Crop Scientist: Digital Agriculture
Tel: 031 508 7400 (Switchboard)
Tel: 031 508 7446 (Direct)


Collaborate with multi-disciplinary teams of scientists and agricultural engineers to build digitally enabled research capacity, technology development, and knowledge exchange capabilities.

Exploit geospatial data and related technologies to develop digital resources that are rapid, precise, and cost-effective in support of crop improvement, crop protection, and crop management research.

To explore potential commercial opportunities for science-based digital agricultural solutions for sugarcane growers.

Mentoring fellow scientists, postgraduate students, technical staff, and research interns.


Remote sensing, GIS, Statistical data analysis, Supervised learning, Digital image processing.

Research Interests

Precision agriculture; developing models using geospatial data and related technologies for optimising sugarcane farming, and modelling plant stress resulting from climate change and pest & disease.

Key Outcomes

To implement and support the sugarcane industry’s Agriculture 4.0 initiatives.

Key Publications

Hacking C, Poona N, & Poblete Echeverría C 2020. Vineyard yield estimation using 2-D proximal remote sensing: a multitemporal analysis. Oeno one 54(4), 793-812.

Loggenberg K & Poona N 2020. A feature selection approach for terrestrial hyperspectral image analysis. South African Journal of Geomatics 9(2), 302-320.

Poona NK & Ismail R 2019. Developing optimized spectral indices using machine learning to model Fusarium circinatum stress in Pinus radiata seedlings. Journal of Applied Remote Sensing 13(3): 034515. doi:10.1117/1.JRS.13.034515.

Hacking C, Poona N, Manzan N, & Poblete-Echeverría C 2019. Investigating 2-D and 3-D proximal remote sensing techniques for vineyard yield estimation. Sensors 19(3652). doi:10.3390/s19173652.

Loggenberg K, Strever A, Greyling B, & Poona N 2018. Modelling water stress in a shiraz vineyard using hyperspectral imaging and machine learning. Remote Sensing 10(202). doi:10.3390/rs10020202.

Poona N, Van Niekerk A, & Ismail R 2016. Investigating the utility of oblique tree-based ensembles for the classification of hyperspectral data. Sensors, 16(11): 1918.

Poona N, Van Niekerk A, Nadel R, & Ismail R 2016. Random Forest (RF) wrappers for waveband selection and classification of hyperspectral data. Applied Spectroscopy 70(2): 322-333.

Poona N & Ismail R 2014. Using Boruta-selected spectroscopic wavebands for the asymptomatic detection of Fusarium circinatum stress. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(9): 3764-3772.

Poona N, Ismail R 2013. Discriminating the occurrence of pitch canker fungus in Pinus radiata trees using QuickBird imagery and artificial neural networks. Southern Forests 75(1): 29-40.

Poona N & Ismail R 2013. Reducing hyperspectral data dimensionality using random forest based wrappers. Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS2013), 21-26 July, Melbourne, Australia. IEEE ISBN 978-1-4799-1114-1/13: 1470-1473.

Poona N & Ismail R 2012. Discriminating the early stages of Fusarium circinatum infection in Pinus radiata seedlings using high spectral resolution data. Proceedings of International Conference of the African Association of Remote Sensing and the Environment (AARSE2012), 29 October-2 November, El Jadida, Morocco.

Poona N & Ismail R 2012. Discriminating the occurrence of pitch canker infection in Pinus radiata forests using high spatial resolution QuickBird data and artificial neural networks. In: Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS2012), 22-27 July, Munich, Germany. IEEE ISBN 978-1-4673-1159-5/12: 3371-3374.


MSc (Environmental Science): University of KwaZulu-Natal

PhD (Geoinformatics): Stellenbosch University