eNLB - "The Future of AG"

Project Info

AgTech 2050 thumbnail

Team Members


James K

Project Description


eNLB –
“The Future of AG” Brings together interactive technology with the traditional livestock branding process in line with other national livestock tracking and identification systems.

Our easy to use out of the box software for farmers & other enthusiasts bridges the gap with current extensive wait periods for a successful brand registry and Identity, saving you time and money.

Technology Implemented:

Machine Based Learning Algorithms
API Implementation within the Horse and Cattle Brands in Queensland & State Wide
Livestock Brand Availability Search
Ag Business IP Portal
IOT Sensor’s – Weather Alerts & Updates

Electronic National Livestock Brands Portal powered by Advanced Data Matching of TM-LINK


Data Story


Our interactive software has the ability in utilizing 5G Technology and the way IOT Devices will be interacting within our daily lives. By 2050 The future of Agriculture will be so advanced in Technology that the productivity of the average farmer will see them enjoying more time with family and less time doing things the hard way.

As previous generations have never had this technology before we bring to GOVHACK 2019 The first of its kind with plugin capabilities of endless idea’s integrating with the current framework of existing systems and modules

We have implemented Machine learning with IOT Devices & 5G Technology for faster communication guaranteed by 2050 All Black spots in Australia will be operating with a minimal of 4GX leaving those in remote area’s with ultra-fast internet speed’s.

Our Prototype is underway but our Presentation and a video paints a picture from how advanced we have come from previous generations and moving the Cattle & Agriculture Industry forward.

This technology is here and we are now ready to revolutionize and suspect our plugins will integrate and make space for other’s on open source in coming together and working towards a better future for all Australians
Our Data sets are used in a unique way’s as we gather and utilize DATA Scraping and integrate with machine learning and matching branding techniques to prevent the copyright

Our Main Data was focused on

https://www.data.qld.gov.au/dataset/horse-and-cattle-brands-in-queensland


Evidence of Work

Video

Homepage

Team DataSets

TM-Link

Description of Use we propose its use for its existing capabilities for its recognition with international trademarks and algorithms for how they could be utilized to match livestock brands in registration with those being registered.

Data Set

Landmark areas - Queensland

Description of Use Abattoirs - QLD Beef Industry - The Idea- Landmarking and identifcation from Paddock to plate of certain livestock owner's producing quality beef

Data Set

CSIRO DATA

Description of Use Data scraping with integration into IOT devices a of advanced weather modification

Data Set

Soils of Queensland agricultural research stations series

Description of Use Ag-Tech

Data Set

Horse and Cattle Brands in Queensland

Description of Use Our main data set we used is branding with utilizing the current out dated system and looking at ways we can implement machine learning algorithms into a system that speeds up the 50 day registration for a cattle brand

Data Set

Challenges

Thrive or survive: how can we adapt for the future?

What will Australia in 2050 look like?

Go to Challenge | 38 teams have entered this challenge.

Queensland OpenAPI

Create a project using one or more of Queensland's Open-API’s

Go to Challenge | 39 teams have entered this challenge.

Environment and Science Data

How might we use environment and science data to better engage with the community?

Go to Challenge | 19 teams have entered this challenge.

Spatial Information

How might Queenslanders find out more about where they live?

Go to Challenge | 19 teams have entered this challenge.

TM-Link Data Discovery

TM-Link is a newly available trade mark database developed in collaboration between IP Australia, Swinburne University and Melbourne University. TM-Link includes administrative data from jurisdictions across the world, linked at the application level by advanced neural network algorithms. We are encouraging hackers to explore this new data set and consider what creative visualisations, innovative insights and/or opportunities to further enrich the data they might imagine.

Go to Challenge | 8 teams have entered this challenge.

🌟 Improving the customer experience of government services

How can government data be used to improve the experience of citizens interacting with government?

Go to Challenge | 24 teams have entered this challenge.

🌟 Community open data weather monitoring and alerts system

Develop a digital solution, underpinned by IoT (Internet of Things) data, which is capable of early detection and alerting for weather and climate related risks to the community.

Go to Challenge | 10 teams have entered this challenge.

🌟 Telling Stories with Open Data

In recent years, data story telling has emerged as a powerful and engaging form of communication. Using any data that you can find on data.vic tell us an interesting story in the form of a feature article or video report.

Go to Challenge | 18 teams have entered this challenge.

Helping a social impact ‘start up’ (small organisation) to tell their story

Small and informal community/interest groups who have formed to solve local problems need data to know if their activities are making a difference and to re-design programs. How can we help these groups tell their story through data so they can seek support (political, financial, and on the ground) by showing how their programs are working, and decide where to focus next?

Go to Challenge | 23 teams have entered this challenge.

Helping Start-ups and New Small Businesses in Australia

Choose one of the following questions to address: 1. What trends in business activity can help encourage self-employment through NEIS? 2. What type of NEIS businesses are being started and are successful (participated in the full 12 months of NEIS Assistance) and what can we learn from broader industry growth areas? 3. How do we encourage self-employment through greater participation in NEIS for cohorts currently underrepresented?

Go to Challenge | 21 teams have entered this challenge.