Research & Development

A key research focus at AAGI-AU is the design of experiments (DoE), and the development of effective sampling protocols for field experiments and plant breeding trials. This work is conducted in collaboration with leading statisticians and global experts in DoE and sampling theory, and grounded in the practical challenges faced by growers working on investments within the GRDC’s Research & Development portfolio. Our research delivers computationally efficient and user-friendly solutions that can be readily applied to real-world agricultural settings, ensuring broad accessibility and immediate research impact.

Causal learning revolutionises crop development by revealing the true drivers of yield potential, soil health, and long-term sustainability. Causal analytics research enables growers, researchers, and decision-makers to answer “what if” questions about their crop management strategies: for instance, estimating the impact of a new fertiliser, irrigation practice, or climate-smart technology under various conditions. By intersecting the expertise in statistics, machine learning, and mathematical optimisation available at AAGI-AU, we aim to accelerate the translation of research into practical agronomic solutions, rapidly benefiting grains growers and the broader Australian agricultural industry.

AAGI maintains a strong national focus on plant breeding analytics research, supported by core analytics teams embedded across all of the AAGI strategic partners. At AAGI-AU, our vast network of experts spans across national and international collaborators, including leading institutions such as Australian National University, Queensland DPI, and AV Data Analytics. Our research is driven by industry priorities and encompasses the full analytics pipeline of plant breeding, including experimental design, complex comparative modelling of multi-environment trials, high-throughput phenotyping analytics, and the integration of high-dimensional ‘omics data. With our strong collaborative links to the grains industry, we efficiently drive variety improvement in breeding programs and maximise the benefit for Australian growers.

AAGI-AU, in close partnership with its strategic and project partners, leads novel research in Decision Agriculture (DA), a field that leverages computational models and advanced analytics to provide actionable and data-driven insights to the agricultural sector. Our research is inherently multidisciplinary, drawing on expertise across AAGI-AU in areas such as data science, mathematics, plant science, and agronomic data assimilation. Collaboration with the GRDC and key stakeholders in the Australian grains industry provides invaluable domain-specific knowledge and high-quality data streams. This integrated approach ensures that the decision support tools developed through our DA research are scientifically robust and industry-focused, providing real-time value in the face of Australia’s dynamic and evolving agricultural environment.

AAGI boasts a dynamic and extensive network of Machine Learning and Artificial Intelligence (ML/AI) researchers and engineers across its national partners. AAGI-AU has a close association with the Australian Institute for Machine Learning, a globally recognised leader in ML/AI research. Our research spans a wide range or applications relevant to the Australian grains industry, including using state-of-the-art ML/AI algorithms for complex predictive tasks and generating insights, as well as development of robust image-based computer vision solutions that use remote or proximal sensing data to accurately extract proxy traits for plants, pests, and pathogens to produce critical inputs for downstream analytics pipelines. These innovations enhance the decision-making, productivity and sustainability for Australia’s grain growers.

At AAGI-AU, statistical computing and software development form a foundational pillar of our research ecosystem. Our work focuses on the development and application of software tools tailored to meet the modern demands of Australian grains research and development, including scripting in R and Python, as well as larger applications and libraries written in powerful low-level programming languages such as C++. Our software is designed with usability in mind, and enables scalable, reproducible and high-performance analytics for all researchers and growers working in the Australian grains industry. By embedding software development within our broader research agenda, AAGI-AU ensures that our tools are fit-for-purpose and industry-aligned, providing the necessary computational infrastructure needed to drive innovation and research insights.