CSSIP will minimise nutrient runoff, improve soil health and increase wetlands water quality by facilitating the adoption of world-class irrigation practices in sugarcane farming systems.
Currently, best practice irrigation is assisted by an Irrigation Decision Support Tool (IDST) that provides evidence-based advice. However, IDSTs have not reached their full potential and have several drawbacks, such as requiring substantial manual data entry, lack of weather data integration and spatial scale.
CSSIP will address these drawbacks by:
incorporating the Bureau or Meteorology's new high-resolution climate model into the Irrigation Decision Support Tool
reduce the need for manual data entry by using real-time monitoring via Internet of Things technologies.
This will increase irrigation efficiency, reducing excessive runoff into river systems and onto the Reef, and, will help farmers save water and energy costs.
Funded by the Department of Agriculture and Water Resources - National Landcare Program: Smart Farming Partnerships Grant.
To develop Narrowband Internet-of-Things (NB-IoT) testing methods and procedures for NB-IoT end-user terminals (devices) based on NB-IoT Release 13.
This will involve detailed research, evaluation and development of processes and procedures for carrying out testing against the 3GPP standard for NB-IoT devices within a framework that enables formal certification under NATA rules; working with external partners towards carrying out an initial NB-IoT device 3GPP certification; and developing a business model and recommendations for the establishment of an NB-IoT Certification Centre.
Funded by Huawei Technologies (Australia) Pty Ltd - JCU-Enex NB-IoT Testing and Certification Centre
The vast natural environment of Northern Australia feeds the cattle industry; however, biosecurity threats have negatively impacted this.
Conventional management of such threats such as weeds are not suited to such broad, harsh landscapes. The project will use an Internet of Things network with low-cost environmental sensors, drone mapping and big data analytics to develop and test data-driven, strategic pest management programs - ultimately improving both cattle industry and natural assets.
Funded under the CRC for Developing Northern Australia Scheme - Projects
To enable the collaboration on opportunities that include:
establishing a joint Huawei-JCU Internet of Things (loT) Laboratory
use the joint loT Lab to promote strong academic-industry collaboration; leveraging on Australia's first loT Engineering degree program run by JCU.
Funded through a grant from Huawei Technologies (Australia) Pty Ltd
This project will assist to analyse the large repository of data held by Cairns marine in relation to its full operations, i.e., from harvest to husbandry, inventory tracking and sales fulfilment. The physical systems currently in use at Cairns Marine are extensive and complex, involving a range of activities in Northern Australia, all activities on site (including R&D) in Cairns and full product (marine animals) stewardship to destination. A deep and informed understanding of data is required by the business to meet its growing global product commitments.
Funded by Department of Industry, Innovation and Science - Innovations Connections
To design and validate traps that are low cost and sensitive enough for Aedes aegypti and Aedes albopictus mosquitoes that they can be deployed for both SIT release surveillance during suppression and elimination operations, and also for sentinel surveillance after elimination.
Funded by Verily Life Sciences - Contract Research
CeresTag is investing in the development of a smart ear tag for livestock to enable near real-time geo-location and health monitoring.
The developed ear tag will be compliant with the current NLIS identification system and cost only marginally more than existing tags. This technology will revolutionise the industry through enhanced animal welfare, improved land management practices and increased profitability. It will form the starting point of block chain traceability that will underpin the continued success of this important component of the Australian economy and help maintain the premium status of Australian livestock products.
Funded by QLD Department of Science, Information, Technology and Innovation - Advance Queensland Innovation Partnerships
Investigate a machine learning sensor network to maintain optimum conditions and collect data for Molten Oxygen Electrolysis (MOE) reactions. The project has implications for establishing industry standards for data collection and fail-safe implementation, and improvements to hardware components and configuration.
For this project, JCU's Internet of Things researchers will examine:
sensor quality before purchase and calibration logic once installed into PCB
any errors in the output value and possible causes
provided network structures for reliably and continuously recording data from multiple sensors to multiple SBC's to an academic standard
methods for machine learning collection of data
methods provided for protecting sensor hardware in extreme conditions
failsafe methods and data collection failsafes for industry standards requirements .
At the conclusion of the research study, JCU will consult on final hardware design for industrial conditional requirements, based on industrial standards reports gathered prior to consultation.
Funded by Janco Enterprises Pty Ltd
The primary aim of this grant application is to bring smart city technology into urban water management, to improve urban water quality discharging to the Great Barrier Reef.
Develop IOT technology to manage large data sets obtained from existing smart meters and water quality monitoring probes to make effective management decisions;
Support the development of new cost effective, real time water quality monitoring technology.
Advise the purchase of commercially available water quality monitoring probes suitable for a tropical urban stormwater environment;
Support the development of new real time monitoring technology for nutrients
Develop data analysis tools using IOT technology for both smart meter water consumption data, sewer pump station overflow data and stormwater water quality data so that the data is available in real time and can be used for effective decision making.
Funded through the Department of Industry, Innovation and Science - Smart Cities and Suburbs Program
Recent advances in light field (LF) photography open a new horizon for digital video streaming, where the single-shot LF capture and glasses-free 3D display capabilities are perfect for a wide range of 3D interactive applications.
This proposal aims to propose an interactive 3D VR streaming system based upon the state-of-the-art LF video compression and transmission technology. The proposed new framework and novel algorithms will be able to efficiently compress the enormous amounts of LF-based VR video data, ensure the quality of the views of interest, and achieve low-latency and error-resilient 3D video transmission.
Funded by Huawei Technologies Co. Ltd - Huawei Innovation Research Program
The aim of this project is to improve water quality for the Great Barrier Reef and wetlands impacted by sugarcane farming. This project will work in partnership with industry, extension, NRM, research and government organisations to develop and deploy an irrigation system that is automatically controlled by remotely accessing feedback from the IrrigWeb decision support tool.
Irrigweb provides optimal irrigation schedules on a paddock-by-paddock basis by linking information about climate, soils and management regimes. If new water quality targets as specified in the revised Burdekin Water Quality Improvement Plan are to by met by 2025, it will be critical to establish pathways that enable industry partners to capitalise on new technologies.
Funded by Department of the Environment and Energy - National Environmental Science Program (NESP) - Tropical Water Quality Hub (TWQ Hub)
This project involves conducting field testing of a LoRaWAN rain gauge prototype that was developed collaboratively between JCU and CSIRO. The prototype system uses cutting-edge low-power long-range IoT communications technology to transmit rainfall data from rain gauges in to a cloud system designed by CSIRO. The objective of the field testing is to determine whether the developed prototype is suitable for a deployed system in the Wet Tropics.