Plant growth modelling from 2D images
Dr Jinhai Cai, Mr Ross Frick, Dr Mahmood Golzarian, Dr Hamid Laga and Professor Stan Miklavcic
The aim of this project is to automatically model the temporal plant growth patterns based on a sequence of images and to map the linkage between genotype and phenotype. The outcomes of this research program are becoming increasingly important given the establishment of advanced high throughput plant phenotyping facilities. The identification of plant growth stages is key to developing a plant growth stage ontology. It includes the recognition of plant leaves, stems, tillers, and spikes. Some plant components can be differentiated by temporal and spatial features. Therefore, it is essential to track the growth of the plant through its life. This research program will
- develop image segmentation methods for plant images,
- extract and analyse important features from the images,
- design and implement pattern recognition methods to identify leaves, stems and spikes,
- develop leaf tracking methods to distinguish leaves and tillers,
- identify plant growth stages based on plant ontologies
J. Cai, M.R. Golzarian, S.J. Miklavcic (2011) Novel image segmentation based on machine learning and its application to plant analysis, Int. Journal of Information and Electronics Engineering, 1, 79-84.
J. Cai, M.R. Golzarian, S.J. Miklavcic, (2011) Novel image segmentation using Gaussian mixture models - application to plant phenotypic analysis, 3rd International Conference on Signal Acquisition and Processing (ICSAP), 308-312.
M.R. Golzarian, J. Cai, R.A. Frick, S.J. Miklavcic, (2011) Segmentation of cereal plant images using level set methods. A comparative study, Int. Journal of Information and Electronics Engineering, 1, 72-78.
M.R. Golzarian, J. Cai, R.A. Frick, S.J. Miklavcic (2011) A comparative evaluation of level set algorithms with applications for the segmentation of narrow-leaf plants", 3rd International Conference on Signal Acquisition and Processing (ICSAP), pp282-286.
M.R. Golzarian, R.A. Frick, K. Rajendran, B. Berger, S. Roy, M. Tester, D.S. Lun. (2011) Accurate estimation of plant biomass using information extracted from high-throughput plant images, Journal of Plant Methods, 7(2), DOI: 10.1186/1746-4811-7-2.
Areas of study and research
- Health Research
- Alliance for Research in Exercise, Nutrition and Activity (ARENA)
- Centre for Cancer Biology
- Centre for Drug Discovery and Development
- Centre for Population Health Research
- Centre of Research Excellence for the Prevention of Chronic Conditions in Rural and Remote High Risk Populations
- International Centre for Allied Health Evidence
- Medicine and Device Surveillance CRE
- Quality Use of Medicines and Pharmacy Research Centre
and Social Sciences
- Art, Architecture and Design
- Communication, International Studies and Languages
- Psychology, Social Work and Social Policy
- Hawke Research Institute
- Asia Pacific Centre for Work Health and Safety
- Australian Centre for Child Protection
- Barbara Hardy Institute
- Centre for Research in Education
- Hawke EU Jean Monnet Centre of Excellence
- Centre for Islamic Thought and Education
- International Centre for Muslim and non-Muslim Understanding
- Research Centre for Languages and Cultures
- Zero Waste SA Research Centre for Sustainable Design and Behaviour (sd+b)
IT, Engineering and
- Future Industries Institute
- UniSA College