Dr. Bronson Philippa is an electronics and software engineer with highly interdisciplinary research interests. His main focus is software and modelling. Developing scientific software has allowed Bronson to contribute to materials science, image processing, and smart agriculture. In materials science, his primary interests are organic solar cells, photodetectors, and light emitting diodes. In agriculture, Bronson's interests are smart irrigation, crop modelling, and quality assessment by near infrared spectroscopy. He has raised over $4M in grant funding.

Bronson has a Bachelor of Engineering (Electrical and Electronic) / Bachelor of Science (Mathematics) and a PhD in Physics. He has lectured at James Cook University since 2011, and has been based in Cairns since 2016. He is a Senior Member of the IEEE, a Chartered Professional Engineer (CPEng) with Engineers Australia, and a Registered Professional Engineer of Queensland (RPEQ).

Organic semiconductors research

Pictured above: an organic solar cell printed onto a flexible plastic substrate.

Organic semiconductors use carbon-based materials, instead of traditional inorganic semiconductor materials like silicon. They offer low-cost, lightweight, flexible and transparent electronic devices. There is a vast space of possible molecular structures, allowing for tunability of device properties. Organic semiconductors are best known for their commercial success in OLED screens, but the technology is also applicable to solar cells, photodetectors, sensors, and more.

Bronson studies organic semiconductors using computational methods. He develops mathematical models and computer software to offer insight into how to improve device performance and more accurately analyse their behaviour. He is a member of the organising committee of the Australasian Community for Advanced Organic Semiconductors.

PhD projects are available in the theory and computational modelling of organic electronics, especially aspects related to charge transport, device performance, exciton behaviour, and morphology.

Agricultural technology research

Agriculture is a critical industry for Northern Australia. Bronson is developing agricultural technology for the sugarcane industry, providing improved automation and decision support tools. His primary focus is irrigation, and using technology to improve irrigation decision making. Bronson also works on image processing to detect invasive weed species. Another area of interest is the application of near infrared spectroscopy and other methods to non-destructively test food products as part of the JCU Rapid Assessment Unit.


Bronson has taught various engineering subjects including EG1002 Computing and Sensors, EG1012 Electric Circuits, EE2201 Circuit Theory, CC2511 Embedded Systems Design, and CC3501 Computer Interfacing and Control. His priority in teaching is to deliver active and authentic learning to ensure that students develop practical skills. He has been recognised with several teaching awards including a 2016 JCU citation for outstanding contributions to student learning and a 2018 citation from the Australasian Association for Engineering Education.

PhD and Masters by research projects

I am available to supervise PhD and Masters projects in the topics below. If you are interested, please reach out by email to bronson.philippa@jcu.edu.au and include a brief CV, a summary of your previous research experience, and a description of the type of research project that you wish to undertake. Scholarships are available for both domestic and international students, but please note that the scholarships are highly competitive.

PhD or Masters project: organic electronics charge transport modelling

Multiple projects are available in the space of organic electronics (e.g. organic light emitting diodes, solar cells, photodetectors, and other types of devices). These projects suit a student with an interest in computational physics or chemistry. We develop software and theory to understand how these devices work on a fundamental level and how to improve them. These are highly collaborative projects and would call for students to work with chemists, physicists and engineers on development of next-generation electronics technology.

PhD or Masters project: machine learning

Machine learning is a vast space and these projects can be adapted to suit your interests. I am particularly seeking students with an interest in developing computer vision for marine applications (such as reef surveying and habitat monitoring). These projects will be part of the M-DataTech hub. I am also seeking students interested in more fundamental questions in machine learning such as how to improve robustness, how to improve data efficiency to train effective models with less data, and how to deploy models to edge computing devices. These projects can be customised to suit your individual interests.

