Greenotyper: Image-Based Plant Phenotyping Using Distributed Computing and Deep Learning

Marni Tausen*, Marc Clausen, Sara Moeskjær, A. S.M. Shihavuddin, Anders Bjorholm Dahl, Luc Janss, Stig Uggerhøj Andersen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Image-based phenotype data with high temporal resolution offers advantages over end-point measurements in plant quantitative genetics experiments, because growth dynamics can be assessed and analysed for genotype-phenotype association. Recently, network-based camera systems have been deployed as customizable, low-cost phenotyping solutions. Here, we implemented a large, automated image-capture system based on distributed computing using 180 networked Raspberry Pi units that could simultaneously monitor 1,800 white clover (Trifolium repens) plants. The camera system proved stable with an average uptime of 96% across all 180 cameras. For analysis of the captured images, we developed the Greenotyper image analysis pipeline. It detected the location of the plants with a bounding box accuracy of 97.98%, and the U-net-based plant segmentation had an intersection over union accuracy of 0.84 and a pixel accuracy of 0.95. We used Greenotyper to analyze a total of 355,027 images, which required 24–36 h. Automated phenotyping using a large number of static cameras and plants thus proved a cost-effective alternative to systems relying on conveyor belts or mobile cameras.

Original languageEnglish
Article number1181
JournalFrontiers in Plant Science
Number of pages17
Publication statusPublished - 7 Aug 2020


  • Deep learning
  • Greenness measures
  • Image detection
  • Object detection
  • Plant phenotyping
  • Raspberry Pi
  • Segmentation
  • Software


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