Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

Pound, Michael P.; Burgess, Alexandra J.; Wilson, Michael H.; Atkinson, Jonathan A.; Griffiths, Marcus; Jackson, Aaron S.; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M.; Murchie, Erik H.; Pridmore, Tony P.; French, Andrew P.

Abstract

Deep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches.

Citation

Pound, M. P., Burgess, A. J., Wilson, M. H., Atkinson, J. A., Griffiths, M., Jackson, A. S., …French, A. P. Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

• Deposit Date: Apr 30, 2019
• Public URL: https://nottingham-repository.worktribe.com/output/1858786
• Publisher URL: https://www.biorxiv.org/content/10.1101/053033v1
• Additional Information: preprint in bioRxiv