  • CC3501: Computer Interfacing and Control (Level 3; CNS & TSV)
  • EE3300: Electronics 2 (Level 3; CNS & TSV)
  • EE3901: Sensor Technologies (Level 3; CNS)
  • EE5901: Advanced Sensor Technologies (Level 5; CNS)
  • Organic semiconductors
  • Charge transport
  • Image processing
  • Smart agriculture
  • Internet of Things
  • Embedded systems
  • 2019 to present - Senior Lecturer, James Cook University (Cairns)
  • 2016 to 2018 - Lecturer, James Cook University (Cairns)
  • 2014 to 2015 - Lecturer, James Cook University (Townsville)
Socio-Economic Objectives
  • 2022 - JCU Citation for Outstanding Contributions to Student Learning
  • 2019 - JCU Award for Excellence in Graduate Research Leadership
  • 2018 - Australasian Association for Engineering Education Citation for Outstanding Early Career Contribution to Engineering Education
  • 2016 - JCU Citation for Outstanding Contributions to Student Learning, for enthusing students in electronics engineering with active and authentic learning
  • 2015 - JCU Sessional Staff Teaching Award for outstanding contributions to student learning
  • 2015 - Bev Frangos Graduate Instructor Prize for an outstanding contribution to teaching
  • 2015 - Selected to attend the Lindau Meeting of Nobel Laureates as an Australian representative
  • Senior Member of the IEEE (Institute of Electrical and Electronics Engineers)
  • 2022 - Registered Professional Engineer of Queensland (RPEQ)
  • 2022 - Chartered Professional Engineer (CPEng) of Engineers Australia
  • 2021 - Vice Chair of IEEE Northern Australia Section

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

ResearchOnline@JCU stores 42+ research outputs authored by Dr Bronson Philippa from 2011 onwards.

Current Funding

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

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
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.
Russell Withers, Bronson Philippa, Lori Lach and Eric Wang (College of Science & Engineering)
Yellow crazy ant; Anoplolepis gracilipes; Formicidae; Near-infrared spectroscopy; Passive identification

Australian Research Council - Discovery - Projects

Pathways for performance improvements of organic light emitting diodes

Indicative Funding
$94,000 over 4 years (administered by University of Queensland)
Organic light-emitting diodes (OLEDs) represent the next generation technology for displays and lighting. Despite their rapid uptake, one of the factors limiting their application in lighting is the efficiency roll-off at high brightness. This project aims to work towards solutions for this problem using an innovative combination of simulation studies and experimental work. Expected outcomes include improved theoretical and experimental approaches leading to new design rules for OLEDs. This should provide significant benefits such as a pathway for development of improved efficient, high brightness OLEDs for applications in low energy consumption lighting and long-lasting, bright displays.
Ian Gentle, Bronson Philippa and Almantas Pivrikas (The University of Queensland, College of Science & Engineering and Murdoch University)

Great Barrier Reef Foundation - Reef Trust Partnership

Reducing herbicide usage in the Burdekin and Proserpine reef catchment areas with precise robotic weed control in sugarcane

Indicative Funding
$900,000 over 4 years, in partnership with Autoweed Pty Ltd ($10,000)
The main objective of this project is to develop and build the world?s first robotic platform for selective weed control in sugarcane. Specifically, we aim to retrofit two 12-metre sugarcane booms for farmers in the Burdekin and Proserpine GBR catchment areas with state-of-the-art deep learning detection and spraying technology. The fundamental aim of this project is to significantly minimise the herbicide usage by selective spot spraying. We have set a target to reduce the knockdown herbicide usage on the chosen farms by 80% compared to the traditional blanket spraying that is performed during various stages of the crop cycle.
Mostafa Rahimi Azghadi, Ron White, Bronson Philippa, Brendan Calvert, Alex Olsen, Emilie Fillols, Molly O'Dea, Ross Marchant and jake Woods (College of Science & Engineering, Autoweed Pty Ltd and Sugar Research Australia)
precision agriculture; Deep Learning; automatic weed control; Robotic; weed management

QLD Department of Agriculture and Fisheries - Grant

Sex determination of fruit fly pupa using Near Infrared Spectroscopy.

