data. Additionally, this research applies and compares three different training strategies to improve the learning model's performance and assess the validity of the synthetic data: 1) Training on synthetic and real data independently, 2) Ensemble learning using both data types, and 3) Pre-training the model with synthetic data and fine-tuning it with real data. Experimental results showed that the third method exhibited the best performance. The outcomes of this research are expected to assist in building large-scale datasets for training deep learning algorithms in the field of forestry.