Roselius, Maxwell L., authorPallickara, Sangmi Lee, advisorPallickara, Shrideep, committee memberMcKay, John, committee member2019-01-072019-01-072018https://hdl.handle.net/10217/193210Remote sensing of plant traits and their environment facilitates non-invasive, high-throughput monitoring of the plant's physiological characteristics. Effective ingestion of these sensing data into a storage subsystem while georeferencing phenotyping setups is key to providing timely access to scientists and modelers. In this thesis, we propose a high-throughput distributed data ingestion framework with support for fine-grained georeferencing. The methodology includes a novel spatial indexing scheme, the nested hash grid, for fine-grained georeferencing of data while conserving memory footprints and ensuring acceptable latency. We include empirical evaluations performed on a commodity machine cluster with up to 1TB of data. The benchmarks demonstrate the efficacy of our approach.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.georeferencingprecision agriculturehigh-throughputdistributed systemsText