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Simplifying PlantCV workflows with multiple objects
  • +2
  • Haley Schuhl,
  • J David Peery,
  • Jorge Gutierrez,
  • Malia A Gehan,
  • Noah Fahlgren
Haley Schuhl

Corresponding Author:[email protected]

Author Profile
J David Peery
University of North Carolina at Chapel Hill
Jorge Gutierrez
Malia A Gehan
Noah Fahlgren

Abstract

Imaging of plants using multi-camera arrays in high-density growth environments is a strategy for affordable high-throughput phenotyping. In multi-camera systems, simultaneous imaging of hundreds to thousands of plants eliminates the time delay in measurements between plants seen in plant-to-camera or camera-to-plant systems, which allows for the analysis of plant growth, development, and environmental responses at a high temporal resolution. On the other hand, high plant density, camera-to-camera variation, and other trade-offs increase the complexity of data analysis. Here we present two recent updates to the PlantCV image analysis package to improve usability when working with multi-plant datasets. First, we introduce a method to automate detection of plants organized in a grid layout, reducing the need to make separate workflows for each camera in a multi-camera system. Second, we reduced the number of input and output parameters for functions handling the shape and location of plants and introduce automatic iteration over multiple objects of interest (e.g. plants), reducing the level of programming needed to build workflows.
24 Oct 2022Submitted to NAPPN 2023 Conference Papers
28 Oct 2022Published in NAPPN 2023 Conference Papers