Non-Invasive Sensor Technology

A mango tree with ripening fruits on it.

The IFFS Non-Invasive Sensor Technologies group is focused on the development of new sensor hardware and applications of existing sensors to assess agricultural commodities and advance productivity without damaging the product. Their work has focussed to tree-fruit crops, and mango in particular, and has been recognised by several awards including the Australian Mangoes 2024 Industry Innovation Award. 

The team is known for pioneering work on the use of near-infrared spectroscopy for assessing horticultural produce, and on the implementation of machine-vision for in-orchard fruit load estimation. A current direction involves use of both technologies within a selective fruit harvesting solution to address labour shortages and occupational risks to workers.

A hallmark of the group has been partnership with international technology companies and the Australian horticultural supply chain, delivering research into practical outcomes.

The work of the group is multi-disciplinary in nature, and has involved work in:

  • Spectroscopy
  • Chemometrics
  • Instrumentation – electronics
  • Machine vision
  • Mechatronics
  • Geospatial analysis
  • Agronomy
  • Business (technology adoption)

The group has delivered outcomes in:

  • A handheld near-infrared spectroscopy (NIRS) measurement system to assess harvest maturity and future eating quality of mango (and other) fruit 
  • An on-packline NIRS measurement system to screen fruits for certain internal defects
  • Algorithm and temperature sensor hardware for forecast of the time fruit will be harvest mature
  • A web-application for display of data, enabling decisions on the order of harvest of orchard blocks
  • Automated technologies for fruit and flower counting automated technologies for fruit picking. Read about the world-first mango auto-harvester

Our main adoption partners in this journey have been MAF Oceania, Felix Instruments, SensorHost, Agricultural Robotics and Australian Mango Industry Association.

Research Projects

Our research is redefining how quality and productivity are measured in horticulture, without damaging the crop. Explore our projects developed in close collaboration with industry to deliver real-world impact.

Our non-invasive sensors team has led the world in adapting and implementing near-infrared spectroscopy (NIRS) measurement systems to assess the eating quality of mangos and predict the ideal harvest time.

This technology has been widely adopted within the mango industry, laying the foundation for a range of other research. For example, in-field machine vision systems have been developed for count of fruit and estimation of fruit size, allowing fruit load estimates before harvest. Coupled with an on-line app for display of data, these capabilities allow farmers to better plan their harvest (e.g. employing the right number of pickers at the right time).

Our researchers developed the world’s first mango auto-harvester, with its latest prototype displaying improved fruit handling in trials at Central Queensland orchards.

The auto-harvester has turned heads within the mango industry for some time, but recently underwent substantial refinement and is becoming a more viable for commercialisation.

Read about the world-first mango auto-harvester

CQUniversity's non-invasive sensor technology

Key Research Personnel

From electronics and mechatronics to agronomy and data science, our multidisciplinary team brings deep technical expertise to every project. Meet the innovators driving sensor-based solutions for the future of horticulture.

The theme of Kerry’s career has been the application of non-invasive instrumentation to issues related to plant performance, and in particular, in photosynthate transport - assimilate partitioning.

He has led multidisciplinary work resulting in the association of a phytoplasma with the papaya dieback disorder, and the use of near infra-spectroscopy (NIRS) for fruit quality assessment. Kerry has a practical, hands-on, capability, yet an academic perspective on life.

He strongly believes that his R&D effort should result in a gain to society and that he should provide practically relevant training to undergraduates and postgraduates.

Dr Zhenglin Wang is an active researcher in computer vision, machine learning, and precision agriculture, with over 10 years of industry experience as a software engineer.

He has played a key role in multiple agricultural innovation projects, including the development of the world’s first automated mango harvesters—a milestone in agri-tech.

Driven by a strong passion for applied research, Dr Wang focuses on delivering practical automation solutions that address real-world challenges in agriculture. 

Over 20 students have progressed through Masters and PhD programs under the groups umbrella. New students benefit from a rich array of existing industry contacts, prospects for industry-relevant projects and equipment and technique base.  Examples of three recent graduates follow:

Nicholas Anderson 

Nicholas began working on mango orchards while on a working holiday visa and was soon hooked on the fruit and lured into postgraduate work on crop forecasting at CQUniversity. His experience includes near-infrared spectroscopy in assessment of mango fruit maturity led him into a position using the technology in assessment of soil carbon. He is now a NIR spectroscopist (Measurement analysis group lead) with CarbonLink P/L.

Dr Anand Koirala

Anand delivered a PhD thesis through CQUniversity on the use of machine vision technologies for fruit quality and yield estimation. His activity included work on a pipeline for processing imagery collected for from cameras mounted to mobile platforms driven through mango orchards for count and map display of flowers and fruit. He has continued in a senior role at RetinaVisions P/L involving processing of images from cameras mounted to garbage trucks, mapping road defects.

Dr Hari Dhonju

Hari produced a thesis at CQUniversity on the design and development of an orchard harvest management information system, incorporating the outputs of the various sensors that the group has developed. Hari has continued into a position as Geospatial Automation Lead at Agronomeye P/L.