Transforming Agriculture with Intelligent Data Management and Insights
- URL: http://arxiv.org/abs/2401.13672v1
- Date: Tue, 7 Nov 2023 22:02:54 GMT
- Title: Transforming Agriculture with Intelligent Data Management and Insights
- Authors: Yu Pan, Jianxin Sun, Hongfeng Yu, Geng Bai, Yufeng Ge, Joe Luck, Tala
Awada
- Abstract summary: Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber 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.
- Score: 3.027257459810039
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Building Multi-Agent Copilot towards Autonomous Agricultural Data Management and Analysis [2.763670421921841]
We build a proof-of-concept multi-agent system called ADMA Copilot, which can understand user's intent.
ADMA Copilot accomplishes tasks automatically, in which three agents: a LLM based controller, an input formatter and an output formatter collaborate.
arXiv Detail & Related papers (2024-10-31T20:15:14Z) - Synthetic Data Generation with Large Language Models for Personalized Community Question Answering [47.300506002171275]
We build Sy-SE-PQA based on an existing dataset, SE-PQA, which consists of questions and answers posted on the popular StackExchange communities.
Our findings suggest that LLMs have high potential in generating data tailored to users' needs.
The synthetic data can replace human-written training data, even if the generated data may contain incorrect information.
arXiv Detail & Related papers (2024-10-29T16:19:08Z) - On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms [56.119374302685934]
There have been severe concerns over the trustworthiness of AI technologies.
Machine and deep learning algorithms depend heavily on the data used during their development.
We propose a framework to evaluate the datasets through a responsible rubric.
arXiv Detail & Related papers (2023-10-24T14:01:53Z) - Semantic Modelling of Organizational Knowledge as a Basis for Enterprise
Data Governance 4.0 -- Application to a Unified Clinical Data Model [6.302916372143144]
We establish a simple, cost-efficient framework that enables metadata-driven, agile and (semi-automated) data governance.
We explain how we implement and use this framework to integrate 25 years of clinical study data at an enterprise scale in a fully productive environment.
arXiv Detail & Related papers (2023-10-20T19:36:03Z) - Dynamic Spatio-Temporal Summarization using Information Based Fusion [3.038642416291856]
We propose a dynamic-temporal data summarization technique that identifies informative features in key timesteps and fuses less informative ones.
Unlike existing methods, our method retains both raw and summarized timesteps, ensuring a comprehensive view of information changes over time.
We demonstrate the versatility of our technique across diverse datasets, encompassing particle-based flow simulations, security and surveillance applications, and biological cell interactions within the immune system.
arXiv Detail & Related papers (2023-10-02T20:21:43Z) - Privacy-Preserving Graph Machine Learning from Data to Computation: A
Survey [67.7834898542701]
We focus on reviewing privacy-preserving techniques of graph machine learning.
We first review methods for generating privacy-preserving graph data.
Then we describe methods for transmitting privacy-preserved information.
arXiv Detail & Related papers (2023-07-10T04:30:23Z) - Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic
Data [91.52783572568214]
Synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs.
We discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data.
arXiv Detail & Related papers (2023-04-07T16:38:40Z) - A Transformer Framework for Data Fusion and Multi-Task Learning in Smart
Cities [99.56635097352628]
This paper proposes a Transformer-based AI system for emerging smart cities.
It supports virtually any input data and output task types present S&CCs.
It is demonstrated through learning diverse task sets representative of S&CC environments.
arXiv Detail & Related papers (2022-11-18T20:43:09Z) - Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy [2.9005223064604078]
We introduce a scalable Aerial Synthetic Data Augmentation (ASDA) framework tailored to aerial autonomy applications.
ASDA extends a central data collection engine with two scriptable pipelines that automatically perform scene and data augmentations.
We demonstrate the effectiveness of our method in automatically generating diverse datasets.
arXiv Detail & Related papers (2022-11-10T04:37:41Z) - TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual
Environments [84.6017003787244]
This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets.
We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
arXiv Detail & Related papers (2022-08-16T20:46:08Z) - Crop Knowledge Discovery Based on Agricultural Big Data Integration [2.597676155371155]
Agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses.
We propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models.
arXiv Detail & Related papers (2020-03-11T00:13:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.