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<a class="nav-link page-scroll" href="#callforpapers">Call for Papers</a>
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<a class="nav-link page-scroll" href="#orgainization">Organization</a>
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<a class="nav-link page-scroll" href="#program">Program</a>
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<a class="nav-link page-scroll" href="#proceedings">Proceedings</a>
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<h3 class="h1-large">Third International Workshop on Machine Learning for Cyber-Agricultural
Systems <br>(MLCAS2021)</h3>
<a class="btn-solid-lg page-scroll" href="https://forms.gle/kbRhL8AWRSc8JpGVA" target="_blank">Register</a>
<p class="btn-outline-lg page-scroll"><i class="fab fa-chrome"></i> Chrome Recommended</p>
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<div class="col-xl-10 offset-xl-1">
<h5 class="text-center text-muted mt-2">This workshop is supported by JST (Japan), NSF (U.S.A), and USDA-NIFA (U.S.A)</h5>
<p class="mt-2">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.</p>
<p class="mb-3">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. </p>
<ul class="list-unstyled li-space-lg mb-3">
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Proceedings</strong>: Coming soon.</div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body" title="Absolute Reference: JST (UTC +9) Tue, 2 Nov 2021 21:00 ~ JST (UTC +9) Thu, 4 Nov 2021 23:00"><strong >Date</strong>: <span class="text-primary" id="st_tz">JST (UTC +9)</span> <span id="st_time">Tue, 2 Nov 2021 21:00</span> ~ <span class="text-primary" id="ed_tz">JST (UTC +9)</span> <span id="ed_time">Thu, 4 Nov 2021 23:00</span></div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Venue</strong>: online</div>
</li>
</ul>
<h5 class="mb-0 mt-5">What's new:</h5>
<p class="text-muted">(updated Oct 26, 2021)</p>
<p> Program online! <span class="text-primary">Oct 26, 2021</span></p>
<p> Submission deadline extended to <span class="text-primary">Oct 15, 2021</span></p>
<p> Submission will open at <span class="text-primary">Sept 1, 2021</span></p>
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<div id="callforpapers" class="container">
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<div class="col-lg-12">
<h2 class="h2-heading text-center">Call for Papers</h2>
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<div class="col-xl-10 offset-xl-1">
<h5 class="mt-4">Target Participants</h5>
<p class="mt-2">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.
</p>
<h5 class="mt-4">Guidelines</h5>
<ul class="list-unstyled li-space-lg mt-3">
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Guidelines for Extended abstract submissions</strong>: Up to
2 pages including figures and tables (excluding references). Extended <a href="/files/MLCAS2021-template.docx">abstract template</a>.</div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Submission Guidelines</strong>: 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 2021 by following <a href="https://cmt3.research.microsoft.com/MLCAS2021" target="_blank">this link</a>.</div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Guidelines for Poster presentations</strong>: Poster Size:
A0 (width around 84.1 cm and height around 118.9 cm) or you can use <br/><u>poster template</u> (to be released).
</div>
</li>
</ul>
<h5 class="mt-4">Procedure</h5>
<p>Comming soon.</p>
<h5 class="mt-4">Publication of Papers</h5>
<p>Select papers from the workshop will be published in the <a href="https://spj.sciencemag.org/plant-phenomics-special-issues/image-analysis/" target="_blank">special issue of journal "Plant Phenomics"</a>.</p>
<!--https://spj.sciencemag.org/plant-phenomics-special-issues/image-analysis/-->
<h5 class="mt-4" id="importantdates">Important Dates</h5>
<ul class="list-unstyled li-space-lg mt-3">
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Submission open</strong>: <span class="text-primary">Wednesday,
September 1st, 2021</span></div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Paper (extended abstract) deadline</strong>: <span class="text-primary">Friday,
October 15th, 2021</span></div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Decision sent to authors</strong>: <span
class="text-primary">later in October, 2021</span></div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Workshop date</strong>: <span
class="text-primary">November 2nd~4th, 2021</span></div>
</li>
</ul>
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<div class="cards-1 ex-basic-1 bg-gray">
<div class="container" id="orgainization">
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<div class="col-lg-12">
<h2 class="h2-heading text-center">Workshop Organization</h2>
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<h5 class="mt-4">Organizing Committee</h5>
<ul class="list-unstyled li-space-lg mt-3">
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Wei Guo</strong>, Assistant Professor, Field Phenomics
Laboratory, Graduate School of Agriculture and Life Sciences, The University of Tokyo.
