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@ARTICLE{Tsaftaris:820456,
      author       = {Tsaftaris, Sotirios A. and Minervini, Massimo and Scharr,
                      Hanno},
      title        = {{M}achine {L}earning for {P}lant {P}henotyping {N}eeds
                      {I}mage {P}rocessing},
      journal      = {Trends in plant science},
      volume       = {21},
      number       = {12},
      issn         = {1360-1385},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2016-05766},
      pages        = {989–991},
      year         = {2016},
      abstract     = {We found the article by Singh et al. [1] extremely
                      interesting because it introduces and showcases the utility
                      of machine learning for high-throughput data-driven plant
                      phenotyping. With this letter we aim to emphasize the role
                      that image analysis and processing have in the phenotyping
                      pipeline beyond what is suggested in [1], both in analyzing
                      phenotyping data (e.g., to measure growth) and when
                      providing effective feature extraction to be used by machine
                      learning. Key recent reviews have shown that it is image
                      analysis itself (what the authors of [1] consider as part of
                      pre-processing) that has brought a renaissance in
                      phenotyping [2]. At the same time, the lack of robust
                      methods to analyze these images is now the new bottleneck 3,
                      4 and 5 – and this bottleneck is not easy to overcome. As
                      the following aims to illustrate, it is coupled not only to
                      the imaging system and the environment but also to the
                      analytical task at hand, and requires new skills to help
                      deal with the challenges introduced.A successful
                      high-throughput image-based phenotyping system starts with
                      the imaging approach itself. The choices are to image many
                      plants simultaneously or one plant at a time, requiring
                      movable systems to bring the plant to the camera or vice
                      versa. These systems add cost but have the benefit of
                      isolating the object of interest. In turn, this simplifies
                      its processing, for example facilitating object
                      segmentation, in other words the image analysis process
                      isolating the plant from background (e.g., soil), as Figure
                      1A shows (many image-processing tasks are related to how we
                      perceive and analyze an object of interest, such as
                      segmentation, detection, tracking, and many others).},
      cin          = {IBG-2},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582)},
      pid          = {G:(DE-HGF)POF3-582},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000389098000001},
      pubmed       = {pmid:27810146},
      doi          = {10.1016/j.tplants.2016.10.002},
      url          = {https://juser.fz-juelich.de/record/820456},
}