Transforming Agriculture with Intelligent Data Management and Insights
Yu Pan, Jianxin Sun, Hongfeng Yu, Geng Bai, Yufeng Ge, Joe Luck, Tala Awada
TL;DR
ADMA tackles the growing need for intelligent, FAIR-aligned data management in agriculture by unifying heterogeneous datasets with semantic search and vector representations. The authors design a five-layer architecture and a seven-component implementation that provides data portals, services, analytics, storage, and infrastructure, all containerized for scalability and HPC compatibility, with JupyterHub integration and robust privacy controls. Through system demos and qualitative comparisons, ADMA demonstrates advanced capabilities in semantic search, file and pipeline management, tool execution, model hosting, and data governance, outperforming several existing platforms on key dimensions. This work advances data-driven agriculture by enabling cross-disciplinary discovery, reproducible workflows, and secure data sharing, leveraging open-source technologies to promote collaboration and innovation across agroecosystems.
Abstract
Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber with population growth under the constraints of climate change and dwindling natural resources. Data innovation is urgently required to secure and improve the productivity, sustainability, and resilience of our agroecosystems. As various sensors and Internet of Things (IoT) instrumentation become more available, affordable, reliable, and stable, it has become possible to conduct data collection, integration, and analysis at multiple temporal and spatial scales, in real-time, and with high resolutions. At the same time, the sheer amount of data poses a great challenge to data storage and analysis, and the \textit{de facto} data management and analysis practices adopted by scientists have become increasingly inefficient. Additionally, the data generated from different disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic, can be highly heterogeneous. That is, datasets across disciplines often do not share the same ontology, modality, or format. All of the above make it necessary to design a new data management infrastructure that implements the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we propose Agriculture Data Management and Analytics (ADMA), which satisfies the FAIR principles. Our new data management infrastructure is intelligent by supporting semantic data management across disciplines, interactive by providing various data management/analysis portals such as web GUI, command line, and API, scalable by utilizing the power of high-performance computing (HPC), extensible by allowing users to load their own data analysis tools, trackable by keeping track of different operations on each file, and open by using a rich set of mature open source technologies.