Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping
Michael P. Pound,Jonathan A. Atkinson,Alexandra J. Burgess,Michael Wilson,Marcus Griffiths,Aaron S. Jackson,Adrian Bulat,Georgios Tzimiropoulos,Darren M. Wells,Erik H. Murchie,Tony P. Pridmore,Andrew P. French +11 more
TLDR
Deep learning–based phenotyping is shown to have very good detection and localization accuracy in validation and testing image sets and to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines.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.read more
Citations
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Deep learning in agriculture: A survey
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Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives.
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Uncovering the hidden half of plants using new advances in root phenotyping
TL;DR: In this paper, the authors describe how advances in imaging and sensor technologies are making root phenomic studies possible However, methodological advances in acquisition, handling and processing of the resulting "big-data" is becoming increasingly important.
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