Jaco Fourie
BEng, MScEng, PhD
Jaco leads Lincoln Agritech’s image processing and machine learning capability, applying computer vision and machine learning to solve complex challenges in agriculture and resource management.
With a PhD in Computer Vision from the University of Canterbury and a Master’s in Applied Mathematics from Stellenbosch University, Jaco has built a career at the forefront of imaging technologies. Before joining Lincoln Agritech in 2013, he worked at CropLogic and the Geospatial Research Centre, developing modelling and optimisation tools for industry.
Jaco is passionate about developing and integrating machine learning methods and models into real-world applications, building strategic capability that helps industries harness the power of data science.
Focus areas:
- Computer vision and image processing
- Machine learning (graph neural networks, physics informed neural networks)
- Data science and optimisation
Publications
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Developments and Applications of Electromagnetic Tomography in Process Engineering, Chemical Engineering Research and Design, June 2024
Sharifi M, Fourie J, Heffernan B, Young B
Preprint proof link
Foreign Object Detection in Aqueous Food Media using Surface Electric Potential and Machine Learning Techniques, International Conference Image and Vision Computing New Zealand, January 2024
Lee KW, Hayes M, Heffernan B, Bones P, Fourie J
https://doi.org/10.1109/IVCNZ64857.2024.10794483
Two-Stage Punch-Code Recognition Using a CNN and the Hough Transform, International Conference Image and Vision Computing New Zealand, January 2024
Fourie J, Pahalawatta K
http://dx.doi.org/10.1109/IVCNZ64857.2024.10794469
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
https://doi.org/10.1016/j.biosystemseng.2023.09.006
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
https://doi.org/10.1111/ajgw.12555
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
https://doi.org/10.20870/oeno-one.2022.56.1.4717
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
https://doi.org/10.1109/IVCNZ54163.2021.9653172
Solving the occlusion problem and learning by example, 72nd national conference of the American society for enology and viticulture
Fourie, J
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
https://doi.org/10.1002/int.22317
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
https://doi.org/10.1109/IVCNZ51579.2020.9290720
Towards automated grape vine pruning: Learning by example using recurrent graph neural networks, “International Journal of Intelligent Systems”
Fourie, J., Hsiao J., Bateman, C., Misse, P.E., Batchelor, O., Werner, 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.
https://doi.org/10.3389/fpls.2020.00159
Fusion of thermal and visible colour images for robust detection of people in forests, 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)
Fourie J, Pahalawatta K, Hsiao J, Bateman C, Carey P
doi:10.1109/IVCNZ48456.2019.8960964.
