Toward effective high-throughput georeferencing over voluminous observational data in the domain of precision agriculture
Date
2018
Authors
Roselius, Maxwell L., author
Pallickara, Sangmi Lee, advisor
Pallickara, Shrideep, committee member
McKay, John, committee member
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Abstract
Remote 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.
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Subject
georeferencing
precision agriculture
high-throughput
distributed systems