PhytoOracle: Scalable, modular phenomics data processing pipelines

Emmanuel M. Gonzalez, Ariyan Zarei, Nathanial Hendler, Travis Simmons, Arman Zarei, Jeffrey Demieville, Robert Strand, Bruno Rozzi, Sebastian Calleja, Holly Ellingson, Michele Cosi, Sean Davey, Dean O. Lavelle, Maria José Truco, Tyson L. Swetnam, Nirav Merchant, Richard W. Michelmore, Eric Lyons, Duke Pauli

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

Original languageEnglish (US)
Article number1112973
JournalFrontiers in Plant Science
Volume14
DOIs
StatePublished - 2023

Keywords

  • data management
  • distributed computing
  • high performance computing
  • image analysis
  • morphological phenotyping
  • phenomics
  • physiological phenotyping
  • point cloud analysis

ASJC Scopus subject areas

  • Plant Science

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