Yield components detection and image-based indicators for non-invasive grapevine yield prediction at different phenological phases

HIGHLIGHTS

  • who: OENO One and colleagues from the Linking Landscape, Environment, Agriculture and Food (LEAF), Instituto Superior de Agronomia, Universidade have published the Article: Yield components detection and image-based indicators for non-invasive grapevine yield prediction at different phenological phases, in the Journal: (JOURNAL)
  • what: In this work, the visibility of several yield components was explored in field conditions along the grapevine growing cycle.
  • how: When the same approach was attempted in field conditions results showed that bunch projected area in 2D images outperformed the 3D alternative. The experiment was carried out in . . .

     

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