TheLNet270v1 – A Novel Deep-Network Architecture for the Automatic Classification of Thermal Images for Greenhouse Plants - Islam, Md. Parvez; Nakano, Yuka; Lee, Unseok; Tokuda, Keinichi; Kochi, Nobuo (Frontiers in Plant Science, 2021)
The real challenge for separating leaf pixels from background pixels in thermal images is associated with various factors such as the amount of emitted and reflected thermal radiation from the targeted plant; absorption of reflected radiation by the greenhouse’s humidity; and the outside environment. We proposed TheLNet270v1 (thermal leaf network with 270 layers version to recover the leaf canopy from its background in real-time with higher accuracy than previous systems. Furthermore, the proposed network has an accuracy of 91% (mean boundary F1 score or BF score) for distinguishing canopy pixels from background pixels and then segmenting the image into two classes: leaf and background. We evaluated the classification (segment) performance by using more than 13766 images and obtained 95.75% training and 95.23% validation accuracies without overfitting issues. The purpose of this research is to develop a deep learning technique for the automatic segmentation of thermal images to continuously monitor the canopy surface temperature inside a greenhouse.