DeepPod: a convolutional neural network based quantification of fruit number in Arabidopsis.

Journal: GigaScience
PMID: 32129846

Abstract

BACKGROUND: High-throughput phenotyping based on non-destructive imaging has great potential in plant biology and breeding programs. However, efficient feature extraction and quantification from image data remains a bottleneck that needs to be addressed. Advances in sensor technology have led to the increasing use of imaging to monitor and measure a range of plants including the model Arabidopsis thaliana. These extensive datasets contain diverse trait information, but feature extraction is often still implemented using approaches requiring substantial manual input.

Authors

  • Azam Hamidinekoo
    Department of Computer Science, Aberystwyth University, United Kingdom. Electronic address: azh2@aber.ac.uk.
  • Gina A Garzón-Martínez
    National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, Ceredigion SY233EB, UK.
  • Morteza Ghahremani
    Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion SY233DB, UK.
  • Fiona M K Corke
    National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, Ceredigion SY233EB, UK.
  • Reyer Zwiggelaar
    Department of Computer Science, Aberystwyth University, Ceredigion, United Kingdom.
  • John H Doonan
    National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, Ceredigion SY233EB, UK.
  • Chuan Lu
    Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom.

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