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@ARTICLE{Jollet:1014709,
      author       = {Jollet, Dirk and Junker-Frohn, Laura and Steier, Angelina
                      and Meyer-Lüpken, T. and Müller-Linow, Mark},
      title        = {{A} new computer vision workflow to assess yield quality
                      traits in bush bean ({P}haseolus vulgaris {L}.)},
      journal      = {Smart agricultural technology},
      volume       = {5},
      issn         = {2772-3755},
      address      = {[Amsterdam]},
      publisher    = {Elsevier B.V.},
      reportid     = {FZJ-2023-03404},
      pages        = {100306 -},
      year         = {2023},
      note         = {Grant: IGF-Vorhaben 20943N},
      abstract     = {Quality assessments of horticultural products are still
                      often carried out manually in breeding contexts, although
                      computer vision systems have been reported to be able to
                      overcome the limitations of manual assessments, e.g. in
                      automated food processing. Here, a new computer vision
                      workflow for quality trait assessment of bush bean pods
                      (Phaseolus vulgaris) is introduced to replace physical
                      measurements and visual scorings of expert breeders, while
                      increasing consistency, accuracy, and objectivity of the
                      measurements. A closed imaging box was used to take images
                      of bean pods from 40 different varieties to develop and
                      validate computer vision workflows to assess breeding
                      relevant shape and color traits of bean pods. For the
                      detection of beaks and peduncles via a neural network
                      approach (Mask R-CNN) accuracies of $95.5\%$ were reached.
                      Computer vision estimations and manual reference
                      measurements of length and caliber were highly correlated
                      (R=0.99). Also, curvature and brightness of green bean pods
                      well- correlated with visual scorings of expert breeders
                      (R=0.81, R=-0.87). A Random Forest Classifier was trained to
                      distinguish yellow and extremely rare bicolored pods and a
                      cross validation accuracy of 83 $±7\%$ was reached. An
                      additional backlight LED panel enabled non-destructive
                      analysis of seed formation inside the pod and promising
                      results were achieved using a Faster R-CNN model. This new
                      computer vision workflow provides the opportunity to replace
                      well-established manual workflows for quality trait
                      assessment of bush bean pods as it is more objective,
                      reliable, and considerably faster.},
      cin          = {IBG-2},
      ddc          = {630},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {2171 - Biological and environmental resources for
                      sustainable use (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2171},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001133851100001},
      doi          = {10.1016/j.atech.2023.100306},
      url          = {https://juser.fz-juelich.de/record/1014709},
}