Skip to main content

Research Repository

Advanced Search

Outputs (4)

Root Gap Correction with a Deep Inpainting Model (2018)
Conference Proceeding
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.

Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting (2018)
Journal Article
Giuffrida, M. V., Doerner, P., & Tsaftaris, S. A. (2018). Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting. Plant Journal, 96(4), 880-890. https://doi.org/10.1111/tpj.14064

Direct observation of morphological plant traits is tedious and a bottleneck for high‐throughput phenotyping. Hence, interest in image‐based analysis is increasing, with the requirement for software that can reliably extract plant traits, such as lea... Read More about Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting.

Citizen crowds and experts: observer variability in image-based plant phenotyping (2018)
Journal Article
Giuffrida, M. V., Chen, F., Scharr, H., & Tsaftaris, S. A. (2018). Citizen crowds and experts: observer variability in image-based plant phenotyping. Plant Methods, 14(1), Article 12 (2018). https://doi.org/10.1186/s13007-018-0278-7

Background: Image-based plant phenotyping has become a powerful tool in unravelling genotype–environment interactions. The utilization of image analysis and machine learning have become paramount in extracting data stemming from phenotyping experime... Read More about Citizen crowds and experts: observer variability in image-based plant phenotyping.

Leveraging multiple datasets for deep leaf counting (2018)
Conference Proceeding
Dobrescu, A., Giuffrida, M. V., & Tsaftaris, S. A. (2018). Leveraging multiple datasets for deep leaf counting. . https://doi.org/10.1109/ICCVW.2017.243

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.