Source: IOWA STATE UNIVERSITY submitted to NRP
FACT: INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR CYBER-AGRICULTURAL SYSTEMS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1018343
Grant No.
2019-67021-28987
Cumulative Award Amt.
$50,000.00
Proposal No.
2018-08952
Multistate No.
(N/A)
Project Start Date
Nov 1, 2018
Project End Date
Oct 31, 2020
Grant Year
2019
Program Code
[A1541]- Food and Agriculture Cyberinformatics and Tools
Recipient Organization
IOWA STATE UNIVERSITY
2229 Lincoln Way
AMES,IA 50011
Performing Department
Mechanical Engineering
Non Technical Summary
The proposers in collaboration with Prof. Masayuki Hirafuji's team at the University of Tokyo, are organizing the First International workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS 2018) on October 24, 2018 as a part of the Asia-Pacific Federation for Information Technology in Agriculture and the World Congress on Computers in Agriculture (AFITA/WCCA) being held from October 24, 2018 to October 26, 2018 that will be collocated at the Victor Menezes Convention Centre at the Indian Institute of Technology, Bombay (IITB) in Mumbai, India.
Animal Health Component
(N/A)
Research Effort Categories
Basic
(N/A)
Applied
(N/A)
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
40272102020100%
Knowledge Area
402 - Engineering Systems and Equipment;

Subject Of Investigation
7210 - Remote sensing equipment and technology;

Field Of Science
2020 - Engineering;
Goals / Objectives
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. In this context, we propose to organize an International workshop on Machine Learning for Cyber-Agricultural Systems. The proposed workshop will bring international experts in this area to discuss the recent success stories and the path forward.
Project Methods
We will host multiple keynote lectures from renowned international experts in the field, a discussion panel as well as oral and 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 will particularly encourage ML concepts applied to plant breeding, field-based experiments, production agriculture as well as lab based controlled experiments.

Progress 11/01/18 to 10/31/20

Outputs
Target Audience:In this workshop, we brought together academic and industrial researchers and practitioners in the fields of machine learning, data science and engineering, plant sciences and agriculture. Changes/Problems:We wanted to host MLCAS 2020 in Tokyo, Japan in the Fall of 2020. We also had planning meetings at Iowa State and in the University of Tokyo, Japan. However, due to COVID-19, we could not have the workshop (hence, we could not use the entire fund by the end of the project period). We now plan to have MLCAS 2021 virtually later in the year. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?1. Both MLCAS 2018 and MLCAS 2019workshops wereattended by a large number of academic and industrial researchers and practitioners in the fields of machine learning, data science and engineering, plant sciences and agriculture. 2. Beyond the direct attendees, we rana Special Issue in the Plant Phenomics journal with selected papers from the MLCAS 2019 workshop that contained multiple papers that are of great relevance to the community. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? 1. We have organized the First International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS 2018) under the AFITA/WCCA 2018 in Mumbai, India. The workshop was extremely successful and well-attended. 2. We have organized the Second International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS 2019) as an independent event in Ames, IA, USA. The workshop was extremely successful and attended by around 120 participants. 3.We hosted a research roadmap panel during MLCAS 2019. We plan to publish a position paper based on this discussion in near future. 4.We successfully ran a Special Issue on "Image Analysis and Machine Learning for Cyber-Agricultural Systems" in Plant Phenomics, a Science Partner Journal with selected papers from the MLCAS 2019 workshop. https://spj.sciencemag.org/plant-phenomics-special-issues/image-analysis/

Publications


    Progress 11/01/19 to 10/31/20

    Outputs
    Target Audience:In this workshop, we brought together academic and industrial researchers and practitioners in the fields of machine learning, data science and engineering, plant sciences and agriculture. Changes/Problems:We wanted to host MLCAS 2020 in Tokyo, Japan in the Fall of 2020. We also had planning meetings at Iowa State and in the University of Tokyo, Japan. However, due to COVID-19, we could not have the workshop. We now plan to have MLCAS 2021 virtually later in the year. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?The special issue produced out of the MLCAS 2019 workshop contained multiple papers that are of great relevance to the community. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

    Impacts
    What was accomplished under these goals? We successfully ran a Special Issue on "Image Analysis and Machine Learning for Cyber-Agricultural Systems" in Plant Phenomics, a Science Partner Journal with selected papers from the MLCAS 2019 workshop.https://spj.sciencemag.org/plant-phenomics-special-issues/image-analysis/

    Publications


      Progress 11/01/18 to 10/31/19

      Outputs
      Target Audience:In this workshop, we broughttogether academic and industrial researchers and practitioners in the fields of machine learning, data science and engineering, plant sciences and agriculture. Changes/Problems:Although we proposed to host one workshop, due to the great interest shown by the community, we now began a series of these workshops in an annual basis. What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?The workshop was attended by a large number of academic and industrial researchers and practitioners in the fields of machine learning, data science and engineering, plant sciences and agriculture. Beyond the direct attendees, we are in the process of preparing and publishing a Special Issue in the Plant Phenomics journal with selected papers from the workshop as well asa white paper summarizing the outcomes a research roadmap panel hosted during MLCAS 2019.? What do you plan to do during the next reporting period to accomplish the goals?We will complete the special issue as well as do some initial preparation for MLCAS 2020.

      Impacts
      What was accomplished under these goals? 1. We have organized the FirstInternational Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS 2018) under the AFITA/WCCA 2018 in Mumbai, India. The workshop was extremely successful and well-attended. 2.We have organized the SecondInternational Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS 2019) as an independent event in Ames, IA, USA. The workshop was extremely successful and attended by around 120 participants. 3. We hosted a research roadmap panel during MLCAS 2019. We will publish a white paper summarizing the outcomes of the panel. 4. We are in the process of preparing and publishing a Special Issue in the Plant Phenomics journal with selected papers from the workshop.

      Publications