</div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Masayuki Hirafuji</strong>,Project Professor, Field
Phenomics Laboratory, Graduate School of Agriculture and Life Sciences, The University
of Tokyo.</div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Seishi Ninomiya</strong>, Project Professor, Field Phenomics
Laboratory, Graduate School of Agriculture and Life Sciences, The University of Tokyo.
</div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Soumik Sarkar</strong>, Associate Professor, Mechanical
Engineering, Iowa State University.</div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Baskar Ganapathysubramanian</strong>, Professor, Mechanical
Engineering, Iowa State University</div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Asheesh K. Singh</strong>, Professor, Department of
Agronomy, Iowa State University.</div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong>Arti Singh</strong>, Assistant Professor, Department of
Agronomy, Iowa State University</div>
</li>
</ul>
</div>
</div>
<div class="row">
<div class="col-lg-12 mt-4">
<h4 class="mt-4 text-center text-muted">Keynote Speakers</h4>
<h6 class="text-center">Click button to see details, click again to hide</h6>
</div>
<div class="col mt-4">
<div class="card bg-gray">
<div class="card-body">
<div class="details">
<img src="images/tanaka.png" alt="alternative" height="80px">
<div class="text">
<div class="testimonial-author">Professor</div>
<div class="occupation">Tanaka Yuzuru</div>
<a role="button" class="btn btn-outline-info btn-sm" style="text-decoration: none;" data-toggle="popover" data-placement="bottom" title="Biography" data-content="Professor TANAKA Yuzuru has been a professor emeritus of Hokkaido University (2013- ), an adjunct professor of Department of Computing Science, University of Alberta (2018- ), an affiliated scientist of ICS-FORTH (Foundation for Research and Technology - Hellas )(2010- ), the research supervisor (program officer) of the JST CREST Program on Big Data Applications (2013-2021), the program officer of EIG JST CONCERT-Japan Program on ICT for Resilient, Safe and Secure Society (2020- ), an MI (Materials Informatics) research advisor of Research and Services Division of Materials Data and Integrated System at National Institute of Materials Science (2017-2019), and visiting professors of National Institute of Informatics (2004- ), Institute of Catalysis at Hokkaido University (2017- 2020), and specially appointed researcher at Comprehensive Research Organization for Science and Society (CROSS)(2019- ). He had been a full professor of computer architecture at the Department of Electrical Engineering (1990-2003), then of knowledge media architecture at the Department of Computer Science, Graduate School of Information Science and Technology (2004-2017), Hokkaido University, and the founding director of Meme Media Laboratory (1995-2013), Hokkaido University. He also worked as a visiting research fellow at IBM T.J. Watson Research Center (1985-1986), a full professor of Digital Library, Graduate School of Informatics, Kyoto University (1998-2000), and series editor of Springer’s LNAI (Lecture Notes in Artificial Intelligence). His research areas covered multiprocessor architectures, database schema-design theory, database machine architectures, full text search of document image files, and automatic cut detection in movies and full video search. His recent research areas cover meme media architectures, knowledge federation frameworks, proximity-based federation of smart objects, their application to digital libraries, e-Science, clinical trials, materials informatics, and social cyber-physical systems especially for the optimization of snow removal in Sapporo based on the big data analysis of weather, traffic and road conditions.">Bio</a>
</div>
</div>
<p class="testimonial-text">Title: Advanced Application Technologies to Boost Big Data Utilization for Multiple-Field Scientific Discovery and Social Problem Solving</p>
</div>
</div>
</div>
<div class="col mt-4">
<div class="card bg-gray">
<div class="card-body">
<div class="details">
<img src="images/ninoimiya.png" alt="alternative" height="80px">
<div class="text">
<div class="testimonial-author">Professor</div>
<div class="occupation">Ninomiya Seishi</div>
<a role="button" class="btn btn-outline-info btn-sm" style="text-decoration: none;" data-toggle="popover" data-placement="bottom" title="Biography" data-content="Prof. Seishi Ninomiya originally studied applied plant genetics and biometrics, and received his PhD in Agricultural Sciences from the University of Tokyo in 1982. Since then, he has worked in National Agriculture and Food Research Organization as the director of Department of Agricultural Information Research and, at the University of Tokyo as a professor at the Institute of Agro-ecosystem Services. Currently, he is the project professor of Field Phenomics Laboratory of the same university. And Invited professor at Plant Phenomics Resarch Center, Nanjing Agricultural University.
