Agri AI 2026

First International Workshop on AI in Agriculture
Workshop at AAAI 2026
January 26, 2026
EXPO, Singapore

About Agri AI 2026

Agriculture is undergoing a rapid transformation driven by digital technologies, including IoT sensors, drones, satellite imagery, and mobile platforms, which generate vast streams of heterogeneous, temporally rich data. These advancements offer unprecedented opportunities for AI to enable smarter, more resilient, and efficient agricultural practices. As climate change increases variability in crop yields, pest outbreaks, and resource availability, traditional agricultural methods are proving inadequate in addressing these challenges. Although AI has started to tackle these issues through applications like crop forecasting, disease detection, and irrigation optimization, many existing solutions remain narrow in focus, difficult to generalize, and often lack scalability and adaptability. Furthermore, important human-centered concerns, such as transparency, fairness, and participatory system design, are often underexplored. The First International Workshop on AI in Agriculture (Agri AI), co-located at AAAI 2026, aims to address these gaps by bringing together researchers and practitioners to advance robust, responsible, and scalable AI methods tailored to real-world agricultural systems.

Call for Papers

Agri AI 2026 Call for Papers (PDF)
This workshop will cover the following research themes, including but not limited to the topics listed below:

Data Foundations

  • Multi-modal heterogeneous agriculture data collection and fusion (satellites, drones, IoT sensors, camera)
  • Large-scale, open source, high-quality benchmark datasets for AI in agriculture
  • Synthetic data generation and simulators to address uncertainty and data/label scarcity

Decision Intelligence and Scalable AI Models

  • AI-powered decision support systems for precision farming and resource optimization
  • Generative AI, foundation models, and transfer learning for agriculture
  • Edge AI, federated and distributed learning for agriculture

Applications and Real-World Deployments

  • AI-driven plant disease and insect detection, yield forecasting, etc.
  • Longitudinal study, field validation, and performance benchmarking for real-world case studies
  • Security, privacy and trustworthiness of AI in agriculture

Submission Guidelines

We invite research papers presenting original results—including deployment experiences and case studies—that have not been published previously and are not under review elsewhere. Papers may be up to 6 pages in length, including figures, tables, and references. All submissions must follow the AAAI 2026 submission format and be submitted electronically. The workshop will follow a single-blind review process; therefore, authors should include their names and contact details in the paper.
Accepted papers will be archived on the workshop website but will not appear in the official AAAI 2026 proceedings. Papers of special merit, properly extended, will be considered for possible publication in the Elsevier’s Pervasive and Mobile Computing (PMC) journal. At least one author of each accepted paper must attend the workshop in person; otherwise, the paper will be withdrawn from the program.


Submission link

All submissions must be in Adobe Portable Document Format (PDF) format through the OpenReview: https://openreview.net/group?id=AAAI.org/2026/Workshop/AgriAI

Important Dates

  • Paper submission: October 22 November 20, 2025 (AOE)
  • Notifications: November 30 December 8, 2025 (AOE)
  • Camera-ready: December 7 December 26, 2025 (AOE)
  • Workshop: January 26, 2026

Program

*All times are local (Singapore)(UTC+8)

