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

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

doi:10.1101/053033

bioRxiv · 10.1101/053033

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

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

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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.

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Michael P Pound, Alexandra J Burgess, Michael H Wilson, Jonathan A Atkinson, Marcus Griffiths, Aaron S Jackson, Adrian Bulat, Georgios Tzimiropoulos, Darren M Wells, Erik H Murchie, Tony P Pridmore, Andrew P French. 2016-05-12. Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping. https://doi.org/10.1101/053033

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