Dr Jinhai Cai, Ms Marcela Cespedes, Mr Ross Frick, Dr Mahmood Golzarian, Professor Stan Miklavcic
Applied to plant biology, automated phenotypic analysis of plant images, captured as a function of growth conditions, can provide us with a large volume of information on the function of genes as well as on the impact of abiotic stress on plants. Among agricultural plants, cereals are the most important crops in the world. Most importantly, cereal (maize, wheat and rice) together account for 87% of all grain production worldwide. In this project, we estimate the biomass of leaves, stems and spikes of cereal plants from 2D images. Additionally, we study the impact of abiotic stresses on each component as well as on the plant as a whole.
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