Super-Resolution Reconstruction-Based Plant Image Classification Using Thermal and Visible-Light Images

Few studies have been conducted on thermal plant images. This is because of the difficulty in extracting and analyzing various color-related patterns and features from the plant image obtained using a thermal camera, which does not provide color information. In addition, the thermal camera is sensitive to the surrounding temperature and humidity. However, the thermal camera enables the extraction of invisible patterns in the plant by providing external and internal heat information. Therefore, this study proposed a novel plant classification method based on both the thermal and visible-light plant images to exploit the strengths of both types of cameras. To the best of our knowledge, this study is the first to perform super-resolution reconstruction using visible-light and thermal plant images. Furthermore, a method to improve the classification performance through generative adversarial network (GAN)-based super-resolution reconstruction was proposed. Through the experiments using a self-collected dataset of thermal and visible-light images, our method shows higher accuracies than the state-of-the-art methods.

Automated summary

This study proposes a novel plant classification method based on both thermal and visible-light images, which shows higher accuracies than existing methods. It is the first study to perform super-resolution reconstruction using visible-light and thermal plant images, and a method to improve classification performance is proposed using generative adversarial network (GAN)-based super-resolution reconstruction.