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Home>Major Activities>Seminars and Workshops in 2019>International Symposium on Developing Innovation Strategies in the Era of Data-driven Agriculture



Date
October 29-30, 2019
Venue
RDA, Jeonju, Korea
Co-organizer
Rural Development Administration (RDA)
Background / Highlights of Activity

1. Rationale:

FAO has declared that intelligent agriculture or smart farming is the key to the future. Farmers have access to computer-guided equipment with GPS for tasking crop protection agents and fertilizer application. Sensors loaded at unmanned aerial vehicle (UAV) and satellite for farm observation provide agricultural information service to farmers, farm consultant, and agricultural processing industries, to help them understand spatial differences in their crops & their health and soil & field condition, and produce tasking map of treatment instructions for agricultural practices and policy. It is expected that the agriculture sector will be the one of the largest users of UAV in the near future. Sensor networks based on the Internet of things (IoT) are increasingly being used to meet the challenge of appropriate crop and field management decision making from the big data generated by these systems. Emerging artificial intelligence (AI) technology facilitates farmers to analyze the overwhelming data, and the significance hidden in the avalanche of that data.

The primary aims of this seminar are 1) to address huge potentials for AI technology to revolutionize agriculture, 2) to assess the current status and challenges of UAV and other remote sensing technologies for smart agriculture, and 3) to develop cooperative innovation strategies towards data-driven agriculture, in order to identify research outputs with potential applications for small-scale farmers in Asia and the Pacific region.

2. Objectives

  • Share information on the state of the art research in agriculture;
  • Provide a venue for exchange of ideas, emerging technologies and knowledge on smart agriculture with potential applications in the Asian and Pacific region; and
  • Establish collaboration in smart research that must be pursued at multilateral levels.

 

3. Major Findings and Recommendations

  • Utilize data-driven agriculture to meet the challenges of food security, aging farmers, sustainable development, farming activity and production efficiency, especially for the small household farmers in the Asia-Pacific region by sensors, unmanned vehicles, cloud database, and AI algorithms.
  • Link and connect various collaborated database, agricultural service providers and farmer users for precise farming.
  • Protect and supervise privacy, ownership, transparency of data under the government official regulation, thus ensuring the incentive for business development and private investments.
  • Strengthen knowledge sharing and technical exchange to solidify the capacity building capability of digital agriculture.

 


Group Photo

Program

Program:(5)

Papers

Artificial intelligence and robotics-driven digital agriculture | PPT:(5)
Dr. Kyeong-Hwan Lee
Chonnam National University, Gwangju, Republic of Korea

The bigdata management and use case study for agriculture based on data | PPT:(10)
Dr. Yongbeen Cho
Rural Development Administration, Jeonju, Republic of Korea

Agriculture 4.0: Data driven approach to galvanize Malaysia's agro-food sector development | PPT:(7)
Dr. Ahmad Safuan Bin Bujang
Agricultural Research and Development Institute, Serdang, Malaysia

Application of common information platform to foster data-driven agriculture in Taiwan | PPT:(4)
Dr. Jyh-Rong Tsay
Taiwan Agricultural Research Institute, Taichung, Taiwan

Developing a database of agricultural production and rural development in Vietnam | PPT:(4)
Dr. Mai Van Trinh
Institute for Agricultural Environment, Vietnam Academy of Agricultural Sciences, Hanoi, Vietnam

Agricultural data collaboration platform: WAGRI – system structure and operation  | PPT:(6)
Dr. Shigehiko Hayashi
National Agriculture and Food Research Organization, Tokyo, Japan

Toward prediction of agricultural traits using multi-omics Model | PPT:(6)
Dr. Yang Jae Kang
Gyeongsang National University, Republic of Korea

Remote sensing, UAV, and Spectroscopy: A Game changer in Philippine agriculture  | PPT:(4)
Dr. Ronaldo T. Alberto
Central Luzon State University, Philippines

Trends of AIoT applications in smart agriculture | PPT:(8)
Dr. Ta-Te Lin
National Taiwan University, Taipei, Taiwan

Shared geolocaiton gateway lora network: An alternative smart farm deployment strategic infrastructure | PPT:(4)
Dr. Jeong Hyop Lee
Data Alliance, Republic of Korea

Current status and challenges of UAV and other remote sensing technologies for smart(er) agriculture in Thailand | PPT:(5)​​​​​​​
Dr. Poonpipope Kasemsap
Kasetsart University, Thailand

Project SARAI technologies for climate-smart agriculture in the Philippines | PPT:(6)​​​​​​​
Dr. Nelio C. Altoveros
University of the Philippines Los Baños, Laguna, Philippines

Drones application in farming management in Taiwan | PPT:(4)​​​​​​​​​​​​​​
Mr. Horng-Yuh Guo
Taiwan Agricultural Research Institute, Taichung, Taiwan

Proceedings

Seminar Proceeding:(5)