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).
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Claims 9
Claim · model-written summary
PhytoOracle processed 9,270 RGB images in 235 minutes using 1,024 cores.
Verbatim
e0“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)”
Claim · model-written summary
PhytoOracle processed 9,270 thermal images in 235 minutes.
Verbatim
e0“235 minutes for 9,270 thermal images (5.4 GB)”
Claim · model-written summary
PhytoOracle processed 39,678 PSII images in 13 minutes.
Verbatim
e0“13 minutes for 39,678 PSII images (86.2 GB)”
Claim · model-written summary
Bounding area repeatability ranged from 0.39 to 0.95 in Field Scanalyzer data.
Verbatim
e0“Repeatability values of 0.39-0.95 (bounding area)”
Claim · model-written summary
Axis-aligned bounding volume repeatability ranged from 0.81 to 0.95.
Verbatim
e0“0.81-0.95 (axis-aligned bounding volume)”
Claim · model-written summary
Oriented bounding volume repeatability ranged from 0.79 to 0.94.
Verbatim
e0“0.79-0.94 (oriented bounding volume)”
Claim · model-written summary
Plant height repeatability ranged from 0.83 to 0.95.
Verbatim
e0“0.83-0.95 (plant height)”
Claim · model-written summary
Point count repeatability ranged from 0.81 to 0.95.
Verbatim
e0“0.81-0.95 (number of points)”
Claim · model-written summary
Bounding area repeatability in drone data ranged from 0.55 to 0.95.
Verbatim
e0“drone data with a repeatability of 0.55-0.95 (bounding area)”