He was one of the pioneers who introduced computer science to agricultural sciences and has conducted several research topics in agriculture using data mining, machine learning, pattern recognition etc. He was appointed as the national project leader of Japan for ICT application to agriculture in 1996 and suggested the importance of virtual and dynamic integration of distributed models and databases to realize truly useful decision support systems in agriculture. Since then, he has organized several national-level projects on ICT in agriculture as the leader. His current studies mainly focus on plant phenomics based on the latest data science, targeting a wide variety of crops and their application to accelerate crop breeding. He has also been involved in several international R&D programs for ICT in agriculture, particularly in the Asia and Pacific regions. He is also the incoming President of the International Commission of Agricultural and Biosystems Engineering (CIGR).
">Bio</a>
</div>
</div>
<p class="testimonial-text">Title: Long-term Policy of Japan toward Sustainable and Productive Agriculture with Smart Farming</p>
</div>
</div>
</div>
<div class="col mt-4">
<div class="card bg-gray">
<div class="card-body">
<div class="details">
<img src="images/george.png" alt="alternative" height="80px">
<div class="text">
<div class="testimonial-author">Professor</div>
<div class="occupation">George Kantor</div>
<a role="button" class="btn btn-outline-info btn-sm" style="text-decoration: none;" data-toggle="popover" data-placement="bottom" title="Biography" data-content="George Kantor is a Research Professor at Carnegie Mellon University’s Robotics Institute. He has over 20 years of experience research in developing and deploying robotic technologies for real-world applications in military, agriculture, mining, and scientific exploration domains. His technical interests lie in position estimation and mapping for mobile robots, control of robotic systems with nontrivial dynamics, off-road autonomous driving, and deep learning for image analysis and sensor fusion “on the edge”. His work translates ideas related to these technical interests into rugged robotic systems that reliably solve real-world problems. He has led tech development in multi-institutional programs to investigate and develop (1) robotic technologies for agriculture, including field robots for rapid in-field phenotyping and sensor networks for environmental control;(2) general purpose autonomous off-road driving with all-terrain vehicles; (3) perception and positioning technologies for multi-robot systems performing cooperative mapping and assembly tasks; and (4) mapping, mission planning, and distributed communications to support collaborative unmanned air and ground vehicle re-supply missions; ">Bio</a>
</div>
</div>
<p class="testimonial-text">Title: Bringing Robotic Intelligence to the Field: Moving from Perception to Manipulation</p>
</div>
</div>
</div>
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<div class="testimonial-author">Professor</div>
<div class="occupation">James Schnable</div>
<a role="button" class="btn btn-outline-info btn-sm" style="text-decoration: none;" data-toggle="popover" data-placement="bottom" title="Biography" data-content="James Schnable is an Associate Professor and the Gardner Professor of Maize Quantitative Genetics at the University of Nebraska - Lincoln. With over a decade of experience in plant genetics, he maintains a diverse relationship between multiple sciences within his lab and research. Schnable leads and mentors a team of postdoc, graduate students, and technicians in his position in the Department of Agronomy and Horticulture and the Center for Plant Science Innovation at the University of Nebraska - Lincoln. His research is currently supported by the US Department of Agriculture, the National Science Foundation, the Department of Energy, and the Nebraska Corn Growers. He is especially interested in harnessing new technologies from engineering and computer sciences and integrated them into maize and sorghum genetic and genomic research. As a founding partner in three successful startups in his field, Schnable understands the importance of innovative pathways combined with academic research. Data2Bio, Dryland Genetics, and EnGeniousAg continue to break new ground in the agriculture and genomic sectors.