8:25 – 8:30
Welcome address
8:30 – 9:00
Invited talk 1
  • AI-Driven Innovations in Resilient Agriculture
    Baskar Ganapathysubramaniam (Iowa State University)
9:00 – 10:00
Technical Session 1 (4 papers, 15 mins each)
  • YOLO-Guided Pretraining for Apple Counting under Limited Annotations
    Giuliano Ramírez (Universidad Andres Bello, Chile), Orietta Nicolis (University of Messina, Italy), Hans Lobel (Pontificia Universidad Catolica de Chile, Chile), Billy Peralta (Universidad Andres Bello, Chile)
  • BloomBench: A Multi-Species Benchmark for Evaluating the Generalization of Fruit Tree Phenology Models
    Ron van Bree (Wageningen University & Research, Netherlands), Diego Marcos (Inria, University of Montpellier, France), Ioannis N. Athanasiadis (Wageningen University & Research, Netherlands)
  • Learning Causal Agroecosystem Dynamics through Physics-Guided Machine Intelligence
    Mahule Roy (University of Oxford, UK), Subhas Roy (TATA Consumer Products Limited, India)
  • Drone-based 3D Reconstruction of Plants in Field Conditions Using Neural Radiance Fields (NeRFs)
    Shambhavi Joshi, Ashlyn Rairdin, Elizabeth Tranel, Talukder Jubery, Soumik Sarkar, Arti Singh, Baskar Ganapathysubramaniam, Adarsh Krishnamurthy (Iowa State University, USA)
10:00 – 10:45
Break + Poster Session
10:45 – 11:15
Invited talk 2
  • Future of Food: Challenges and Opportunities in Feeding Humanity for the Next Century
    Alok Talekar (Google Research)
11:15 – 12:15
Technical Session 2 (4 papers, 15 mins each)
  • Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting
    Peining Zhang, Hongchen Qin, Haochen Zhang, Ziqi Guo, Guiling Wang, Jinbo Bi (University of Connecticut, USA)
  • StateSpace-SSL: Linear-Time Self-supervised Learning for Plant Disease Detection
    Abdullah Al Mamun, Miaohua Zhang, David Ahmedt-Aristizabal, Zeeshan Hayder (Griffith University, Australia; Data61 CSIRO, Australia), Mohammad Awrangjeb (Griffith University, Australia)
  • PaddyVLM: An Expert-tuned Vision-Language Model for Paddy Disease Diagnosis
    Arun Kumar, Sangam Kumar Jena, Pandarasamy Arjunan (Indian Institute of Science, India)
  • LLM-Orchestrated Digital Twins for Safe, Human-Centered Decision Support in Precision Agriculture
    Charles Cao (University of Tennessee, USA), Sajal K. Das (Missouri University of Science and Technology, USA), Jie Zhuang (University of Tennessee, USA), Robert Davis (University of Tennessee Health Science Center, USA)
12:15 – 12:20
Closing remarks
12:20 – 1:15
Lunch
17:00 – 18:00
Joint panel with AI4ASE

Poster Presentations

  • An Overall Real-Time Mechanism for Classification and Quality Evaluation of Rice
    Wanke Xia, Ruoxin Peng, Haoqi Chu, Xinlei Zhu, Zhiyu Yang (China Agricultural University, China), Yiting Zhao (Tsinghua University, China), Lili Yang (China Agricultural University, China)
  • Bilinear Magic Meets CLIP: Unsupervised Learning in Pig Counting Scenario
    Yue Sun, Lili Yang (China Agricultural University, China)
  • ReproPheno and ReproPhenoNet: A Large-Scale Multimodal Benchmark Dataset and Deep Learning Framework for Reproductive-Stage Plant Phenotyping
    Sanjan Baitalik, Rajashik Datta, Utsho Banerjee (Institute of Engineering & Management, India), Rajarshi Karmakar (University of Engineering & Management, India), Vincent Stoerger (University of Nebraska-Lincoln, USA), Himadri Nath Saha (University of Calcutta, India), Sruti Das Choudhury (University of Nebraska-Lincoln, USA)
  • When YOLO Meets SAM: Data-Efficient Weed Density Estimation
    Soniya (Indian Institute of Science, India; ICASS SD, Canada), Nived Ambadipudi (Purdue University, USA; ICASS SD, Canada), Apurva Narayan (University of Western Ontario, Canada)
  • Causal-GeoSim: Evaluating Directional Robustness and Spatial Risk Awareness of Large Language Models in Climate–Agriculture Systems
    Youla Yang (Indiana University Bloomington, USA)
  • AI-Enabled Reconfigurable Edge Device for Plant Health Assessment in Greenhouse Environment
    Prabha Sundaravadivel, Rafael Reyes-Ramirez, Prajwal Mahesha Sethurani, Chase Brown, Shekhar Suman Borah (The University of Texas at Tyler, USA), Shaker Kousik (USDA-ARS, USA)
  • On-Edge Weed Detection Using Unmanned Aerial Vehicles
    Mohamed Elmohandes, Diaa Addeen Abuhani, Maya Haj Hussain, Jowaria Khan, Imran Zualkernan (American University of Sharjah, UAE)
  • Toward Multimodal Agricultural Digital Twins: Integrating Autonomous UGV Monitoring, High-Precision Phenotyping, and Diffusion-Based Generative Data Augmentation
    Yuichiro Nomura, Hiroshi Mineno (Shizuoka University, Japan)
  • PaddyFormer: An Improved RT-DETRv2 based Approach for Paddy Crop Growth Stage Detection on Drone based RGB Imagery
    Anonymous submission
  • Benchmarking Tabular Foundation Models for Agricultural Yield Prediction
    Mohammed Musthafa Rafi, Timilehin T. Ayanlade, Baskar Ganapathysubramaniam, Soumik Sarkar, Adarsh Krishnamurthy (Iowa State University, USA), Chinmay Hegde (New York University, USA), Aditya Balu (Iowa State University, USA)

