Coordinate the work.
Supported the RMIT work-integrated-learning coordination and agreement process, alongside project discussions.
Project note / 2025 · RMIT work-integrated learning
Agritech starts
with ground truth.
Supported 2025 RMIT work-integrated-learning coordination, technical scope and documentation for agritech data-collection and smart-irrigation concepts.
FROM OBSERVATION TO DECISIONCONCEPT
- Context
- RMIT work-integrated learning
- Contribution
- Coordination · Scope · Documentation
- Focus
- Ground-truth data & irrigation concepts
01 / The contribution
Give a technical idea
a workable scope.
The 2025 work brought industry–university coordination together with early technical framing. The contribution centred on helping define and document an agritech project, with data quality as a starting point.
Frame the scope.
Contributed technical scope around ground-truth data collection, low-cost sensing and potential smart-irrigation applications.
Document the concept.
Supported project documentation so that the proposed technical direction could be discussed and developed further.
02 / Proposed technical direction
From field signals
to useful questions.
The proposed scope explored low-cost field-sensor calibration and validation against satellite data, with Open Data Cube as a candidate framework. Smart irrigation was an application concept.
- 01 / OBSERVE
Collect field data
Consider local measurements and the context needed to interpret them.
- 02 / COMPARE
Check the signal
Explore sensor calibration and comparison with satellite observations.
- 03 / INTERPRET
Inform irrigation
Consider how trustworthy data could support a future irrigation decision.
Illustrative concept pathway. These steps describe the proposed direction, not a record of completed deployment.
03 / Questions the concept raises
What would make
the data useful?
A sensor reading becomes more useful when its meaning, reliability and connection to a decision are explicit. These are design questions for further development.
Explore Open Data Cube ↗- Measurement
- Which quantities need to be measured, and what calibration would make them comparable?
- Context
- How would timing, location and local conditions be recorded alongside each observation?
- Comparison
- How would field measurements and satellite observations be aligned at suitable spatial and temporal scales?
- Decision
- What evidence would be needed before a system could support a real irrigation action?
Evidence & scope
A contribution record.
This page describes coordination, technical scoping and documentation support. It does not report a deployed irrigation controller, completed field trials, validated model accuracy or measured water savings.
The 2025 work is presented separately from MAI Physics’s later public software demonstrations. RMIT is named to describe the work-integrated-learning context; no institutional endorsement is implied.
04 / Cloud & startup resources
Explore the ecosystem.
Public programme information for future exploration. These links do not indicate programme membership, credits, funding, partnership or endorsement of this work.
CLOUD & BUILDINGAWS for StartupsExplore AWS startup resources ↗DEVELOPMENT & SUPPORTMicrosoft for StartupsExplore the Microsoft programme ↗STARTUP RESOURCESGoogle for StartupsExplore the Google programme ↗
Programme names are used descriptively. No programme badges or partner marks are displayed.
Continue the inquiry
Start with a question
worth measuring.
Discuss the project scope, the contribution or a possible next research question.