He holds a BA in Biology from Cornell University and a Ph.D. in Plant Biology from UC-Berkeley. He was NSF Plant Genome Fellowship supported postdoctoral scholar at the Danforth Center in St. Louis and the Chinese Academy of Agricultural Sciences in Beijing, China. He received the Marcus Rhoades early career award for maize genetics in 2018, the North American Plant Phenotyping Network Early Career award, and the American Society of Plant Biologists Early Career Award in 2019.
">Bio</a>
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<p class="testimonial-text">Title: Learning to lead the target: plant breeding in an unpredictable world</p>
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<h3 class="text-center"><span style="font-weight:bold">Day 1-1</span></h3>
<div title="Absolute Reference: JST (UTC +9) Tue, 2 Nov 2021 21:00~23:00"> <span class="text-primary" id="tz11">JST (UTC +9)</span> <span id="st_day11">Tue, 2 Nov 2021 21:00</span> ~ <span id="ed_day11">23:00</span></div>
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<th class="border">
<h3 class="text-center"><span style="font-weight:bold">Day 1-2</span></h3>
<div title="Absolute Reference: JST (UTC +9) Wed, 3 Nov 08:00 ~ 10:00"> <span class="text-primary" id="tz12">JST (UTC +9)</span> <span id="st_day12">Tue, 2 Nov 2021 21:00</span> ~ <span id="ed_day12">23:00</span></div>
</th>
<th class="border">
<h3 class="text-center"><span style="font-weight:bold">Day 2-1</span></h3>
<div title="Absolute Reference: JST (UTC +9) Wed, 3 Nov 2021 21:00 ~ 23:00"> <span class="text-primary" id="tz21">JST (UTC +9)</span> <span id="st_day21">Tue, 2 Nov 2021 21:00</span> ~ <span id="ed_day21">23:00</span></div>
</th>
<th class="border">
<h3 class="text-center"><span style="font-weight:bold">Day 2-2</span></h3>
<div title="Absolute Reference: JST (UTC +9) Wed, 3 Nov 2021 23:00 ~"> <span class="text-primary" id="tz22">JST (UTC +9)</span> <span id="st_day22">Tue, 2 Nov 2021 21:00</span> ~ </div>
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<!--tr>
<td>JST(UTC +9) Tue, 2 Nov 21:00 ~ 23:00<br />
CDT(UTC -5) Tue, 2 Nov 07:00 ~ 09:00</td>
<td>JST(UTC +9) Wed, 3 Nov 08:00 ~ 10:00<br />
CDT(UTC -5) Tue, 2 Nov 18:00 ~ 20:00</td>
<td>JST (UTC +9) Wed, 3 Nov 2021 21:00 ~ 23:00<br />
CDT(UTC -5) Wed, 3 Nov 2021 07:00 ~ 09:00</td>
<td>JST (UTC +9) Wed, 3 Nov 2021 23:00 ~<br />
CDT(UTC -5) Wed, 3 Nov 2021 09:00 ~</td>
</tr-->
<tr>
<td width="25%">Opening remarks and Introduction of communication tools</td>
<td class="pt-3" width="25%">Introduction of communication tools</td>
<td class="pt-3" width="25%">Farmers Conversation </td>
<td class="pt-3" width="25%" rowspan="4">Free discussion on slack</td>
</tr>
<tr>
<td>Keynote by <br />Prof. Yuzuru TANAKA<br/>Prof. Seishi NINOMIYA</td>
<td>Keynote by <br />Prof. George KANTOR<br/>Prof. James SCHNABLE</td>
<td>Presentations from competition winners</td>
</tr>
<tr>
<td>Long Presentation x 4</td>
<td>Long Presentation x 4</td>
<td>Award ceremony for competition winners</td>
</tr>
<tr>
<td>Flash talk x 8</td>
<td>Flash talk x 10</td>
<td>Closing remarks</td>
</tr>
</tbody>
</table>
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<h6 class="text-center text-muted">Time zone: <span id="proceed_tz"></span></h6>
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<h5>01</h5>
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<div class="col-md-2 col-10">
<p><span style="font-weight:bold">Haozhou Wang,Tang Li,Erika Nishida,Yuya Fukano,Yoichiro Kato,Wei Guo</span></p>
</div>
<div class="col-md-3">
<p>Graduate School of Agricultural and Life Sciences, The University of Tokyo;</p>
