2024 International Symposium on AI for Agriculture

AAAI 2024 Fall Symposium Series

Symposium Title: Using AI To Build Secure and Resilient Agricultural Systems

Leveraging AI to mitigate Cyber, Climatic, and Economic Threats in Food, Agricultural, and Water (FAW) Systems. 

November 7 – 9, 2024 | Arlington, VA

Westin Arlington Gateway, Arlington, Virginia, USA 

Important Dates:

Submission deadline: August 9
Decision notification: August 31
Camera-ready papers due: September 13
Symposium: November 7 - 9

For any questions please contact Feras Batarseh (batarseh@vt.edu) and Frank Stein (fstein@vt.edu).

Easy Chair Submission Link: https://easychair.org/conferences/?conf=fss24

The increasing frequency of threats to the Food, Agriculture, and Water (FAW) has heightened the need to develop more resilient and secure underpinnings to the systems that support these critical sectors. AI can make significant contributions to this effort by detecting, predicting, analyzing, and mitigating the threats, and thus creating novel, robust approaches to create a more secure and resilient FAW for the world. Threats to these systems can be climatic in nature, such as due to extreme weather, floods, and droughts; or economic, such as the impacts of trade policies and supply chain issues, but more recently, cybersecurity challenges are becoming more evident. Recent developments in data, sensors, and precision technologies have elevated their adoption in the FAW sector. However, these newly adopted cyber-physical systems also present additional cybersecurity challenges. This symposium will bring together AI researchers and industry practitioners working in the FAW sectors to share and discuss state-of-the-art AI approaches to addressing these challenges.

Description:

Public trust in Food, Agriculture, and Water (FAW) supplies is contingent on protecting these increasingly digitized systems. FAW systems influence >20% of the nation's economy and >15% of American jobs. These systems run from “farm-to-fork”; and are dependent on other critical infrastructures such as energy, supply chains, and water systems. There are increasing threats to these systems due to natural causes such as climate change, intentional adversarial actions from state and non-state actors, worldwide economic shifts, and domestic policy impacts.

The goal of this Fall Symposium is to bring together participants from academia, industry, and government to explore how AI is currently being used and can be expanded to support its key role in addressing challenges to FAW systems. The symposium will discuss work in the area of AI applied to protecting FAW. We will also cover best practices, issues, barriers to success, risk, and policy issues associated with the broad relationship between FAW and AI. This year, we will include a discussion on the use of generative AI and LLMs for FAW use cases as well as ethical AI, trustworthy AI, and secure AI frameworks that may be specific to this context.

The FAW resources are increasingly being digitized and connected and driven by data. The safety and security of these connected systems now rely on digital tools (e.g., agricultural robots, drones, IoT devices, etc.) as well as AI and data engineering. Consequently, this symposium invites submissions from researchers working in tangential areas impacting FAW and illustrating the use of AI, such as for smart farms, data-driven farming, agricultural robotics, bio-technologies, intelligent water systems, cyber-physical systems, plant science, controlled-environment agriculture, vertical farming, cyber biosecurity, and other topics listed below in the “areas” section.

AI can make significant contributions toward the goal of building resilient and secure FAW systems. We hope the symposium will provide a forum to present novel AI use-cases, technical advances in the state-of-the-art, approaches and lessons learned from current implementations, and broader policy and governance frameworks. The forum will invite participation from academia, industry, government, and civil society to better understand how different sectors can work together to overcome the challenges faced when using AI to address threats in the FAW domain.

Topic Areas

The symposium will be organized around several topics within which AI approaches apply. These topics and some examples include:

