2 results

Community Trust in Rural Industries 2022-2025 - Joint RDC initiative

Project number: 2023-179
Project Status:
Current
Budget expenditure: $48,855.43
Principal Investigator: Georgina Toose
Organisation: AgriFutures Australia
Project start/end date: 10 Apr 2024 - 14 Dec 2025
Contact:
FRDC

Need

A continuation of representative national surveys of the Australian public regarding attitudes towards trust and acceptance of rural industries, trends shifts, and drivers.

Objectives

1. Develop capability across the sector to monitor, anticipate and respond to shifts in the levels oftrust the community has in Australia's rural industries.
2. Build a common language and collective national narrative around the community trust challenge.
3. Identify common best practice approaches, strategies and interventions for building, rebuilding andmaintaining community trust.

Exploring semantic search and linking technologies for application on GrowAG platform

Project number: 2024-049
Project Status:
Current
Budget expenditure: $0.00
Principal Investigator: Alex Bundock
Organisation: AgriFutures Australia
Project start/end date: 28 Feb 2025 - 30 Sep 2025
Contact:
FRDC

Need

AgriFutures growᴬᴳ⋅ is the platform for Australian and global agrifood innovation. Explore research, technology, and commercialisation opportunities in one place. Connect with a diverse ecosystem including researchers, investors, and startups and discover funding avenues, list projects, and engage with over 400 organisations.

Agricultural research is vital for ensuring food security, sustainable farming practices, and rural development. AgriFutures growAG. involves collaboration between the Department of Agriculture Fisheries and Forestry (DAFF) and Australia’s 15 Research & Development Corporations (RDCs), collating details on the vast rural RDC investment landscape. Collation of project details on growᴬᴳ is currently a manual, keyword-based tagging system that suffers from limitations:
- Limited Semantic Understanding: Keywords often fail to capture the nuanced relationships between projects, leading to fragmented information retrieval.
- Scalability Issues: Manual tagging is time-consuming and prone to inconsistencies, hindering efficient data management as the database grows.
- Lack of Interoperability: The current system lacks the ability to seamlessly integrate with other agricultural datasets and knowledge bases.
This project proposes to develop an AI-driven solution for semantically linking agricultural research projects, enabling more accurate and comprehensive project navigation and knowledge discovery. By leveraging Natural Language Processing (NLP) and knowledge graph technologies, we aim to transform the current keyword-based system into a dynamic and interconnected knowledge repository.

Objectives

1. Provide seamless ingestion of RDC data onto the platform including automation, providing projectsummaries and tagging.
2. Facilitate advanced semantic search and exploration through the integration of AI and data visualisation techniques.
3. Provide a value add for the ecosystem, partners, and visitors by being able to query the underlying growAG data set and better understand potential opportunities and trends.