Previous MLCAS workshops: MLCAS2022; MLCAS2021
This workshop is supported by JST (Japan), NSF (U.S.A), and USDA-NIFA (U.S.A)

Today, efficient and cost-effective sensors as well as 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 amount of data at varying spatial and temporal scales at both experimental and production agriculture levels. Therefore, both offline and real-time agricultural analytics that assimilates such heterogeneous data and provides automated, actionable information is a critical needed for sustainable and profitable agriculture.

Data analytics and decision-making for Agriculture has been a long-standing application area. The application of advanced machine learning 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 presentation of accepted papers, and a panel discussion.

Gold Sponsors
John Deere Logo Bayer Logo
Silver Sponsors
Plant Phenomics logo
Bronze sponsors
Quantomics Logo

Call for Papers

Target Participants

We invite extended 2-page-abstract for oral and/or poster presentations on topics Including but not limited to machine learning applications to plant phenotyping, plant pathology (e.g., disease scouting), plant breeding (e.g., yield prediction) and enabling smart farm management practices. We particularly encourage ML concepts applied to plant breeding, field-based experiments, production agriculture as well as lab based controlled experiments. We also encourage work that result in creating annotated benchmark datasets for ML in agriculture.

  • 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 an Microsoft CMT account, please login to your account and enter as an author for MLCAS 2023 by following this link.
  • Guidelines for Poster presentations: Poster Size: A0 (width around 84.1 cm and height around 118.9 cm) or you can use
    poster template (to be released).

Comming soon.

Publication of Papers

Select papers from the workshop will be published in the special issue of journal "Plant Phenomics".

Important Dates
  • Submission open: April 1st, 2023
  • Paper (extended abstract) deadline: May 15th->29th, 2023
  • Decision sent to authors: later in May, 2023
  • Workshop date: July 3rd~5th, 2023

Workshop Organization

Organizing Committee
  • Wei Guo, Associate Professor, Laboratory of Field Phenomics, Graduate School of Agriculture and Life Sciences, The University of Tokyo.
  • 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.
  • Soumik Sarkar, Associate Professor, Mechanical Engineering, Iowa State University.
  • Baskar Ganapathysubramanian, Professor, Mechanical Engineering, Iowa State University
  • Asheesh K. Singh, Professor, Department of Agronomy, Iowa State University.
  • Arti Singh, Assistant Professor, Department of Agronomy, Iowa State University

Keynote Speakers

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Dr. Asheesh K Singh
Professor, Soybean breeder, ssociate Chair for Discovery and engagement, Department of Agronomy, Iowa State University.

Title:Cyber-Agricultural Systems in Crop Breeding and Production

Dr.Takashi Okayasu
Professor of Department of Agro-environmental Sciences, Kyushu University.

Title: Plant Phenotyping Technology Leading to Data-driven Agriculture

Dr.Ruth Wagner
VP Head of Data Science & Analytics, Plant Biotechnology, Crop Science at Bayer

Title: AI for Agriculture R&D; Insights from Bayer Crop Science

Dr.Satoshi Iida
Senior Technical Advisor, Kubota Corporation

Title: Kubota’s Smart Agriculture and Future Directions

Dr.Tadatoshi Satow
Dr. Tadatoshi Satow
Project Professor, Professor Emeritus, Lab. of Digital Agriculture and Machinery, Obihiro University of Agriculture and Veterinary Medicine.

Title:Introduction of robotic tractors expected for upland farming in Hokkaido


Day 1

JST (UTC +9) 3 July 2023 09:00 ~ 17:00

Day 2

JST (UTC +9) 4 July 2023 09:00 ~ 17:00

Day 3

JST (UTC +9) 5 July 09:00 ~ 12:00
Opening remarks and Welcome Message from CIGR(CIGR nternational Commission of Agricultural and Biosystems Engineering) President Keynote by
Dr. Ruth Wagner
Dr. Satoshi Iida
Farmers Conversation
Keynote by
Dr. Asheesh K Singh
Dr.Takashi Okayasu
Invited Talk by
Dr. Arti Singh
Dr. Scott Chapman
Farmers Conversation
Invited Talk by
Dr. Bénédicte Fontez
Dr. Iwata Hiroyoshi
Keynote by
Dr.Tadatoshi Satow
Presentation x N Presentation x N
Presentation x N Banquet at 農家バル FOODBABY (The bar is operated by Matsuhashi Farm, which has been raising livestock and farming in Sarabetsu Village for about 100 years.)


Time zone:

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Sponsorship Information

  • Gold sponsors -- $10K, 4 free registrations, 1 sponsor table/booth
  • Silver sponsors -- $5K, 2 free registrations, 1 sponsor table/booth
  • Bronze sponsors -- $2K, 1 free registration
Translational AI Center Logo AI Institute for Resilient Agriculture COntext-Aware LEarning for Sustainable CybEr-agricultural (COALESCE) systems Japan Science and Technology Agency Logo National Science Foundation CIGR International Commision of Agricultural and Biosystems Engineering Japanese Society of Agricultural information