Previous MLCAS workshops: MLCAS2024; MLCAS2023

Today, efficient, cost-effective sensors and high performance computing technologies are looking to transform traditional plant-based agriculture into an efficient cyber-physical system. The easy availability of cheap, deployable, connected sensor technology has created an enormous opportunity to collect vast amounts of data at varying spatial and temporal scales at both experimental and production agriculture levels. Therefore, both offline and real-time agricultural analytics that assimilate such heterogeneous data and provide automated, actionable information are critical for sustainable and profitable agriculture.

Data analytics and decision-making for Agriculture has been a long-standing application area. The application of advanced Artificial Intelligence (AI) and Machine Learning (ML) methods to this critical societal need can be viewed as a transformative extension for the agriculture community. In this workshop, we intend to bring together academic and industrial researchers and practitioners in the fields of machine learning, data science and engineering, plant sciences and agriculture, in the collaborative effort of identifying and discussing major technical challenges and recent results related to machine learning-based approaches. It will feature invited talks, oral/poster presentations of accepted papers, and an Ag-ML competition.

Registration categories and fees
Note:
Category Before August 1 After August 1 (onside payment only)
Industry Professional ¥20,000 ¥25,000
Academia/Non-Profit/Start-Up ¥15,000 ¥20,000
Students ¥10,000 ¥15,000
Gold Sponsors
Silver Sponsors
Bronze sponsors

Call for Contributions

Target Participants

The Seventh International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS) 2025 will focus on the theme “Convergence of Multi-Modal Sensing, Multi-Omics Integration, and Multi-Platform Analytics for Next-Generation Cyber-Agricultural Systems.” This year’s workshop aims to explore how synergizing heterogeneous data streams—from spectral imaging and IoT sensor networks to genomic and environmental datasets—can revolutionize precision agriculture. By bridging advances in machine learning with multi-scale biological, environmental, and operational data, MLCAS2025 will address critical challenges in crop resilience, resource optimization, and climate-smart farming. The workshop will emphasize computational frameworks capable of harmonizing data across spatial, temporal, and biological scales, enabling predictive digital twins that integrate plant physiology, field conditions, and management practices.

Guidelines
  • Guidelines for Extended abstract submissions: Up to 2 pages including figures and tables (excluding references). Extended abstract template.
  • Submission Guidelines: Submissions are through Microsoft CMT. If you do not have an Microsoft CMT account, please create one first. If you already have a Microsoft CMT account, please login to your account and enter as an author for MLCAS 2025 by following this link.
Acknowledgement

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support..

Important Dates
  • Submission open: Match 16
  • Paper (extended abstract) deadline: May 30 (Friday, AoE)
  • Decision sent to authors: June 15
  • Competition date: See below

Workshop Organization

Organizing Committee
  • Soumik Sarkar, Professor, Mechanical Engineering, Iowa State University
  • Arti Singh, Associate Professor, Department of Agronomy, Iowa State University
  • Wei Guo, Associate Professor, Field Phenomics Laboratory, Graduate School of Agriculture and Life Sciences, The University of Tokyo
  • Ian Stavness, Professor, Department of Computer Science, the University of Saskatchewan
  • Baskar Ganapathysubramanian, Professor, Mechanical Engineering, Iowa State University
  • Asheesh K. Singh, Professor, Department of Agronomy, Iowa State University
  • Masayuki Hirafuji, Project Professor, Field Phenomics Laboratory, Graduate School of Agriculture and Life Sciences, The University of Tokyo
  • Seishi Ninomiya, Project Professor, Field Phenomics Laboratory, Graduate School of Agriculture and Life Sciences, The University of Tokyo

Speakers

alternative
Dr. Hiroyoshi Iwata
Professor, Graduate School of Agricultural and Life Sciences
University of Tokyo
alternative
Dr. Yan-Fu Kou
Professor, Department of Biomechatronics Engineering,
National Taiwan University
alternative
Dr. Haiyan Ceng
Professor, College of Biosystems Engineering and Food Science
Zhejiang University
alternative
Dr. Alexander Bucksch
Associate Professor, College of Agriculture, Life and Environmental Sciences
The University of Arizona
alternative
Dr.
Professor, College of
The University of
alternative
Dr.
Professor, College of
The University of

Program(TBD)

Time
(JST)
Session Chair
Activity
8:30 - 9:00 AM Dr. Wei GUO Coffee and Welcome Address
3:00 - 3:15 PM Break
3:15 - 3:45 PM Dr. Ian Stavness Competition Presentations and Award Ceremony
3:45 - 5:00 PM Poster Session, Networking and Sponsor Booths

Competition

Topic

Global Wheat Full Semantic Segmentation

The details for participation can be found here.

Important Dates
  • March 16, 2025: Start Date
  • June 16, 2025: Development Phase Deadline
  • June 17, 2025: Start of test phase
  • June 30, 2025: Final Submission Deadline
  • July 15, 2025: Announcement of Results
Prize
  • Two prizes will be awarded for the GWFSS Competition. The prize will be one travel award per team (reimbursement of flight, accommodation, and conference registration fees, up to a maximum of $4,000 USD) for presenting the competition solution at one affiliated academic conference/workshop: select one from MLCAS (Tokyo, August 5-6 2025), EPPS (Bonn, September 16-19 2025), or CVPPA (Honolulu, October 19-25 2025).
  • Prize 1: Top Performance Award
  • Prize 2: Innovation Award
Disclaimer

To be eligible for the prizes, participants will have to release the code to their solutions under an open source license of their choice and agree for a post-competition presentation and interview. The submitted code must be reproducible and produce the same score as on the leaderboard. Winners shall, as a condition for receiving their prize, grant to the Organizer a perpetual, worldwide, non-exclusive, royalty-free, transferable, irrevocable license to use their Submission Materials (and any intellectual property relating thereto) for any purpose, including the right to reproduce, modify, prepare derivative works, publicly display, sublicense, and distribute them. The winning participants will have to provide a valid and unexpired ID card or passport which the organizer will use only for the purpose of verifying the individual's identity in case of travel reimbursement and for internal record keeping. Any and all prize(s) is(are) non transferable. All taxes, fees, and expenses associated with participation in the Challenge or receipt and use of a prize are the sole responsibility of the Prize Winner(s). No substitution of prize or transfer/assignment of prize to others or request for the cash equivalent by winners is permitted. Acceptance of prize constitutes permission for the Organizers to use the winner’s name and entry for purposes of advertising and trade without further compensation unless prohibited by law.

Contacts

For details regarding the competition, please contact us:

  • Shuai Xiang, The University of Tokyo (JP): shuai.xiang@fieldphenomics.com
  • Keyhan Najafian, University of Saskatchewan (CA): keyhan.najafian@usask.ca
  • Zijian Wang, The University of Queensland (AU): zijian.wang@uq.edu.au

Sponsorship Information

  • Gold sponsors -- ¥300K, 3 free registrations
  • Silver sponsors -- ¥200K, 2 free registrations
  • Bronze sponsors -- ¥100K, 1 free registration
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