Outputs (2)

Root Gap Correction with a Deep Inpainting Model (2018)
Presentation / Conference Contribution
Chen, H., Giuffrida, M. V., Doerner, P., & Tsaftaris, S. A. (2018). Root Gap Correction with a Deep Inpainting Model.

Imaging roots of growing plants in a non-invasive and affordable fashion has been a long-standing problem in image-assisted plant breeding and phenotyping. One of the most affordable and diffuse approaches is the use of mesocosms, where plants are gr... Read More about Root Gap Correction with a Deep Inpainting Model.

Leveraging multiple datasets for deep leaf counting (2018)
Presentation / Conference Contribution
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2017, October). Leveraging multiple datasets for deep leaf counting. Presented at 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy

The number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on l... Read More about Leveraging multiple datasets for deep leaf counting.