Andrew Terhorst

Wireless sensor network deployment for water use efficiency in irrigation

Australia is facing a severe water shortage due to below-average rainfall received over the past decade. The agricultural industry is significantly affected by this shortage due to its high water demands. It is important to adopt changes in agricultural practices and employ innovative ideas for the agricultural industry to maintain its current rate of production. Sensor technology can be used to study soil dynamics based on information gathered at regular intervals, and the data collected can be used as feedback to improve irrigation efficiency. In this paper, we describe our experiences in the design, development and deployment of a wireless sensor network to improve water use efficiency for pasture production. Sensor nodes, called sensor pods, were developed using off the shelf components. The design of the sensor pod was a challenging task as the installation has to withstand seasonal weather changes, and be resistant to damages that may be inflicted by cattle in the field. Each sensor pod measures soil moisture, temperature and humidity. Granular matrix sensors are used to measure soil moisture at three different ground depths. Temperature and humidity are measured using the Tmote Skys on-board sensors. 70 sensor pods were deployed at the TIAR (Tasmanian Institute for Agricultural Research) Elliott Research Farm near Burnie, in the North West of Tasmania, Australia, at the end of December 2007. Preliminary results are now available. The data gathered will be used to develop efficient data evaluation techniques so that irrigation regimes can be automated. This will lead to precision agricultural techniques involving the close monitoring of the field state, and the use of real time data to drive more efficient irrigation practices.

StarFL – a modularised metadata language for sensor descriptions

An ever-increasing number of sensor resources are being exposed via the World Wide Web to become part of the Digital Earth. Discovery, selection and use of these sensors and their observations require a robust sensor information model, but the consistent description of sensor metadata is a complex and difficult task. Currently, the only available robust model is SensorML, which is intentionally designed in a very generic way. Due to this genericness, interoperability can hardly be achieved without the definition of application profiles that further constrain the use and expressiveness of the root language. So far, such SensorML profiles have only been developed up to a limited extent. This work describes a new approach for defining sensor metadata, the Starfish Fungus Language (StarFL) model. This language follows a more restrictive approach and incorporates concepts from the recently published Semantic Sensor Network Ontology to overcome the key issues users are experiencing with SensorML. StarFL defines a restricted vocabulary and model for sensor metadata to achieve a high level of interoperability and a straightforward reusability of sensor descriptions.

Bulk soil moisture estimation using CosmOz cosmic ray sensor and ANFIS

This paper investigates a novel technique based on Fuzzy C means (FCM) algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate soil moisture using cosmic ray soil moisture probes deployed across Australia. These probes are a brand new sensing technology still being evaluated. Preliminary results indicate ANFIS is able to estimate soil moisture with 90% accuracy.

Adaptive scheduling in deficit irrigation - a model-data fusion approach.

The technological demands required to successfully practice either targeted irrigation control and/or deficit irrigation strategies are currently reliant on numerical models which are often underutilised due to their complexity and low operational focus. A simple and practical real-time control system is proposed using a model-data fusion approach, which integrates information from soil water representation models and heterogeneous sensor data sources. The system uses real-time soil moisture measurements provided by an in situ sensor network to generate site-specific soil water retention curves. This information is then used to predict the rate of soil drying. The decision to irrigate is made when soil water content drops below a pre-defined threshold and when the probability of rainfall is low. A deficit strategy can be incorporated by lowering the irrigation “refill” point and setting the fill amount to a proportion of field capacity. Computer simulations show how significant water savings can be achieved through improved utilisation of rainfall water by plants, spatially targeted irrigation application, and precision timing through adaptive control

Tacit Knowledge Sharing in Open Innovation Projects

Tacit knowledge is considered critical to the success of open innovation projects, yet little is known about the factors that promote or impede tacit knowledge sharing in such projects. This article uses exponential random graph modeling to examine both tacit and explicit knowledge sharing in two early-stage open innovation projects. Results indicate autonomous motivation predicts tacit knowledge sharing, suggesting that managers need to promote a team culture that satisfies members’ needs for autonomy, competence, and relatedness. The modeling also suggests that brokerage is important in the early stage of a project to build the strong informal social structures needed to facilitate the exchange of tacit knowledge.

Provenance in streamflow forecasting

Within this extended abstract we describe a data set of provenance traces that we collected over the past two years for a continuous streamflow forecast in the South Esk river catchment in Tasmania, Australia.