Keynote Speakers

Baskar Ganapathysubramaniam
Baskar Ganapathysubramaniam
Anderlik Professor of Engineering
Iowa State University

AI-Driven Innovations in Resilient Agriculture

The AI Institute for Resilient Agriculture (AIIRA) is advancing a suite of ambitious "moonshot" projects at the nexus of artificial intelligence and agricultural resilience. I will present our efforts to transform agriculture through AI-enabled tools that are both use-inspired and foundationally rigorous. Our vision includes scalable AI agents for pest identification and mitigation, 3D digital twins of plants for ideotype breeding, multi-modal sensing for field operations, and integration of large language/reasoning models for decision support. These efforts, powered by a transdisciplinary team, aim to democratize access, enable robust decision-making, and catalyze global collaborations to ensure a sustainable and productive agricultural eco-system.

BIO

Baskar Ganapathysubramaniam is Anderlik Professor of Engineering at Iowa State University. Baskar received his BTech from IIT Madras, and a PhD from Cornell University. He directs a curiosity driven, computational sustainability group (me.iastate.edu/bglab) with research interests in the areas of scientific computing, applied mathematics, and machine learning with applications in food, energy, and healthcare systems. He is the director of the NSF/USDA funded AI Institute for Resilient Agriculture (aiira.iastate.edu) which is a multi-institutional project focused on use-inspired AI developments.

Alok Talekar
Alok Talekar
Sustainability and Agriculture Lead
Google DeepMind

Future of Food: Challenges and Opportunities in feeding humanity for the next century

The global food system is facing unprecedented challenges. In 2023, 2.4 billion people experienced moderate to severe food insecurity, a crisis precipitated by anthropogenic climate change and evolving dietary preferences. Furthermore, the food system itself significantly contributes to the climate crisis, with food loss and waste accounting for 2.4 gigatonnes of carbon dioxide equivalent emissions per year (GT CO2e/yr), and the production, mismanagement, and misapplication of agricultural inputs such as fertilizers and manure generating 2.5 GT CO2e/yr. To sustain a projected global population of 9.6 billion by 2050, food production must increase by at least 60%. Transitional sustainable agricultural practices can transform the sector from a net source of greenhouse gas emissions to a vital carbon sink. In this talk, Alok will cover the broad range of opportunities for ML experts to build transformative applications in the food and agriculture sector, and share learnings from his own team’s work at DeepMind.

BIO

Alok is the Sustainability and Agriculture lead at Google DeepMind. His team is focused on digitizing the agricultural sector using remote sensing and machine learning, to solve urgent problems faced by the sector in India and the rest of the global south, by enabling targeted data driven allocation of resources and services. He was a founding member of the Climate Trace initiative. He has been with Google in various teams for a decade, and worked in the tech industry for over 15 years.

Organization

General Co-chairs

Sajal Das
Sajal Das
Computer Science
Missouri University of Science and Technology
Pandarasamy Arjunan
Pandarasamy Arjunan
Indian Institute of Science, Bangalore
India
Soumik Sarkar
Soumik Sarkar
Mechanical Engineering
Iowa State University, USA

Technical Program Committee

Vishesh Tanwar
Vishesh Tanwar
Missouri University of Science and Technology, USA
Charles Qing Cao
Charles Qing Cao
University of Tennessee, Knoxville
Kesavan Subaharan
Kesavan Subaharan
ICAR-National Bureau of Agricultural Insect Resources
Kesavan Subaharan
Yadati Narahari
Indian Institute of Science, Bangalore