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@ARTICLE{Bauer:911250,
      author       = {Bauer, Felix Maximilian and Lärm, Lena and Morandage,
                      Shehan and Lobet, Guillaume and Vanderborght, Jan and
                      Vereecken, Harry and Schnepf, Andrea},
      title        = {{D}evelopment and {V}alidation of a {D}eep {L}earning
                      {B}ased {A}utomated {M}inirhizotron {I}mage {A}nalysis
                      {P}ipeline},
      journal      = {Plant phenomics},
      volume       = {2022},
      issn         = {2643-6515},
      address      = {Washington, D.C.},
      publisher    = {American Association for the Advancement of Science},
      reportid     = {FZJ-2022-04546},
      pages        = {1 - 14},
      year         = {2022},
      abstract     = {Root systems of crops play a significant role in
                      agroecosystems. The root system is essential for water and
                      nutrient uptake, plant stability, symbiosis with microbes,
                      and a good soil structure. Minirhizotrons have shown to be
                      effective to noninvasively investigate the root system. Root
                      traits, like root length, can therefore be obtained
                      throughout the crop growing season. Analyzing datasets from
                      minirhizotrons using common manual annotation methods, with
                      conventional software tools, is time-consuming and
                      labor-intensive. Therefore, an objective method for
                      high-throughput image analysis that provides data for field
                      root phenotyping is necessary. In this study, we developed a
                      pipeline combining state-of-the-art software tools, using
                      deep neural networks and automated feature extraction. This
                      pipeline consists of two major components and was applied to
                      large root image datasets from minirhizotrons. First, a
                      segmentation by a neural network model, trained with a small
                      image sample, is performed. Training and segmentation are
                      done using “RootPainter.” Then, an automated feature
                      extraction from the segments is carried out by
                      “RhizoVision Explorer.” To validate the results of our
                      automated analysis pipeline, a comparison of root length
                      between manually annotated and automatically processed data
                      was realized with more than 36,500 images. Mainly the
                      results show a high correlation (r=0.9) between manually and
                      automatically determined root lengths. With respect to the
                      processing time, our new pipeline outperforms manual
                      annotation by $98.1-99.6\%.$ Our pipeline, combining
                      state-of-the-art software tools, significantly reduces the
                      processing time for minirhizotron images. Thus, image
                      analysis is no longer the bottle-neck in high-throughput
                      phenotyping approaches.},
      cin          = {IBG-3},
      ddc          = {580},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / DFG project 390732324 - EXC 2070: PhenoRob -
                      Robotik und Phänotypisierung für Nachhaltige
                      Nutzpflanzenproduktion},
      pid          = {G:(DE-HGF)POF4-2173 / G:(GEPRIS)390732324},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35693120},
      UT           = {WOS:000834077000003},
      doi          = {10.34133/2022/9758532},
      url          = {https://juser.fz-juelich.de/record/911250},
}