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@ARTICLE{Bauer:867784,
      author       = {Bauer, Jan and Jarmer, Thomas and Schittenhelm, Siegfried
                      and Siegmann, Bastian and Aschenbruck, Nils},
      title        = {{P}rocessing and filtering of leaf area index time series
                      assessed by in-situ wireless sensor networks},
      journal      = {Computers and electronics in agriculture},
      volume       = {165},
      issn         = {0168-1699},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2019-06394},
      pages        = {104867 -},
      year         = {2019},
      abstract     = {A precise and up-to-date situational awareness of crop
                      conditions is important for precision farming. The
                      temporallycontinuous monitoring of relevant crop parameters
                      has recently been shown to assist in a large number
                      ofapplications. In this context, the leaf area index (LAI)
                      is a key parameter. However, continuous LAI monitoringusing
                      traditional assessment methods is hardly possible and very
                      expensive. For this reason, low-cost sensorsbased on
                      Wireless Sensor Network (WSN) technology have been developed
                      and interconnected to agricultural insitu sensor networks
                      that seem promising for LAI assessment. In this paper, an
                      approach for the processing andfiltering of distributed in
                      situ sensor data for a credible LAI estimation is proposed.
                      This approach is developedbased on a long-term WSN
                      deployment in experimental plots with different wheat
                      cultivars (Triticum aestivum L.)and water regimes.
                      Non-negligible environmental impacts on radiation-based LAI
                      assessment are also taken intoaccount. A comparative
                      analysis with a conventional LAI instrument shows that WSNs
                      with adequately processeddata gathered by low-cost sensors
                      have the potential to produce credible LAI trajectories with
                      hightemporal resolution, that fit the dynamic crop growth
                      process. Moreover, they are also shown to be able to
                      detectyield-limiting trends and even to differentiate
                      between individual wheat cultivars. Hence, those WSNs
                      enablenew applications and can greatly support modern crop
                      management, cultivation, and plant breeding.},
      cin          = {IBG-2},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582)},
      pid          = {G:(DE-HGF)POF3-582},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000488143100001},
      doi          = {10.1016/j.compag.2019.104867},
      url          = {https://juser.fz-juelich.de/record/867784},
}