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@ARTICLE{Agarwal:1041666,
      author       = {Agarwal, Avinash and de Jesus Colwell, Filipe and Dinnis,
                      Rosalind and Correa Galvis, Viviana Andrea and Hill, Tom R.
                      and Boonham, Neil and Prashar, Ankush},
      title        = {{I}nfrared {T}hermography in {P}lant {F}actories: {S}olving
                      {S}patiotemporal {V}ariations {V}ia {M}achine {L}earning},
      journal      = {Modern agriculture},
      volume       = {3},
      number       = {1},
      issn         = {2751-4102},
      address      = {Weinheim},
      publisher    = {Wiley-VCH GmbH},
      reportid     = {FZJ-2025-02371},
      pages        = {e70012},
      year         = {2025},
      abstract     = {Infrared thermography (IRT) for real-time stress detection
                      in plant factories (PFs) remains largely unexplored. Hence,
                      this study investigates the feasibility of implementing IRT
                      in PFs, using machine learning (ML) to address the
                      challenges in information processing. Herein, purple basil
                      plantlets were subjected to root dehydration within a
                      pilot-scale PF, and canopy temperature was monitored at
                      regular intervals using a thermal camera. Subsequently,
                      eight ML models using the ‘support vector machines’
                      algorithm were tested for stress detection. Our findings
                      revealed that differences in canopy temperature due to
                      microenvironmental variations led to inaccurate
                      representation of stress. Nonetheless, binary classification
                      models trained using plants at medial and high stress
                      overcame this issue by identifying stressed samples with
                      $81\%–94\%$ accuracy. However, although models trained
                      with medially stressed samples performed well for all stress
                      levels, models trained using highly stressed samples failed
                      to identify medial stress reliably. Additionally, ternary
                      and quaternary classification models were able to identify
                      unstressed samples but could not distinguish between
                      different levels of stress. Hence, binary classification
                      models trained using medially stressed samples overcame
                      spatiotemporal variations in canopy thermal profile most
                      effectively and provided probabilistic estimates of plant
                      stress within the PF most consistently.},
      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},
      doi          = {10.1002/moda.70012},
      url          = {https://juser.fz-juelich.de/record/1041666},
}