Research
Big Ideas, Real Impact.
The conceptual framework of the gRAIn Hub, integrates five connected capabilities: Responsible AI, Sustainable Grain, Sensing, Agronomy, Digital Carbon. Combined, these spheres of expertise will resolve the challenges of transforming Australia’s grain industry into a sustainable AI-driven sector. Importantly, these capabilities address issues across the entire cultivation cycle, from breeding to farming to the supply chain. Across each theme, the existing barriers to adopting AI will be identified first. Then, RAI-based methods will be developed to resolve these barriers and produce outputs that directly support the goals of this Hub.
Research Themes
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This theme will focus on advancing grain breeding research through three core projects: P1.1 – identifying the optimal traits for photosynthesis; P1.2 – early prediction of environmental stress tolerances; and P1.3 optimising the balance between nitrogen use and fixation. By integrating genomic breeding with cutting-edge RAI methods for predictive analysis and optimisation, this theme aims to develop innovative approaches to reduce CE, improve carbon fixation (CF) and enhance climate adaptability. In terms of RAI, a key emphasis is placed on explainable AI as a way of providing transparent justifications for the AI’s predictions and optimisation decisions.
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This theme centres on the comprehensive monitoring and autonomous management of farming operations through three core projects: P2.1 – developing a self-sustainable grain production system; P2.2 – advancing soil digitisation; and P2.3 – creating a smart farm monitoring system. To overcome the challenges of unreliable AI results in the real world, where data can be noisy and limited, this theme will prioritise RAI in terms of robust AI techniques. The theme aims to improve the accuracy and resilience of AI-driven solutions in smart farming with reliable decision-making tools and system controls that work across a diverse range of agricultural environments.
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This theme targets the grain distribution stage, aiming to deliver sustainable, high-efficiency supply chain solutions through four core projects. P3.1 targets storage with ways to prevent deterioration. P3.2 concerns optimising logistics. P3.3 involves a grain traceability exercise, while P3.4 entails developing a system to track digital carbon credits (CC). To address privacy issues, this theme emphasises innovations in privacy-preserving AI to ensure that proprietary and sensitive information remains secure during supply chain optimisation. Incorporating these RAI, the hub will support a transparent, resilient, and sustainable grain supply chain.
Related Publications
Responsible AI theories and algorithms
Lin, A., Lu, J., Xuan, J., Zhu, F., & Zhang, G. (2020). A causal Dirichlet mixture model for causal inference from observational data. ACM Transactions on Intelligent Systems and Technology, 11(3), 1-29.
Fang, Z., Lu, J., Liu, F., Xuan, J. & Zhang, G. (2020) ‘Open set domain adaptation: Theoretical bound and algorithm’, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 10, pp. 4309–4322.
Lu, J., Zuo, H., & Zhang, G. (2019). Fuzzy multiple-source transfer learning. IEEE Transactions on Fuzzy Systems, 28(12), 3418-3431.
Sustainable Grain
Li, C. (co-author), Avni, R. et al. (2025) A pangenome and pantranscriptome of hexaploid oat. Nature.
Jayakodi, M., Padmarasu, S., Haberer, G., Bonthala, V. S., Gundlach, H., Monat, C., Lux, T., Kamal, N., Lang, D., Himmelbach, A., Ens, J., Zhang, X.-Q., Angessa, T. T., Zhou, G., Tan, C., Hill, C., Wang, P., Schreiber, M., Boston, L. B., Plott, C., Jenkins, J., Guo, Y., Fiebig, A., Budak, H., Xu, D., Zhang, J., Wang, C., Grimwood, J., Schmutz, J., Guo, G., Zhang, G., Mochida, K., Hirayama, T., Sato, K., Chalmers, K. J., Langridge, P., Waugh, R., Pozniak, C. J., Scholz, U. & Mayer, K. F. X. (2020) ‘The barley pan-genome reveals the hidden legacy of mutation breeding’, Nature.
