Never one to shy away from a challenge to innovate, Ausvet joined in on the three-day AgHack event (27 to 29
The Ministry of Data (MoD) hosts hackathons to solve the most curious technological problems faced by Government agencies. Open to developers, start-ups, entrepreneurs, data aficionados, business analytics, and innovators for a better world, a MoD Hackathon is an opportunity to solve a real validated problem and deliver a product that has great market product fit.
AgHack powered by Ministry of Data was aimed to solve pre-validated agricultural challenges presented by the WA Department of Primary Industries and Regional Development (DPIRD). Eight pre-endorsed challenges were available for teams to choose from. Ausvet broke up into two teams in order to participate in 2 separate challenges:
Challenge 3 – Monitoring rangelands How might we better monitor the condition, health and usage of WA’s remote rangelands?? [Anne Meyer, Rohan Sadler, Mehdi Ravanbakash (from Mapizy)]
Challenge 2 – Where did my lamb chop come from? How might we better track and provide feedback to lamb producers and processors to maximise price and customer satisfaction of meat? [Ben Madin, Ben Fitzhardinge, Deepti Shukla]
Participating in this year’s AgHack was part of our strong commitment to ongoing professional development for staff. The event gave us an opportunity to showcase our enthusiasm for innovation, introduce the talent we have available in the organisation and to network with like-minded professionals from other companies. In addition, participants are given access and exposure to WA Government datasets (https://catalogue.data.wa.gov.au/group/aghack).
Challenge 3 – Monitoring the rangelands – MyLandHealth
Having identified a mission to locate where land is sustainably managed or not, our team downloaded a time series of 130 scenes from the European Space Agency’s Sentinel 2 satellite imagery for a remote rangeland site near Tom Price (10 m resolution, 13 bands). The team then developed a workflow to produce both time series of NDVI, and a hotspot map of where in the landscape the observed NDVI was significantly lower than that predicted by a weather and fire-driven model. The team almost achieved this goal within the two days of the weekend (with some bugs in the processing chain).
Proof of concept and Workflow
Proof of concept
Challenge 2 – Where did my lamb chop come from? –
With the aim of Linking Consumers, Producers and Processors to improve eating quality, the team designed an individual animal linked to
Scan the QR Code for a demonstration:
Below are figures showing how Animals are linked with Product (Fig1), The
User interface proof of concept