About

Dr Eric (Gengkun) Wang received his B.Eng. degree in mechanical engineering and his M.Eng. degree in mechatronic engineering from the University of Science and Technology, Beijing, China, in 2006 and 2009, respectively. He completed his PhD in telecommunications engineering from the University of South Queensland, Australia. He has been working as a Post-Doctoral Research Fellow with the School of Electrical and Mechanical, USQ for two and a half years. He has authored more than 20 high quality papers, and participated in many national and international research projects. He was the runner-up in The Asia-Pacific Robot Contest (ABU Robocon) 2006, and received the best paper award from the IEEE Wireless Communications and Networking Conference, Cancun, Mexico, in 2011.

Eric Wang has over 16 years of invaluable experience in IoT (Internet of Things) technology, specializing in irrigation decision support tools. His expertise lies in developing innovative solutions to address challenges in agricultural sustainability and resource management. With a focus on leveraging technology for enhancing irrigation efficiency and environmental sustainability, Eric has secured substantial grant funding totalling $3.9 million, including a significant contribution of $1.6M  for the Climate Smart Sugarcane Irrigation Practicies Project (CSSIP) which was funded through the Depart of Agriculture and Water Resources – National Landcare Program: Smart Farming Partnerships Opticane.

Eric was also the IoT lead on the NESP funded project which developed proof of concept of the CIPIT (Cybernetic IoT Precision Irrigation Technology) that saves farmers time, money, water, and energy by connecting infield automation with a scientifically validated irrigation decision support system NESP 3.1.2.

Eric is leading the IoT component of the Burdekin Irrigation Project which is a $6M project funded by the Reef Trust Partnership due to finish this June. This project has operated as a consortium led by Sugar Research Australia with consortium members: JCU, AgriTech Solutions, Burdekin Productivity Services, and others. 

Teaching
  • CC2511: Embedded Systems Design (Level 2; CNS & TSV)
  • CC4950: Design Project (Level 4; CNS)
  • EE3600: Automatic Control 1 (Level 3; CNS)
  • EE4600: Automatic Control 2 (Level 4; CNS)
Experience
  • 2014 to 2016 - Postdoc Research Fellow, University of Southern Queensland (Toowoomba, Australia)
  • 2009 to 2010 - Software Engineer, ZBL Tech. (Beijing, China)
Research Disciplines
Socio-Economic Objectives
Honours
Awards
  • 2011 - Best Paper Award at IEEE Wireless Communications and Networking Conference, Cancun, Mexico, in 2011
  • 2006 - Runner-up in The Asia-Pacific Robot Contest (ABU Robocon) 2006
Publications

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

Journal Articles
Conference Papers
More

ResearchOnline@JCU stores 29+ research outputs authored by Dr Eric Wang from 2010 onwards.

Current Funding

Current and recent Research Funding to JCU is shown by funding source and project.

Horticulture Innovation Australia - Hort Innovation International Markets Frontiers Fund

Fresh and Secure Trade Alliance (FASTA)

Indicative Funding
$1,158,498 over 7 years
Summary
FASTA is a national research initiative designed to protect and grow Australia's horticultural exports. Within the overall consortium, JCU is developing smart sensor technologies for a variety of pre- and post-harvest applications. We will develop smart traps for real-time monitoring of insect pests in the field and near-infrared spectroscopy methods to identify the species of insects to contribute to management efforts. We will also develop optical scanning methods to remove infested produce from supply chains. Overall, this work contributes towards a large-scale national effort to improve market access, biosecurity and pest management for Australian horticulture.
Investigators
Bronson Philippa and Eric Wang (College of Science & Engineering)
Keywords
Near infrared spectroscopy; Sensor technology; Fruit flies; Machine Learning

Skyrail Rainforest Foundation - Research Funding

Near-infrared spectroscopy to improve detection of the invasive yellow crazy ant, Anoplolepis gracilipes.

Indicative Funding
$4,524 over 1 year
Summary
This project aims to develop a proof-of-concept device capable of distinguishing the invasive yellow crazy ant from native ants in the Wet Tropics World Heritage Area using a miniaturised near infrared (NIR) spectroscopic sensor. I will conduct an initial study into the variability of the yellow crazy ant NIR spectra then build a classification model to distinguish yellow crazy ants from common native ants using NIR spectra. These studies will be used to inform the prototype development. Prototype development will consist of the design and manufacture of a printed circuit board containing the NIR sensor and its support electronics. This prototype will be then validated using the same classification experiment of YCA against native ants.
Investigators
Russell Withers, Bronson Philippa, Lori Lach and Eric Wang (College of Science & Engineering)
Keywords
Yellow crazy ant; Anoplolepis gracilipes; Formicidae; Near-infrared spectroscopy; Passive identification

Great Barrier Reef Foundation - Reef Trust Partnership

GBRF Burdekin Irrigation Project.

