Earth Precision Agriculture
AI in Agriculture Conference 2026
Shaping the future of agriculture by integrating artificial intelligence, data science, and socio-economic perspectives to enable sustainable and intelligent food production
About the AI in Agriculture Conference Track
The AI in Agriculture Conference at EPA 2026 addresses one of the most promising frontiers in modern science: the convergence of artificial intelligence and agriculture to ensure global food security while preserving our planet’s ecological balance.
As the world’s population continues to grow and climate change intensifies pressure on agricultural systems, the need for intelligent, data-driven approaches to farming has never been more urgent. This special track provides a rigorous and welcoming platform for researchers to share cutting-edge contributions across the full spectrum of AI research that drives innovation in modern agriculture.
We convene experts working in foundational AI, computer vision, machine learning, data analytics, and sustainability science. The track emphasizes how these complementary, multi-disciplinary approaches collectively generate richer, more actionable insights into farm management, crop health, and environmental stewardship. We invite submissions that explore a wide range of research, including but not limited to:
Key Research Themes in AI in Agriculture Conference
Foundational AI for Agriculture
- Generative AI: Development and application of generative models
- Foundation Models: Development of foundation models for agriculture to capture the unique semantics of agricultural data.
- Multimodal Learning: Fusion of diverse data sources, including satellite imagery, sensor data, weather patterns, and textual reports, to create holistic representations for farm-level decision-making.
- Self-Supervised and Few-Shot Learning: Methods for pre-training models on large-scale unlabeled agricultural data, and techniques for adapting models to new crops, pests, or environments with minimal labeled data.
Computer Vision and Image Processing
- 3D Vision for Agriculture: 3D reconstruction of plants for high-throughput phenotyping, and analysis of field topography and soil structure.
- Semantic and Instance Segmentation: Precise segmentation of individual plants, leaves, fruits, or weeds to enable targeted interventions and robotic actions.
- Scene Understanding and Analysis: Holistic analysis of farm environments to provide context for autonomous systems and to monitor complex activities like livestock behavior.
Machine Learning for Crop Management
- Predictive Modeling and Forecasting: Development of models for forecasting pest and disease outbreaks, predicting crop yields, and optimizing soil health and nutrient management.
- Reinforcement Learning for Control: Application of reinforcement learning for optimizing dynamic processes such as irrigation scheduling, fertilizer application, and autonomous harvesting strategies.
Advanced Agricultural Systems
- Agentic AI and Autonomous Systems: Development of AI agents for autonomous farm management, including planning, resource allocation, and coordination of robotic systems.
- Digital Twins and Simulation: Creation of virtual farm representations for scenario planning, crop growth modeling, and testing management strategies in a simulated environment.
- Neurosymbolic AI: Integration of neural and symbolic reasoning to combine deep learning's pattern recognition capabilities with structured agricultural knowledge and expert rules.
Trustworthy and Responsible AI in Agriculture
- Interpretability and Explainability (XAI): Techniques for making AI-driven recommendations (e.g., for fertilizer application or pest control) transparent and understandable to farmers and agronomists.
- Robustness and Uncertainty Quantification: Ensuring models are reliable under real-world farm conditions (e.g., domain shift due to weather) and providing uncertainty estimates for predictions.
- Fairness, Ethics, and Policy: Addressing ethical considerations such as data ownership, algorithmic bias, and the socio-economic impact of AI on rural communities, and exploring policy frameworks for responsible AI deployment.
Dr. Alakananda Mitra
Assistant Professor
Nebraska Water Center, Institute of Agriculture and Natural Resources
University of Nebraska–Lincoln
Dr. Xin (Rex) Sun
Endowed Chair and Director
NDSU Peltier Institute
North Dakota State University
Prof. Spyros Fountas
Professor in Agricultural Engineering
Department of Natural Resources Management and Agricultural Engineering
Agricultural University of Athens
Prof. Christopher Henry
Professor
Department of Computer Science
Faculty of Science
University of Manitoba
Prof. Ignacio Ciampitti
Co-director, Institute for Digital and Advanced Agricultural Systems (IDAAS)
Purdue University
Dr. Vahab Khoshdel
Assistant Professor
Department of Electrical and Computer Engineering
University of Manitoba
Dr. Farhad Maleki
Director
Centre for Precision Agriculture
University of Calgary