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
Objectives :
  • to share experiences in ICDT for Agriculture between member and/or organizations
  • to establish research network among member organizations in specific items thus to secure the region food production
  • to work and share with others working group under APAN and friend of APAN
  • to report the activities to APAN committee
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)

Nowadays, some agricultural machineries have some sensors such as cameras or GPS. These sensors are used as task support equipments, e.g. a rear-view monitor and GPS guidance system. Ordinarily, these data are not logged. By collecting and analyzing sensor data, we can obtain the state of plant growth, trajectory of machinery and the details of the form work. The sensors generate enormous amount of information. So it is necessary to handle them efficiently. As a mechanism to deal with sensor data, we are developing "apras" which is a cloud system to support the production process management of agriculture for each field."apras" can CRUD (Create, Read, Update, Delete) all data via REST API. To extend "arpas" APIs, we can easily implement new APIs for sensor data. And we can develop some viewers for browser to display machinery works, like trajectory, velocity, work status, camera-view and so on. We are using Google Maps API to display field maps at this moment. But, resolution of satellite image is not so high for agricultural use. So, we are trying to use a UAV (Unmanned Aerial Vehicle) to take pictures of the fields. We can obtain high resolution field images and also record the growth of plants. We have already taken some pictures with UAV and displayed on browser using Leaflet.js (JavaScript library for maps). But it takes thousands of man-hours to put multiple photos together and correct geometric distortion. These are future works. - Slides

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


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