Indicative Funding
$990,919 over 4 years
Summary
Increasing Industry Productivity and Profitability Through Transformational, Whole of Systems Sugarcane Approaches that Deliver Water Quality Benefits.
Investigators
Yvette Everingham, Eric Wang and Bronson Philippa (College of Science & Engineering)
Keywords
Water; Agriculture; Precision; Irrigation

QLD Department of Agriculture and Fisheries - Grant

Sex determination of fruit fly pupa using Near Infrared Spectroscopy.

Indicative Funding
$119,894 over 2 years
Summary
Implementation of an effective sterile insect program for fruit fly species Bactrocera tryoni requires that only sterile male insects be released. Therefore at some stage of the fly production process the females need to be removed. Hand sexing is very labour intensive and current automated systems based on colour and size are not effective in differentiating between male and female pupae for B. tryoni. Hence, a non-destructive, rapid method of sex separation is required that does not impact on the viability of the pupae and which can be incorporated into a mass rearing system. Preliminary research has demonstrated that Near Infrared Spectroscopy (NIRS) has great potential as an objective non-invasive method to sex B. tryoni pupae. The aim of this project is to develop and semi-automate a non-invasive rapid assessment technique to rapidly and consistently sort male and female B. tryoni pupae based on NIRS technology.
Investigators
Bronson Philippa and Eric Wang (College of Science & Engineering)
Keywords
sterile insect program; near infrared spectroscopy; bactrocera tryoni; Automation

Australian Government Department of Agriculture, Water and the Environment - National Landcare Program: Smart Farming Partnerships Grant

Climate Smart Sugarcane Irrigation Partnerships (CSSIP)

Indicative Funding
$1,586,884 over 5 years
Summary
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. Firstly, they do not integrate short to medium term weather forecasts (e.g. weekly to multi-weekly forecasts). Secondly, IDSTs do not operate at a spatial scale relevant to farmers. CSSIP will incorporate the Bureau or Meteorology?s new high-resolution climate model into the Irrigation Decision Support Tool. Thirdly, IDSTs require substantial time in manual data entry, which can be alleviated 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.
Investigators
Bronson Philippa, Yvette Everingham, Eric Wang, Stephen Attard and Wei Xiang in collaboration with Geoff Inman-Bamber, Marian Davis, Andrew Schepen, Brock Dembowski, Peter Larsen and Andres Jaramillo (College of Science & Engineering, AgriTech Solutions, Burdekin Productivity Services, Commonwealth Scientific & Industrial Research Organisation, DAF, Wilmar Sugar Australia and Sugar Research Australia)
Keywords
Sugarcane; Internet of Things; Irrigation

Department of Industry - Innovations Connections

Vacuum solution with an IoT-integrated management system.

Indicative Funding
$49,918 over 1 year, in partnership with LG Utilities Pty Ltd ($49,918)
Summary
Vacuum cleaners used in commercial cleaning services have been primarily dominated by corded and cumbersome vacuum cleaners. Although cordless and lightweight vacuum cleaners have been around for domestic users for some time, there ar eno cordless vacuum cleaners designed for commercial cleaning services in the market. In view of this fact, this project aims to develop a new cordless vacuum cleaner for commercial vacuum cleaning services with an IoT-integrated management system.
Investigators
Eric Wang and Jeremy Gordon (College of Science & Engineering)
Keywords
Automation; Internet Of Things; Structure design
Supervision

Advisory Accreditation: I can be on your Advisory Panel as a Primary or Secondary Advisor.

These Higher Degree Research projects are either current or by students who have completed their studies within the past 5 years at JCU. Linked titles show theses available within ResearchOnline@JCU.

Current
  • Near-infrared spectroscopy as a tool for the study and management of the invasive yellow crazy ant, Anoplolepis gracilipes. (PhD , Secondary Advisor)
  • Explainable Deep Learning for Image Understanding (PhD , Secondary Advisor)
  • Development of a Smart irrigation system using IOT and Remote Sensing integrated with Machine Learning Algorithms to improve water efficiency in Agriculture (PhD , Primary Advisor/AM/Adv)
Completed
Collaboration

The map shows research collaborations by institution from the past 7 years.
Note: Map points are indicative of the countries or states that institutions are associated with.

  • 5+ collaborations
  • 4 collaborations
  • 3 collaborations
  • 2 collaborations
  • 1 collaboration
  • Indicates the Tropics (Torrid Zone)

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Email
Phone
Location
  • D4.209, JCU Ideas Lab (Cairns campus)
Advisory Accreditation
Primary Advisor
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