Cybersecurity in FAW
- Using AI to detect and protect against cyber breaches, ransomware, data poisoning attacks, and all forms of adversarial breaches.
- Safeguarding data and AI algorithms in all forms of FAW systems.
- Using AI for improving the security and robustness of FAW systems.
- AI-driven methods for biosecurity, biosafety, cybersecurity, and data management.
- Data/AI engineering best practices related to FAW systems.
- Optimizing data science aspects such as data wrangling and preprocessing.
- Anomaly detection and mitigation of cyberattacks on agricultural machinery systems, irrigation systems, and controlled environment livestock systems.
- Data assurance and quality challenges.
- AI algorithms’ deployment and management.
- The assurance and validation of AI algorithms for FAW.
- Securing sensors and other forms of digital technology in cyber-physical and/or biological systems contexts.
- Vulnerability analysis of existing Ag-specific hardware, firmware, software.
- Privacy-preserving precision agriculture and distributed decision making.
Climatic Threats to FAW
- Using AI for modeling and forecasting climate-related hazards and threats.
- Using AI for improving the resilience and robustness of FAW systems to climate-related hazards and threats.
- Developing methods to adapt agricultural communities to weather and climate change in support of long-term sustainability and carbon reduction.
- Using AI for creating resilient food and water distribution networks that continue to operate in the face of local or global FAW disturbances.
- Addressing FAW insecurity of communities globally.
- Using AI to advance the science associated with the development of climate change-resilient crops.
- Automate the environmental sustainability analysis, such as life-cycle assessment.
Economic Threats to FAW
- Using AI to evaluate agricultural trade flows.
- Developing methods to measure the impacts of agricultural policies, such as by the Farm bill.
- Using AI to analyze and address food and nutrition insecurity caused by supply and demand factors (population growth, diet-related diseases, and community disparities).
- Studies and evaluations for ensuring access to healthy and safe food and water.
- Leveraging AI to evaluate FAW-related price changes and costs of production.
- Using AI to analyze sales, production and supply chain interactions to help build resilient food supply chains and reduce food waste.
FAW Cross-Cutting Issues
- Data management and algorithms used in the FAW systems.
- Farm-to-fork methods and other related supply chain challenges.
- Cold chains, shipping, transportation of agricultural commodities.
- AI assurance for FAW, including explainable, trustworthy, ethical, and secure AI.
- The application of data/AI methods by farmers and operators.
- Smart and data-driven farming.
- Agricultural robotics.
- Bio-technologies (such as synthetic biology) and the effects of FAW on human and public health.
- Intelligent water systems.
- Energy systems that support FAW.
- Cyber-physical systems and critical infrastructures.
- Controlled-environment agriculture and vertical farming.
- Data/AI-driven environmental laws and policies’ analysis.
- Governance and ethics for AI and cybersecurity.
- Using AI to assess and mitigate risks in the supply chain caused by climate change, geopolitical uncertainties, and increased connectivity/digitization.
- Cybersecurity and cyber-biosecurity risks associated with increased digitalization and AI integration within climate workflows.
- Benchmarks and datasets to advance the field.
- Surveying existing cyber-threats, known attacks, and AI tools for vulnerability discovery and mitigation.
- Other related topics.

Chairs:

Feras A. Batarseh (Chair), Associate Professor, Department of Biological Systems Engineering & Commonwealth Cyber Initiative, Virginia Tech, batarseh@vt.edu
Frank Stein (Co-Chair), Research Faculty, Intelligent Systems, National Security Institute, Virginia Tech, fstein@ieee.org

Steering Committee:

Kang Xia, Director, Center for Advanced Innovation in Agriculture (CAIA), Virginia Tech, kxia@vt.edu
Manimaran Govindarasu, Professor, Electrical and Computer Engineering, Iowa State University, gmani@iastate.edu
Melissa Hatton, Capgemini Government Solutions, melissa.hatton@capgemini-gs.com.
Jim Spohrer, ISSIP BOD Member, spohrer@gmail.com.
Matt Wolfe, Vice President of Technology, Virginia Tech Applied Research Corporation, matt.wolfe@vt-arc.org.
Zeb Bowden, Director, Division of Technology Development and Deployment, Virginia Tech Transportation Institute, zbowden@vt.edu
Monowar Hasan, Assistant Professor, School of EECS, Washington State University, monowar.hasan@wsu.edu

Scientific Committee:

Shana Moothedath, Assistant Professor, Electrical and Computer Engineering, Iowa State University, mshana@iastate.edu
Brian Steward, Professor, Agricultural and Biosystems Engineering, Iowa State University, bsteward@iastate.edu
Josh Detre, Director, Health, Food, and Agricultural Resilience, Purdue Applied Research Institute (PARI), ddetre@hotmail.com
Nicholas Guise, Chief Scientist, CIPHER Lab, Georgia Tech Research Institute (GTRI), Nicholas.Guise@gtri.gatech.edu
Yimeng Feng, Assistant Professor, Department of Biological Systems Engineering, Virginia Tech, yimingfeng@vt.edu
Will Singer, Center for Advanced Innovation in Agriculture (CAIA), Virginia Tech, wilmsing@vt.edu
Jennifer Sleesman, Sr. AI Researcher, Johns Hopkins University JHU/APL, jsleem1@umbc.edu
Lav Khot, Associate Professor, Washington State University (WSU), lav.khot@wsu.edu