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Segmentation of Overlapping Plants in Multi-plant Image Time Series
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  • Jorge Alberto Gutierrez Ortega,
  • Noah Fahlgren,
  • Malia Gehan,
  • S. Elizabeth Castillo
Jorge Alberto Gutierrez Ortega
Donald Danforth Plant Science Center, Donald Danforth Plant Science Center

Corresponding Author:jgutierrez@danforthcenter.org

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Noah Fahlgren
Donald Danforth Plant Science Center, Donald Danforth Plant Science Center
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Malia Gehan
Donald Danforth Plant Science Center, Donald Danforth Plant Science Center
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S. Elizabeth Castillo
Donald Danforth Plant Science Center
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Abstract

Multi-plant imaging using arrays of low-cost cameras is a successful strategy for capturing affordable high-throughput plant phenotyping data. An imaging platform of this type can enable simultaneous imaging of hundreds to thousands of plants. The resulting datasets enable analysis of dynamic plant growth, development, and environmental responses at high temporal resolution. Full analysis of these datasets requires the identification of individual plants for measurement, but computational separation of individual plants becomes challenging when neighboring plants overlap. Here, we introduce the use of the watershed transform to segment moderately overlapping plants in multi-plant time series datasets. Rather than focusing on segmenting plants in individual images, we utilize information encoded in the entire time series to propagate plant labels from an early time point when individual plants are separate to later time points. In preliminary studies, using this method allowed us increase the analyzable size of the dataset by 28%.