Karunarathne, S. D., Han, Y., Zhang, X.-Q. & Li, C. (2022) ‘CRISPR/Cas9 gene editing and natural variation analysis demonstrate the potential for HvARE1 in improvement of nitrogen use efficiency in barley’, Journal of Integrative Plant Biology, vol. 64, no. 3, pp. 756–770.
Hu, H., Wang, P., Angessa, T. T., Zhang, X.-Q., Chalmers, K. J., Zhou, G., Hill, C. B., Jia, Y., Simpson, C., Fuller, J., Saxena, A., Al Shamaileh, H., Iqbal, M., Chapman, B., Kaur, P., Dudchenko, O., Lieberman Aiden, E., Keeble-Gagnere, G., Westcott, S., Leah, D., Tibbits, J. F., Waugh, R., Langridge, P., Varshney, R., He, T. & Li, C. (2023) ‘Genomic signatures of barley breeding for environmental adaptation to the new continents’, Plant Biotechnology Journal, vol. 21, no. 9, pp. 1719–1721. doi:10.1111/pbi.14077.
Sensing
Yang, Z., Cole, K. L. H., Qiu, Y., Somorjai, I. M. L., Wijesinghe, P., Nylk, J., Cochran, S., Spalding, G. C., Lyons, D. A. & Dholakia, K. (2019) ‘Light sheet microscopy with acoustic sample confinement’, Nature Communications, vol. 10, no. 1. doi:10.1038/s41467-019-08514-5.
Vettenburg, T., Dalgarno, H. I. C., Nylk, J., Coll-Lladó, C., Ferrier, D. E. K., Čižmár, T., Gunn-Moore, F. J. & Dholakia, K. (2014) ‘Lightsheet microscopy using an Airy beam’, Nature Methods, vol. 11, no. 5, pp. 541–544. doi:10.1038/nmeth.2922.
Arita, Y., Mazilu, M. & Dholakia, K. (2013) ‘Laser-induced rotation and cooling of a trapped microgyroscope in vacuum’, Nature Communications, vol. 4, no. 1. doi:10.1038/ncomms3374.
Agronomy
Murphy, D., Leopold, M., van Gool, D., Hoyle, F. & Stockdale, E. (2017) ‘Soil quality: 1 Constraints to plant production’, SoilsWest, vol. 1, no. 1, Murdoch University, Perth.
Vuong, P., Lim, D. J., Murphy, D. V., Wise, M. J., Whiteley, A. S. & Kaur, P. (2021) ‘Developing bioprospecting strategies for bioplastics through the large-scale mining of microbial genomes’, Frontiers in Microbiology, vol. 12. doi:10.3389/fmicb.2021.697309.
Mathes, F., Murugaraj, P., Bougoure, J., Pham, V. T. H., Truong, V. K., Seufert, M., Wissemeier, A. H., Mainwaring, D. E. & Murphy, D. V. (2020) ‘Engineering rhizobacterial community resilience with mannose nanofibril hydrogels towards maintaining grain production under drying climate stress’, Soil Biology & Biochemistry, vol. 142. doi:10.1016/j.soilbio.2020.107715.
Digital Carbon
Alghanmi, N. A., Alghanmi, N., Alhosaini, H. & Hussain, F. K. (2023) ‘Carbon credits storage: A comparative multifactor analysis of on-chain vs off-chain approaches’, 2023 IEEE International Conference on e-Business Engineering (ICEBE), pp. 134–142. doi:10.1109/icebe59045.2023.00031.
Azadi, M., Emrouznejad, A., Ramezani, F. & Hussain, F. K. (2022) ‘Efficiency measurement of cloud service providers using network data envelopment analysis’, IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 348–355. doi:10.1109/tcc.2019.2927340.
Alhadrami, Y. (2020) ‘Real time dataset generation framework for intrusion detection systems in IoT’, Future Generation Computer Systems, vol. 108, pp. 414–423. doi:10.1016/j.future.2020.02.051.