Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light Images

There have been various studies conducted on plant images. Machine learning algorithms are usually used in visible light image-based studies, whereas, in thermal image-based studies, acquired thermal images tend to be analyzed with a naked eye visual examination. However, visible light cameras are sensitive to light, and cannot be used in environments with low illumination. Although thermal cameras are not susceptible to these drawbacks, they are sensitive to atmospheric temperature and humidity. Moreover, in previous thermal camera-based studies, time-consuming manual analyses were performed. Therefore, in this study, we conducted a novel study by simultaneously using thermal images and corresponding visible light images of plants to solve these problems. The proposed network extracted features from each thermal image and corresponding visible light image of plants through residual block-based branch networks, and combined the features to increase the accuracy of the multiclass classification. Additionally, a new database was built in this study by acquiring thermal images and corresponding visible light images of various plants.

Automated summary

This study focuses on overcoming the limitations of visible light cameras and thermal cameras in plant studies based on thermal images. By using thermal images and corresponding visible light images to extract features, the accuracy of multi-class classification is improved, and a new database is built.