This project leverages OpenSearch to index, search and visualize data scanned by the Field Analyzer, a state-of-the-art outdoor plant phenotyping platform located at the Maricopa Agricultural Center (MAC), focusing on plant metrics across different growth stages.
By integrating visualization dashboards and advanced searching and indexing capabilities, the solution aims to enable researchers to interact with, navigate and analyze the complex data sets seamlessly.
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automation: Contains scripts for automating repetitive tasks such as data preparation, uploading and indexing workflows.
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data_preparation: Contains scripts for preparing the data in JSON format to be indexed. This includes data extraction, transformation, and cleaning processes.
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deployment: Contains scripts for deploying the solution.
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sample_queries: Contains predefined queries in Query DSL format used to extract relevant responses from the indexed data.
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search_configuration: Contains scripts to interact with the OpenSearch Server.
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Installing dependencies: To install dependencies, run the following command:
pip install -r requirements.txt -
OpenSearch Configuration: Get an instance of OpenSearch/ElasticSearch set up and then refer to
example.envto write an.envfile with the necessary environment variables for our program to access OpenSearch. -
Data Preparation: To perform data preparation, please refer to the documentation provided in the data_preparation directory. The usage of data preparation is unique for each sensor type. All prepared data should be available in the
output/directory. -
OpenSearch: Refer to the documentation provided in search_configuration to populate the data in the OpenSearch index.
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Visualization: To visualize the data, run:
streamlit run app/vis.py