eNLB - "The Future of AG"

Project Info

AgTech 2050 thumbnail

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?

Region: Australia

Challenge

Queensland OpenAPI

Region: Australia

Challenge

Environment and Science Data

Region: Queensland

Challenge

Spatial Information

Region: Queensland

Challenge

TM-Link Data Discovery

Region: Australia

Challenge

🌟 Improving the customer experience of government services

Region: New South Wales

Challenge

🌟 Community open data weather monitoring and alerts system

Region: Northern Territory

Challenge

🌟 Telling Stories with Open Data

Region: Victoria

Challenge

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

Region: Australia

Challenge

Helping Start-ups and New Small Businesses in Australia

Region: Australia

Challenge

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