Portfolio 2: Agricultural and environmental technologies

Agricultural and environmental technologies are two application areas for the Challenge, given their importance to New Zealand’s economy. 

There is significant potential for economic growth, since there is already a thriving agritech manufacturing sector with useful market share in some key export markets. 

There are also local businesses of scale in agritech, with a deep knowledge of export customers’ requirements and market trends, to commercialise the technologies developed in the Challenge.  

 

Portfolio 2 Spearhead projects:

Inverting electromagnetics – a new way to measure groundwater flow

This project aims to solve the problem of determining the spatially-averaged velocity of shallow groundwater, which is a vector for contaminants. The research aims to culminate in a sensor system marketable to global regulators, farmers, and consultants.

The research focuses on developing and promoting New Zealand’s capacity to use physical and engineering resources to conduct research, generate export revenue via New Zealand’s high-tech manufacturing sector, and benefit New Zealand-based regulators and farmers by enabling informed decision-making, land management and environmental foot printing. 

Outcomes will particularly benefit Māori managing water quality and where mahinga kai is threatened by waterway contamination.

Sensing technologies developed in the SfTI Challenge will provide New Zealand with a step up in its ability to use electromagnetic sensing in harsh, inaccessible environments, and provide locally-based manufacturers with new high-tech export products. 

The research involves science questions at the forefront of several disciplines:

  • the sensing concept requires precise manipulation of large-scale magnetic fields. The research will explore methods to control magnetic flux density over a large spatial extent.
  • it will explore methodologies to detect the miniscule Faraday signals received at ground electrodes in the presence of substantial interfering signals.
  • due to non-uniform groundwater velocity sensitivity distribution, the magnetic flux will be characterised over 3-dimensional space; the research will formulate methods for profile sensing of potential difference and solving the poorly-posed inverse problem to extract groundwater velocity profiles.
  • supporting data and methods for visualisation will be explored to determine how best to convey the flow and inter-relationships with other data layers to water quality managers and other interest groups.

 

The team

  • Portfolio 2 leader – Professor Ian Woodhead
    • Lincoln University – Chief Scientist and Lincoln Agritech Technology Group Manager.
  • Professor Bob Buckley
    • Robinson Institute, Victoria University of Wellington - magnetics.
  • Dr John Kennedy
    • GNS - nano sensors, sensing magnetic field strength.
  • Dr Ian Platt
    • Lincoln Agritech - modelling, signal processing/ noise reduction.
  • Dr Mike Hayes
    • University of Canterbury - measurement, signal processing
  • Professor Colin Fox
    • University of Otago - modelling, inverse problems.
  • Mr Maui Hudson
    • University of Waikato - data-to-information, incorporating/delivering to Māori.

 

Precision farming technologies for aquaculture

This project aims to develop innovative technologies essential to maintain and enhance New Zealand’s position as a global leader in the production of sustainable aquaculture products.

Precision farming technologies provide the greatest opportunity to unlock aquaculture’s potential by minimising the frequency farmers need to physically access farms and manually sample stock health and condition.  The project seeks to pave the way for marine farms to move further offshore by arming farmers with precision automated technologies and remote monitoring systems.  This will enable farmers to ‘see’ the status of their farms and stock from their computers and mobile devices, allowing them to more efficiently resource their operations.

The project aims to produce technology able to increase aquaculture production and value in New Zealand, and be exported.  We will do this by:

  • developing underwater imaging sensors and sea-to-land communications technologies that can remotely measure parameters useful to farm managers,
  • enabling future automation in aquaculture farming locally and internationally, and
  • enabling New Zealand to become a leading exporter of advanced sensors, and associated data processing, communication and visualisation technologies that will transform ocean farming worldwide

Project innovations providing remote intelligence on the status of farms and stock and growing waters condition will reduce uncertainty, enabling farmers to make informed decisions about offshore farms from their ‘desk’.

Research outcomes will be transferable to applications beyond aquaculture, like real-time surveillance of dredging activities, shipping and biosecurity threats in ports and harbours and collection of data useful in tracking the state of NZ’s coastal waters.

 

The team

  • Project leader – Dr Chris Cornelisen
    • Cawthron Institute – project implementation, cross-project collaboration.
  • Dr Ross Vennell
    • Cawthron Institute – above surface communications, sensor integration, image processing.
  • Mr Paul Barter
    • Cawthron Institute – development and integration of on-form platform, power communications, sensors.
  • Dr Shaun Ogilvie
    • Cawthron Institute – Vision mātauranga.
  • Prof. David Williams
    • University of Auckland – connection of spectroscopic data to mussel condition.
  • Prof. Cather Simpson
    • University of Auckland – laser spectroscopic methods development.
  • Assoc. Prof. Neil Broderick
    • University of Auckland – fibre laser development.
  • Assoc. Prof Richard Green
    • University of Canterbury – imaging and underwater communications.
  • Assoc. Prof. Andreas Willig
    • University of Canterbury – underwater communications.
  • Prof Mengjie Zhang
    • Victoria University of Wellington – machine learning.
  • Brian McMath
    • NZ Product Accelerator – industry connections, capability mapping.

 

Adaptive learning robots to complement the human workforce

This project aims to develop adaptable, cheaply reconfigured, rapidly deployed ‘workforce’ robots able to learn from their environments.

Underpinning future small-scale production of tailored, high value, robots with wide application and an eye on export is a particular focus of the research.

 

The team

Principal Investigators:

  • Dr Armin Werner
    • Lincoln Agritech
  • Associate Professor Will Browne
    • Victoria University
  • Associate Professor Johan Potgieter
    • Massey University.

The project involves researchers from Lincoln Agritech and SCION, as well as Auckland, Victoria, Massey, Canterbury and Otago Universities.