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@ARTICLE{Stavness:1022057,
      author       = {Stavness, Ian and Giuffrida, Valerio and Scharr, Hanno},
      title        = {{E}ditorial: {C}omputer vision in plant phenotyping and
                      agriculture},
      journal      = {Frontiers in artificial intelligence},
      volume       = {6},
      issn         = {2624-8212},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2024-01194},
      pages        = {1187301},
      year         = {2023},
      abstract     = {Plant phenotyping is the process of identifying a plant’s
                      structural and functional characteristics. Plant phenotyping
                      is used by plant scientists to uncover mechanisms of plant
                      physiology, eg, to characterize how plants respond to biotic
                      and abiotic stress. Phenotyping is also used by plant
                      breeders to evaluate cultivars in a plant population for
                      beneficial characteristics in order to inform the selection
                      of progeny to move forward within a multiyear breeding
                      process. In an attempt to reduce the time and cost required
                      to phenotype large plant populations, image-based
                      phenotyping has become popular over the past 10 years.
                      Extracting phenotypic information from images of plants and
                      crops presents a number of challenging real-world computer
                      vision problems, such as analyzing images with highly
                      self-similar repeating patterns and analyzing densely packed
                      and occluded plant organs. This Research Topic is associated
                      with the 7th Computer Vision in Plant Phenotyping and
                      Agriculture (CVPPA) workshop, which was held at the
                      International Conference on Computer Vision (ICCV) on 11
                      October 2021. During the workshop, 18 full-length papers and
                      14 extended abstracts were presented. This Research Topic
                      includes three papers that are extended versions of
                      abstracts presented at the workshop. The Research Topic also
                      includes 11 new articles that fall under the general scope
                      of CVPPA but were not previously presented at the
                      workshop.The papers in this Research Topic explore a number
                      of high-priority challenges in image-based phenotyping,
                      including curating new datasets, developing few-and
                      zero-shot analysis approaches that do not require …},
      cin          = {IAS-8},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IAS-8-20210421},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37261332},
      UT           = {WOS:000998420800001},
      doi          = {10.3389/frai.2023.1187301},
      url          = {https://juser.fz-juelich.de/record/1022057},
}