Harnessing synthetic data for enhanced detection of Pine Wilt Disease: An image classification approach

Abstract

Pine Wilt Disease (PWD) is a devastating disease that affects forest ecosystems and has no known cure. Early detection is essential for suppressing infections of PWD. Recent efforts have focused on leveraging deep learning-based algorithms for the early detection of PWD. However, data collection, labeling, and quality assurance for such a method are costly and challenging. Particularly for PWD, the data collection period is limited, restricting the diversity of the dataset. To overcome these challenges, this paper introduces a virtual forest considering PWD created using 3D rendering tools, from which we built a synthetic dataset. Furthermore, to ensure the resemblance of the synthetic data to the real data, we employed Image-to-Image (I2I) translation techniques. We used the EfficientNetv2-S model to compare the results from each dataset. We were able to confirm the potential of our model trained solely on the PWD synthetic dataset for real PWD detection. Moreover, the model trained on an ensemble comprising both real and synthetic data exhibited improved performance, achieving an F1 Score of 92.88%. Using the I2I translation technique for an ensemble of real and synthetic data also demonstrated enhanced performance. This result confirms the validity and practicality of the synthetic data proposed in this study. The findings of this study are expected to contribute to the broader forest ecosystem preservation and agricultural management by extending to other forest disease detection and agricultural fields. Details of our dataset and code are available at https://github.com/dydgns2017/PWD-Synthetic-Dataset.