Deep learning technique to monitor canopy surface inside a greenhouse

The real challenge when 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 humidity of the greenhouse, and the outside environment.

This study proposed TheLNet270v1, a thermal leaf network with 270 layers version 1, to recover the leaf canopy from its background in real time with higher accuracy than previous systems. The proposed network had an accuracy of 91% (mean boundary F1 score or BF score) to distinguish canopy pixels from background pixels and then segment the image into two classes: leaf and background. The team evaluated the classification (segment) performance by using more than 13,766 images and obtained 95.75% training and 95.23% validation accuracies without overfitting issues.

This research aimed to develop a deep learning technique for the automatic segmentation of thermal images to continuously monitor the canopy surface temperature inside a greenhouse.

Read the complete research at www.researchgate.net.

Islam, Md & Nakano, Yuka & Lee, Unseok & Tokuda, Keinichi & Kochi, Nobuo. (2021). TheLNet270v1 -A Novel Deep- Network Architecture for the Automatic Classification of Thermal Images for Greenhouse Plants. Frontiers in Plant Science. 12. 1282. 10.3389/fpls.2021.630425. 


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