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.
The Need for Data Innovation in Agriculture
Modern agriculture is faced with significant challenges in meeting the growing demands for food, fuel, feed, and fiber. With population growth, climate change, and diminishing natural resources, there is an urgent need for data innovation to ensure the productivity, sustainability, and resilience of our agroecosystems.
Data collection, integration, and analysis have become more feasible with the availability of various sensors and Internet of Things (IoT) devices. These technologies offer the potential to gather real-time data at multiple temporal and spatial scales, with high resolutions. However, the sheer volume of data poses challenges in terms of storage and analysis.
The Inefficiency of Existing Data Management Practices
Scientists in the agricultural domain have been adopting data management and analysis practices that are increasingly inefficient. As the amount of data grows, traditional approaches struggle to handle the diverse and complex datasets generated from different disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic.
Datasets across disciplines often vary in their ontology, modality, and format. This heterogeneity makes it difficult to integrate and analyze data effectively. To address these issues, a new data management infrastructure is needed.
The FAIR Principles
The new data management infrastructure should adhere to the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) data. This means that data should be easily discoverable, accessible with clear terms of use, interoperable with other datasets, and reusable by different research communities.
Introducing Agriculture Data Management and Analytics (ADMA)
In this paper, we propose a solution called Agriculture Data Management and Analytics (ADMA). This new infrastructure aims to meet the requirements of the FAIR principles by providing an intelligent, interactive, scalable, extensible, trackable, and open platform.
Intelligent Data Management
The ADMA platform supports semantic data management across different disciplines. By utilizing semantic technologies, such as ontologies and linked data, the platform can improve the integration and interoperability of diverse datasets. This intelligence enables researchers to gain deeper insights and make more informed decisions.
Interactive Data Management and Analysis Portals
ADMA offers various data management and analysis portals, catering to different user preferences. These include web GUI, command line interface, and API access. Users can choose their preferred interface to interact with the platform, enhancing usability and accessibility.
Scalability through High-Performance Computing (HPC)
To handle large datasets and complex computations, ADMA leverages the power of high-performance computing (HPC). This enables researchers to process data efficiently and perform computationally intensive analyses quickly and accurately.
Extensibility for User-Defined Tools
ADMA allows users to load their own data analysis tools into the platform. This extensibility empowers researchers to apply their preferred methods and algorithms, further enhancing the flexibility and versatility of the platform.
Trackability for Transparent Research
ADMA keeps track of the different operations performed on each file. This feature enhances reproducibility and allows researchers to trace their analysis steps accurately. It promotes transparency and facilitates collaboration among researchers working with shared datasets.
Open Source Technologies
ADMA is built on a rich set of mature open source technologies. This choice ensures that the platform is accessible to a wide community of users, fosters collaboration, and encourages contributions from the open source community.
The Multi-Disciplinary Nature of ADMA
ADMA recognizes the multi-disciplinary nature of agricultural research. It addresses the challenge of integrating data from various disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic. By providing a unified framework that supports diverse datasets, ADMA enables researchers to explore complex relationships and gain a holistic understanding of agroecosystems.
In conclusion, ADMA represents a comprehensive solution to the data challenges faced by modern agriculture. By implementing the principles of FAIR data and incorporating intelligent, interactive, scalable, extensible, trackable, and open features, ADMA empowers researchers to make data-driven decisions and advance agricultural innovation.