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@ARTICLE{Agarwal:1041664,
      author       = {Agarwal, Avinash and de Jesus Colwell, Filipe and Correa
                      Galvis, Viviana Andrea and Hill, Tom R. and Boonham, Neil
                      and Prashar, Ankush},
      title        = {{A}ssessing nutritional pigment content of green and red
                      leafy vegetables by image analysis: {C}atching the “red
                      herring” of plant digital color processing via machine
                      learning},
      journal      = {Biology methods $\&$ protocols},
      volume       = {10},
      number       = {1},
      issn         = {2396-8923},
      publisher    = {Oxford University Press},
      reportid     = {FZJ-2025-02369},
      pages        = {bpaf027},
      year         = {2025},
      abstract     = {Estimating pigment content of leafy vegetables via digital
                      image analysis is a reliable method for high-throughput
                      assessment of their nutritional value. However, the current
                      leaf color analysis models developed using green-leaved
                      plants fail to perform reliably while analyzing images of
                      anthocyanin (Anth)-rich red-leaved varieties due to
                      misleading or “red herring” trends. Hence, the present
                      study explores the potential for machine learning (ML)-based
                      estimation of nutritional pigment content for green and red
                      leafy vegetables simultaneously using digital color
                      features. For this, images of n = 320 samples from six
                      types of leafy vegetables with varying pigment profiles were
                      acquired using a smartphone camera, followed by
                      extract-based estimation of chlorophyll (Chl), carotenoid
                      (Car), and Anth. Subsequently, three ML methods, viz.,
                      Partial Least Squares Regression (PLSR), Support Vector
                      Regression (SVR), and Random Forest Regression (RFR), were
                      tested for predicting pigment contents using RGB (Red,
                      Green, Blue), HSV (Hue, Saturation, Value), and L*a*b*
                      (Lightness, Redness-greenness, Yellowness-blueness) datasets
                      individually and in combination. Chl and Car contents were
                      predicted most accurately using the combined colorimetric
                      dataset via SVR (R2 = 0.738) and RFR (R2 = 0.573),
                      respectively. Conversely, Anth content was predicted most
                      accurately using SVR with HSV data (R2 = 0.818). While Chl
                      and Car could be predicted reliably for green-leaved and
                      Anth-rich samples, Anth could be estimated accurately only
                      for Anth-rich samples due to Anth masking by Chl in
                      green-leaved samples. Thus, the present findings demonstrate
                      the scope of implementing ML-based leaf color analysis for
                      assessing the nutritional pigment content of red and green
                      leafy vegetables in tandem.},
      cin          = {IBG-2},
      ddc          = {570},
      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},
      pubmed       = {40336971},
      UT           = {WOS:001483470400001},
      doi          = {10.1093/biomethods/bpaf027},
      url          = {https://juser.fz-juelich.de/record/1041664},
}