As the capacity of phenomics to generate larger and higher-dimensional data sets improves, there is an urgent need to develop and implement robust data processing pipelines to handle the data volume so that biological insight can be leveraged from them. Current phenomics data pipelines lack extractor modularity and distributed computing, leading to significant bottlenecks in data processing. To address these challenges, we have developed PhytoOracle, a modular, scalable data pipeline that aims to improve data processing for phenomics research. PhytoOracle refines the TERRA-REF data pipeline by integrating CCTools’ Makeflow and Work Queue frameworks for distributed task management. Briefly, PhytoOracle distributes data processing tasks to either local, cloud or high-performance computing (HPC) systems. These systems include CyVerse, JetStream and other XSEDE resources, local/private HPC centers, and commercial cloud providers. Each tool and pipeline is available as containers providing portability as well as modularity, enabling researchers to swap between available extractors or integrate new ones suited to their specific research needs. The future scope and applications of phenomics will largely depend on the capabilities of data pipelines. PhytoOracle handles increasing rates of data collection while also enabling easy development, modification, and customization. As a result, researchers using this pipeline can quickly process data and extract phenotypic information, thereby enabling faster elucidation of genetic components of complex traits. Code, containers, and documentation are available on GitHub.