Data science encompasses data processing, modelling, data visualisation, machine learning, statistical analysis, and database management. The goal is to extract information for prediction and visualisation of the response of physical systems, for example, for decision support or machine control.
Data processing

This includes data collection (often relating to measurements derived from sensors), data cleaning, processing (including mathematical processes such as Fourier analyses and tomographic inversion), and statistical analysis, such as for Krigging. The processed data are then prepared for decision-making, either human via visualisation, or via machine learning. Our work includes processing satellite measurements for land management.

Contact: Jeffrey Hsiao


We use modelling to predict responses, either just theoretically or to supplement physical measurements. It is based on a theoretical understanding of natural and man-made systems, usually implemented as mathematical algorithms that are then validated by experimental observations. Examples of our work include environmental decision support by prediction of contaminant transport in groundwater, describing how the properties of materials affect the passage of microwaves through them, and modelling the effect of electrode-ion interaction on the electrochemical conversion of water-borne CO2 to carbonates.

Machine learning

Machine learning, commonly but misleadingly referred to as artificial intelligence, focuses on creating algorithms and models that make predictions or decisions based on data, including physical measurements. We have substantial experience in machine learning, particularly its application to image processing and machine vision. For example, we have applied machine learning to enable identification of predators from paw sensing pads and from camera-derived images, and are exploring new methods for training algorithms that place less reliance on the typically used large data sets that are meticulously labelled by hand.

Contact: Jeffrey Hsiao

Graph neural networks

We also specialise in more unusual and leading-edge aspects of machine learning, including graph neural networks (GNNs), which specialise in applications that are best described as mathematical graphs. We are investigating data visualisation, statistical analysis, and database management related to GNNs for applications such as landscape optimisation and data security. For example we developed a predictive model to automate the decision–making in cane pruning of wine grapes. We published our results in an international journal and tested our algorithm on both synthetic vines and real vines from the Blenheim and Nelson wine regions.

Contact: Jeffrey Hsiao



Modelling wine grapevines for autonomous robotic cane pruning, Biosystems Engineering
Williams H, Smith D, Shahabi J, Gee T, Nejati M, McGuinness B, Black K, Tobias J, Jangali R, Lim H, Duke M, Bachelor O, McCulloch J, Green R, O'Connor M, Gounder S, Ndaka A, Burch K, Fourie J, Hsiao J, Werner A, Agnew R, Oliver R, MacDonald B
Evaluating sources of variability in inflorescence number, flower number and the progression of flowering in Sauvignon blanc using a Bayesian modelling framework, Vol. 56 No. 1 (2022): OENO One
Amber K. Parker, Jaco Fourie, Mike C. T. Trought, Kapila Phalawatta, Esther Meenken, Anne Eyharts, Elena Moltchanova
Characterising retained dormant shoot attributes to support automated cane pruning on Vitis vinifera L. cv. Sauvignon Blanc, “Australian Journal of Grape and Wine Research”
Epee P T M, Schelezki OJ, Parker A K, Trought M C T, Werner A, Hofmann R W, Almond P, Fourie J
Robust human instance segmentation in a challenging forest environment 36th International Conference on Image and Vision Computing New Zealand (IVCNZ)
K. Pahalawatta, J. Fourie, J. Potgieter, H. Ascot-Evans and A. Werner
Towards automated grape vine pruning: Learning by example using recurrent graph neural networks, “International Journal of Intelligent Systems”
Fourie J, Bateman C, Hsiao J, Pahalawatta K, Batchelor O, Epee Misse P, Werner A
Detection and classification of opened and closed flowers in grape inflorescences using Mask R-CNN IVCNZ Conference
Pahalawatta, K; Fourie, J; Carey, P; Werner, A; Parker, A
Assessment of Mixed Sward Using Context Sensitive Convolutional Neural Networks, “Frontiers in Plant Science”
Bateman, C., Fourie, J., Hsiao, J., Irie, K., Heslop, A., Anthony, H., Hagedorn, M., Jessep, B., Gebbie, S., Ghamkhar, K.