Indicative Funding
$119,894 over 2 years
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.
Bronson Philippa and Eric Wang (College of Science & Engineering)
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
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.
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, Department of Agriculture and Fisheries, Wilmar Sugar Australia and Sugar Research Australia)
Sugarcane; Internet of Things; Irrigation

QLD Department of Environment and Science - Advance Queensland Innovation Partnerships

Smart Ear Tag for Livestock

Indicative Funding
$1,500,000 over 4 years
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.
Ian Atkinson, Wei Xiang, Bronson Philippa, Ed Charmley, Greg Bishop-Hurley, Nigel Bajema, Scott Mills, Gordon Foyster and Richard Keaney (Research Infrastructure, College of Science & Engineering, Commonwealth Scientific & Industrial Research Organisation and Taggle Systems)
Animal tracking; Internet Of Things; Microelectronics; Geo-location; Precision Agriculture; Beef Cattle

WA Department of Primary Industries and Regional Development - Contract Research

Identification of benthic structure using machine learning

Indicative Funding
$36,200 (administered by Wa Department of Primary Industries and Regional Development)
The Project aims at automating the classification of benthic structure and biota from images using machine learning
Marcus Sheaves, Bronson Philippa, Carlo Mattone and Michael Bradley (College of Science & Engineering)
Benthic Assessment; Machine Learning

Department of Industry - Innovations Connections

Vibration analysis of mining industry conveyor belt systems: validation of method and pathways to improvement

Indicative Funding
$48,811 over 1 year, in partnership with Rolco Pty Ltd ($48,811)
Equipment failures in the mining industry can cause serious safety hazards and substantial financial losses. An automated, cost-effective monitoring system that could be retrofitted to existing equipment would provide advance warning to operators and reduce the likelihood of unscheduled outages. This project will test and validate a vibration-based monitoring system for conveyor belts and associated equipment. It will also identify improved methods to analyse the vibration data to increase the sensitivity and/or accuracy of the alerts that are generated.
Bronson Philippa, Bruce Belson, Lei Lei and Wei Xiang (College of Science & Engineering)
Vibration Analysis; Signal Processing; Mining

Department of the Environment and Energy - National Environmental Science Program (NESP) - Tropical Water Quality Hub (TWQ Hub)

Improving water quality for the Great Barrier Reef and wetlands by better managing irrigation in the sugarcane farming system

Indicative Funding
$458,103 over 3 years
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 abut 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.
Yvette Everingham, Wei Xiang and Bronson Philippa in collaboration with Stephen Attard (College of Science & Engineering and AgriTech Solutions)
Irrigation; Water Quality; Great Barrier Reef

Australian Government Department of Agriculture, Water and the Environment - Control Tools and Technologies for Established Pest Animals and Weeds Programme

Improving the accuracy of weed killing robots with new image processing algorithms and near infra-red spectroscopy techniques

Indicative Funding
$271,772 over 2 years
Automated weed species recognition remains a major obstacle to the development and industry acceptance of robotic weed control technology. Particular problems occur in rangeland applications, including high light variability and weed-camera distance variability, which cause camera dynamic range problems, image blurring, and occlusion by other plants. This project aims to develop robust image recognition systems combined with Near Infra-Red spectroscopic methods for these complex rangeland environments with special emphasis on the broad-acre grazing pastures in North Queensland. The developed imaging systems will be suitable for all weed killing applications with particular emphasis given to foliar spot-spraying and Herbicide Ballistic Technologies.
Ron White, Bronson Philippa, Alex Olsen and Owen Kenny in collaboration with Brett Wedding, Michael Graham, Carole Wright and Stephen Grauf (College of Science & Engineering, QLD Department of Agriculture and Fisheries and Forestry)
Weed; Near-Infra-Red; Robotics; Image Analysis

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.

  • Explainable AI for Medical Applications (PhD , Secondary Advisor/AM)
  • Monte Carlo simulation of highly non-equilibrium electron and ion transport in plasma medicine. (PhD , Secondary Advisor)
  • Near-infrared spectroscopy as a tool for the study and management of the invasive yellow crazy ant, Anoplolepis gracilipes. (PhD , Primary Advisor/AM/Adv)
  • Numerical simulations of charge transport in organic semiconductor materials (PhD , Primary Advisor/AM/Adv)
  • Development of high-level programming tools to improve the robustness of Internet of Things networks. (PhD , Primary Advisor)
  • Graphene based supercapacitors from renewable sources (PhD , Primary Advisor)
  • Smart Organic Solar Cell Windows using Photochromic Materials: Theory and Experiment (PhD , Primary Advisor)

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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