% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Agarwal:1046239,
      author       = {Agarwal, Avinash and de Jesus Colwell, Filipe and Bello
                      Rodriguez, Julian and Sommer, Sarah and Barman, Monica and
                      Correa Galvis, Viviana Andrea and Hill, Tom R. and Boonham,
                      Neil and Prashar, Ankush},
      title        = {{S}ynergistic 3{D}, multispectral, and thermal image
                      analysis via supervised machine learning for improved
                      detection of root rot symptoms in hydroponically grown
                      flat-leaf parsley},
      journal      = {Smart agricultural technology},
      volume       = {12},
      issn         = {2772-3755},
      publisher    = {Elsevier B.V.},
      reportid     = {FZJ-2025-03759},
      pages        = {101364},
      year         = {2025},
      abstract     = {Root rot in hydroponically grown leafy vegetables is
                      difficult to detect via conventional manual and machine
                      vision-based approaches as symptoms of infection are not
                      clearly visible on the canopy at earlier stages of
                      infection. Hence, the present study investigates the
                      potential of using machine learning for assessing canopy
                      information obtained from multiple imaging platforms
                      synergistically to improve root rot detection. Herein,
                      flat-leaf parsley seedlings were grown in an experimental
                      hydroponic vertical farm and inoculated with Pythium
                      irregulare and Phytophthora nicotianae. Subsequently, the
                      seedlings were imaged via 3D, multispectral, and thermal
                      sensors at various stages of growth to obtain twenty-six
                      image-based plant features. Following a preliminary
                      screening of redundant features via regression analysis,
                      data for seventeen image features associated with
                      morphometric, spectral, and thermal attributes was
                      co-analyzed using supervised machine learning by Support
                      Vector Machines (SVM). Models using all eleven spectral
                      features provided 98 $\%$ accuracy compared to 90 $\%$ for
                      all five morphometric features and 94 $\%$ for canopy
                      temperature alone. Inclusion of temporal data improved model
                      performance by ca. 0.5 $\%,$ 1.5 $\%,$ and 8 $\%$ for
                      spectral, thermal, and morphometric datasets, respectively.
                      Exhaustive feature selection using different SVM kernels and
                      maximum feature thresholds showed that combining features
                      across the three imaging platforms along with temporal
                      information enabled better identification of infected
                      samples (>99 $\%)$ with as low as three features in
                      comparison to using considerably more features from
                      individual imaging systems. Hence, fusion of data from
                      multiple imaging systems and using it with temporal
                      information enabled better real-time high-throughput
                      monitoring of root rot.},
      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:001568496000001},
      doi          = {10.1016/j.atech.2025.101364},
      url          = {https://juser.fz-juelich.de/record/1046239},
}