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<div class="col-md-6">
<p><a href="#">Cost-efficient broccoli head phenotyping using aerial imagery and SfM-based weakly supervised learning</a></p>
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<h5>01</h5>
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<div class="col-md-2 col-10">
<p><span style="font-weight:bold">Haozhou Wang,Tang Li,Erika Nishida,Yuya Fukano,Yoichiro Kato,Wei Guo</span></p>
</div>
<div class="col-md-3">
<p>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</p>
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<p><a href="#">Cost-efficient broccoli head phenotyping using aerial imagery and SfM-based weakly supervised learning</a></p>
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<h5>01</h5>
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<div class="col-md-2 col-10">
<p><span style="font-weight:bold">Haozhou Wang,Tang Li,Erika Nishida,Yuya Fukano,Yoichiro Kato,Wei Guo</span></p>
</div>
<div class="col-md-3">
<p>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</p>
</div>
<div class="col-md-6">
<p><a href="#">Cost-efficient broccoli head phenotyping using aerial imagery and SfM-based weakly supervised learning</a></p>
</div>
</div>
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<h5 class="mt-4">Topic</h5>
<p class="mt-2"><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252402"> Crop Yield Prediction Integrating Genotype and Weather Variables Using Machine Learning </a>. The competition details for participation are provided <a href="https://eval.ai/web/challenges/challenge-page/1251/overview"> here. </a>
</p>
<h5 class="mt-4" id="importantdates">Important Dates </h5>
<ul class="list-unstyled li-space-lg mt-3">
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong >Sep 3</strong>: Start Date</div>
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<div class="media-body"><strong >Sep 18</strong>: Team composition Deadline</div>
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<div class="media-body"><strong >Oct 25</strong>: Final Submission Deadline</div>
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<div class="media-body"><strong >Oct 28</strong>: Announcement of Results</div>
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<h5 class="mt-4" id="importantdates">Award amounts </h5>
<ul class="list-unstyled li-space-lg mt-3">
<li class="media">
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<div class="media-body"><strong >1st prize </strong>: $2000</div>
</li>
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<i class="fas fa-square"></i>
<div class="media-body"><strong >2nd prize </strong>: $1500</div>
</li><li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong >3rd prize</strong>: $1000</div>
</ul>
<h5 class="mt-4">Datasets</h5>
<p><a href="https://eval.ai/web/challenges/challenge-page/1251/overview"> Datasets will be made available here. </a>
</p>
<h5 class="mt-4">Disclaimer</h5>
<p class="mt-2">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</p>
<h5 class="mt-4" id="importantdates">Contacts </h5>
<p class="mt-2">For details regarding the competition, please contact us:
</p>
<ul class="list-unstyled li-space-lg mt-3">
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong >Tryambak Gangopadhyay</strong>, PhD candidate, Mechanical Engineering, Iowa State University: [email protected]</div>
</li>
<li class="media">
<i class="fas fa-square"></i>
<div class="media-body"><strong >Koushik Nagasubramanian</strong>, PhD candidate, Electrical Engineering, Iowa State University: [email protected]</div>
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