The fieldserver is an Internet based observation robot that can provide an outdoor solution for monitoring environmental parameters in real-time. The data from its sensors can be collected to a central server infrastructure and published on the Internet. The information from the sensor network will contribute to monitoring and modeling on various environmental issues in Asia, including agriculture, food, pollution, disaster, climate change etc. An initiative called Sensor Asia is developing an infrastructure called Sensor Service Grid (SSG), which integrates fieldservers and Web GIS to realize easy and low cost installation and operation of ubiquitous field sensor networks.
We developed a mechanism for seamlessly providing weather data and long-term historical climate data from a gridded data source through an international standard web API, which was the Sensor Observation Service (SOS) defined by the Open Geospatial Consortium (OGC). The National Agriculture and Food Research Organization (NARO) Japan has been providing gridded climate data consisting of nine daily meteorological variables, which are average, minimum, maximum of air temperature, relative humidity, sunshine duration, solar radiant exposure, downward longwave radiation, precipitation and wind speed for 35 years covering Japan. The gridded data structure is quite useful for spatial analysis, such as developing crop suitability maps and monitoring regional crop development. Individual farmers, however, make decisions using historical climate information and forecasts for an incoming cropping season of their farms. In this regard, climate data at a point-based structure are convenient for application development to support farmers' decisions. Through the proposed mechanism in this paper, the agricultural applications and analysis can request point-based climate data from a gridded data source through the standard API with no need to deal with the complicated hierarchical data structure of the gridded climate data source. Clients can easily obtain data and metadata by only accessing the service endpoint. The mechanism also provides several web bindings and data encodings for the clients' convenience. Caching, including the pre-caching mechanism, was developed and evaluated to secure an effective response time. The mechanism enhances the accessibility and usability of the gridded weather data source, as well as SOS API for agricultural applications.
An agricultural information service platform, called FieldTouch, is being built and tested on geospatial data infrastructure and crop modeling framework. More than 100 farmers in Hokkaido, Japan, have been participating on this development and are utilizing the services for optimizing their daily agricultural practices, e.g., planning and targeting areas where to apply fertilizer more to enhance homogeneity of growth and robustness of crops in their fields.FieldTouch integrates multi-scale sensor data for field monitoring, provides functionality for recording agricultural practices, then supports farmers in decision making e.g., fertilizer management. RapidEye satellite images are being used for monitoring vegetation status updated every two weeks. Field sensor data from 25 nodes record soil moisture and temperature data at different soil depths, and suites of meteorological variables e.g., rainfall, minimum and maximum temperature, solar radiation, wind, etc. every 10 minutes. Data from national weather observation network, AMeDAS, is also a source of daily weather data. We used "cloudSense" sensor backend service that serves meta-data and data to FieldTouch via a standard web service called SOS (Sensor Observation Service), which brought great flexibility and enhanced automation of system's operation.Using agronomic data from experimental station, the cultivar parameters (genetic coefficients) of a local wheat variety were calibrated for the DSSAT (Decision Support System for Agrotechnology Transfer) crop model using data assimilation. These were built in a web-based DSSAT wheat crop model called Tomorrow's Wheat (TMW) where in a user can explore the effects of timing of sowing at a given climatic condition, soil and crop management. TMW accesses long-term weather data from the on-line observation station up to the most recent archive, parameterize a built-in weather generator, then generate 100 weather scenarios then runs the wheat model at the chosen planting date, then two weeks, and one week before and after that. The yields are presented as distribution of yields at these different planting options. Future developments are going-on to personalize more the system so that the user can input fertilizer scenario, and be able also to apply seasonal climate forecast, and link to the 25 sensor nodes to simulate current plant conditions given a management scenario. In this way, the user can be informed better on how to manage their sources of vulnerabilities in their fields.
A spinach field monitoring system has been setup near Chiang Mai, Thailand which collects various sensor data and sends them to SSG server at AIT. SSG is an initiative to realize "sensor plug&play" which covers sensor node installation, registration and visualization of sensor data. This system is an ideal example to bridge the confidence between agricultural producers in Thailand and consumers in Japan through real time sensor monitoring.
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