Location: Genomics and Bioinformatics Research
Project Number: 6066-21310-005-38-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 2, 2019
End Date: Aug 1, 2024
Agricultural research continues to move towards multidisciplinary research that requires the use of Big Data and at times the integration of a wide range of scientific research methods and technologies [e.g. unmanned aircraft systems (UAS)/unmanned aerial vehicles (UAV), microbiome, genetics, climate data, animal waste, genomics, analysis of pests and other items related to productivity]. As such, agriculture research requires more complex levels of analysis with the ability to add additional variables when looking to improve agriculture productivity. This in turns results in the creation of a Big Data paradigms with the need for development of new analysis techniques (e.g. geospatial statistics, bioinformatics etc.) the need for high-performance computational system and application of said technologies to individual or mixed datasets. To fully utilize these technologies, computational analysis of Big Data of vast types is needed. In turn, these techniques need access to high-performance computing (HPC) for complex data analysis, modeling and machine learning. Also, computational tools need to run optimally on an HPC environment and this takes specialized development. All of this needs to be developed and put into place with training for proper analysis. Clear examples are development and use of new statistical and image analysis tools for geo-referenced agricultural data related to agricultural operations.
ARS has extensive experience researching agriculture productivity, microbiome research related to animals, animal waste and field studies, crop/animal genetics, unmanned aerial vehicles, genomics and complex agricultural systems. ARS also has a wide-ranging network for research on controlling diseases/pests, especially foreign diseases and non-native invasive pests. The USDA also leads in the development of unique genomic tools for agricultural production systems. Mississippi State University (MSU) has expertise in agriculture research and computational aspects related to that research especially in the area of bioinformatics and pathogen research. MSU has unique experience with unmanned aerial vehicles and subsequent analysis like geospatial statistics. MSU also has extensive experience with the establishment and operations of high-performance computing (HPC) and developing/altering computational software to run in an HPC environment. In this project, ARS and MSU will work together to utilize HPC environment and will work together to analyze Big Data generated by ARS to improve agriculture productivity. Types of analysis include geospatial statistics, bioinformatics, modeling and machine learning (also known as artificial intelligence). These types of analysis will require a non-standard HPC configuration of high memory nodes, normal nodes, graphical processing unit (GPU) nodes and data storage that MSU will provide access to participants of both parties.