Responsible AI in agriculture

What problem are we trying to solve?

Australia’s agricultural sector faces significant productivity challenges, environmental constraints, and labour shortages that are magnified by uncertainties such as climate change and market volatility. While artificial intelligence (AI) and emerging technologies have shown potential to demystify some of the uncertainties associated with these challenges, more work is needed to support responsible development and communication of AI-based technologies tailored to the Australian agricultural sector.

What do we hope to achieve?

We will develop a series of papers that:

Examine how Australia’s AI innovation in agriculture compares globally and establish a specific vision for AI in Australian agriculture within the Ag2050 framework.
Explore how AI can contribute to a productive, profitable, and sustainable future at the farm and food system level.

We will also engage with the agricultural sector in an AI solution co-design process. This process aims to develop flexible, trustworthy AI-based technology that meets real-world needs in the Australian agricultural sector. The technology leverages advanced modelling, enhanced by machine learning, to accelerate simulation capabilities, unlock new uses, and empower users with more flexible tools.

Want to be part of co-designing an AI solution in agriculture?

Participate in our first user survey here!

Explore opportunities for AI in Robotics, Internet of Things (IoT), Computer Vision, Data Analysis, Large Language Models (LLMs), and other AI research to empower the Ag2050 program of work. This may include developing data pipelines for integrating openly available historical agricultural data and addressing the communication of uncertainties and biases associated with modelling.

Through this, we aim to support more transparent decision-making processes that consider the complexities and limitations of agricultural AI modelling. We strive to assist stakeholders in adopting data-informed practices across the agricultural landscape.