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@ARTICLE{Minervini:276445,
      author       = {Minervini, Massimo and Fischbach, Andreas and Scharr, Hanno
                      and Tsaftaris, Sotirios A.},
      title        = {{F}inely-grained annotated datasets for image-based plant
                      phenotyping},
      journal      = {Pattern recognition letters},
      volume       = {81},
      issn         = {0167-8655},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2015-06884},
      pages        = {80–89},
      year         = {2015},
      abstract     = {Image-based approaches to plant phenotyping are gaining
                      momentum providing fertile ground for several interesting
                      vision tasks where fine-grained categorization is necessary,
                      such as leaf segmentation among a variety of cultivars, and
                      cultivar (or mutant) identification. However, benchmark data
                      focusing on typical imaging situations and vision tasks are
                      still lacking, making it difficult to compare existing
                      methodologies. This paper describes a collection of
                      benchmark datasets of raw and annotated top-view color
                      images of rosette plants. We briefly describe plant
                      material, imaging setup and procedures for different
                      experiments: one with various cultivars of Arabidopsis and
                      one with tobacco undergoing different treatments. We proceed
                      to define a set of computer vision and classification tasks
                      and provide accompanying datasets and annotations based on
                      our raw data. We describe the annotation process performed
                      by experts and discuss appropriate evaluation criteria. We
                      also offer exemplary use cases and results on some tasks
                      obtained with parts of these data. We hope with the release
                      of this rigorous dataset collection to invigorate the
                      development of algorithms in the context of plant
                      phenotyping but also provide new interesting datasets for
                      the general computer vision community to experiment on. Data
                      are publicly available at
                      http://www.plant-phenotyping.org/datasets.},
      cin          = {IBG-2},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582) / 583 - Innovative
                      Synergisms (POF3-583)},
      pid          = {G:(DE-HGF)POF3-582 / G:(DE-HGF)POF3-583},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000383822500011},
      doi          = {10.1016/j.patrec.2015.10.013},
      url          = {https://juser.fz-juelich.de/record/276445},
}