|Agriculture Working Group Meeting|
|Chairs :||Pisuth Paiboonrat (Chair)
Ye-Nu Wan (Co-chair)
Takuji Kiura (Co-chair)
J. Adinarayana (Co-chair)
|Members :||AG WG members|
|Target Audience :||Members, Participants who are interested in joint AG WG activities|
|Expected Number of Participants :||30|
|Agenda :||11.00-11.30 Continue session of IoTs for Agriculture
Title: Data collection service for agricultural information by using "apras" for IoT
Authors: Atsushi Itoh (NARO, Japan)
Title: IoT for Agriculture in Japan
Authors: Takuji Kiura, Masayuki Hirafuji, Tomokazu Yoshida (NARO, Japan)
It seems that how to connect things in agriculture is not big problem in Japan. For example, UECS is a IoT for green houses or plant factory. For agricultural machine, ISO11783 exists. How to build "Agricultural Bit Data" and how to use it are investigated in Japan.
Cloud Open Platform in Agriculture (CLOP) encourages to open APIs and XML schema used in agricultural could services to share their data. Field sensing environmental data, crop observation data, remote sensing data (Satellite and UAV), agricultural machine data, and work data are targets of CLOP. CLOP also tries to structure agricultural terms used in data to make "Agricultural Big Data" and utilize it to Japanese agriculture. "Nosho Navi" is a project to clarify knowledges and skills of good agricultural farmers and transfer them to young farmers. In Nosho Navi, the physical data of farmers is also collected additionally. Many members of Nosho Nave are also join CLOP, but physical data is out of scope of CLOP, unfortunately.
For "Food Supply Chain", traceability is a big issue, so there is a good information system already in logistics and used to optimize logistics. To utilize data more efficiently, some project tries to use data with SNS and other Internet services and get some information for marketing. Food Communication Project, are focused on cooperative works and fair information sharing among food industries.
It seems that the ground design agricultural information system is lacked in Japan. Cooperative works among related fields are required in Japan. - Slides
11.30-12.00 Continue session of Rice Phenomic s Network
Title: Integration of RS and GIS, Field Measurement for Supporting Smart Farm Project in Huaykhamin, Saraburi, Thailand
Authors: Associate Professor, Dr. Masahiko Nagai (remote speaker), Center of Spatial Information Science, The University of Tokyo, Japan, Kulapramote Prathumchai, Asian Institute of Technology, Thailand
Smart Farm is properties dream of farmer that Ministry of Agriculture and Corporative has been targeting to achieve, with corroboration of National Electronics and Computer Technology (NECTEC), Asian Institute of Technology students, Rice Department, Sub-district Administrative Organization. Royal Irrigation Department, Department of Agricultural Extension etc. (Rice Bowl magazine, Dec.2013). Huaykhamin subdistrict in Nong Khae district of Saraburi province Thailand is first smart farm model in rice field, which selected the Huaykhamin Community Rice Seed Center for pilot project. The project is introduced a turnkey project as a service, it offers several service such as, soil and fertilizer analysis. Together with Geo-informatic technologies such as using Unmanned Aerial Vehicle (UAV), Satellite images and field survey measurement as source of database. The result and method of the yield estimation research will be attached on this Agriculture Information system as a predictive rice yield on web GIS system.
The objective of research is to study rice growth parameters and identify relation rice parameters for rice yield estimation. This research has been conduced in Hauykhmin sub-district in Sarabui provinces of Thailand. Amount of sample farm are 3 paddy plots, using field survey measurement technique to observe rice characteristic on study site at start rice planting until harvesting period which operated during rice primary crop season plantation on July to November 2013. Initially, data collection is aimed to support SRI Smart Farm project, creating paddy map for GIS database and field data collection to continue crop yield estimation. The result of the study receive rice reflectance in each stage compare with vegetation index (NDVI) which calculate from spectrometer measurement and Leaf Area Index (LAI) taken from field measurement that useful to analysis rice plantation area, rice yield and rice condition in different three farms. The outcomes showed that there is significant correlation between rice growth parameters and rice yield - Slides
12.00-12.30 Conclusion of AG WG activities, and next step
|Seating Arrangement :||Classroom|
|Video Conferencing Facility :||Yes|
|Remarks :||Combined session|