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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.
(updated Oct 26, 2021)
Program online! Oct 26, 2021
Submission deadline extended to Oct 15, 2021
Submission will open at Sept 1, 2021
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.
Comming soon.
Select papers from the workshop will be published in the special issue of journal "Plant Phenomics".
Title: Advanced Application Technologies to Boost Big Data Utilization for Multiple-Field Scientific Discovery and Social Problem Solving
Title: Long-term Policy of Japan toward Sustainable and Productive Agriculture with Smart Farming
Title: Bringing Robotic Intelligence to the Field: Moving from Perception to Manipulation
Title: Learning to lead the target: plant breeding in an unpredictable world
Day 1-1 JST (UTC +9) Tue, 2 Nov 2021 21:00 ~ 23:00
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Day 1-2 JST (UTC +9) Tue, 2 Nov 2021 21:00 ~ 23:00
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Day 2-1 JST (UTC +9) Tue, 2 Nov 2021 21:00 ~ 23:00
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Day 2-2 JST (UTC +9) Tue, 2 Nov 2021 21:00 ~
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Opening remarks and Introduction of communication tools | Introduction of communication tools | Farmers Conversation | Free discussion on slack |
Keynote by Prof. Yuzuru TANAKA Prof. Seishi NINOMIYA |
Keynote by Prof. George KANTOR Prof. James SCHNABLE |
Presentations from competition winners | |
Long Presentation x 4 | Long Presentation x 4 | Award ceremony for competition winners | |
Flash talk x 8 | Flash talk x 10 | Closing remarks |
Haozhou Wang,Tang Li,Erika Nishida,Yuya Fukano,Yoichiro Kato,Wei Guo
Graduate School of Agricultural and Life Sciences, The University of Tokyo;
Haozhou Wang,Tang Li,Erika Nishida,Yuya Fukano,Yoichiro Kato,Wei Guo
Graduate School of Agricultural and Life Sciences, The University of Tokyo;Graduate School of Agricultural and Life Sciences, The University of Tokyo;Graduate School of Agricultural and Life Sciences, The University of Tokyo;Graduate School of Agricultural and Life Sciences, The University of Tokyo;Graduate School of Agricultural and Life Sciences, The University of Tokyo;Graduate School of Agricultural and Life Sciences, The University of Tokyo
Haozhou Wang,Tang Li,Erika Nishida,Yuya Fukano,Yoichiro Kato,Wei Guo
Graduate School of Agricultural and Life Sciences, The University of Tokyo;Graduate School of Agricultural and Life Sciences, The University of Tokyo;Graduate School of Agricultural and Life Sciences, The University of Tokyo;Graduate School of Agricultural and Life Sciences, The University of Tokyo;Graduate School of Agricultural and Life Sciences, The University of Tokyo;Graduate School of Agricultural and Life Sciences, The University of Tokyo
Crop Yield Prediction Integrating Genotype and Weather Variables Using Machine Learning . The competition details for participation are provided here.
Datasets will be made available here.
Paricipant teams must finalize their team members before the composition deadline is on September 18. teams joining after September 18 cannot change their team member composition during the competition phase. Prizes will be awarded to the winning teams. Funds will be paid in the most efficient manner, typically a check to winners living in the US with payment to each team member (up to 5 participants maximum). The team contact can suggest the distribution for the team members. If a team has more than five participants, five or fewer participants need to be identified to receive the prize money. For teams outside the US, prize money will be wired to a single individual representing the team. We will need full wire instructions in an appropriate format. Please note that there will be a wire fee on the receiving end of the transaction based on the recipient's bank/financial institution. At this time, we are unable to send wires to Iran, Cuba, North Korea, or Syria, therefore no prizes will be awarded there. Please note that prizes are tax reportable in the United States. Tax forms are required for payment recipients. US Citizens or permanent US residents: Form W9 including social security number and Foreign individuals: Form W-8BEN
For details regarding the competition, please contact us:
Webpage managed by Haozhou Wang, The University of Tokyo. For any concerns please contact haozhou-wang@outlook.com
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