Automated estimation of tiller number in wheat by ribbon detection
- 343 Downloads
The advent of high-throughput phenotyping installations signals a need for plant biology to use pattern analysis and recognition techniques, especially when analysis is done via digital images. Such installations also provide an opportunity to computer vision. We describe one such application at the UK National Plant Phenomics Centre, in which historically measurements have been made in a labour-intensive manual manner. We develop an estimator of tiller number in growing wheat which, when exploiting per-day averaging, temporal interpolation and dynamic programming, delivers measurements of finer-grain and no less accuracy than manually, and provides observations on plant treatments hitherto difficult or impossible to obtain. The approach developed lends itself to reuse for any similar imaging setup, and plants with tillering characteristics similar to wheat. We consider the work a useful exemplar for co-operation between biologists and computer scientists in such installations.
KeywordsSmall grain cereals Branching Plant development Computer vision
This work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC Grant ref numbers BB/J004405/1 and BB/J004464/1), the European Union (EPPN, an Integrating Activity, Research Infrastructure project funded by the European Union under FP7 Capacities Programme. Grant Agreement No. 284443) and the Biosciences, Environment and Agriculture Alliance (BEAA), a strategic partnership between Aberystwyth and Bangor universities. We are grateful to NPPC staff for technical support and constructive discussions.
- 1.Australian Plant Phenomics Facility. http://www.plantphenomics.org.au/ (2014)
- 4.Campillo, C., Garcia, M., Daza, C., Prieto, M.: Study of a non-destructive method for estimating the leaf area index in vegetable crops using digital images. Hortscience 45(10), 1459–1463 (2010)Google Scholar
- 8.Frangi, A., Niessen, W., Vincken, K., Viergever, M.: 1998. Multiscale vessel enhancement filtering. In: Medical Image Computing and Computer-Assisted Intervention—miccai98, pp. 130–137. Springer, BerlinGoogle Scholar
- 10.Gallagher, J., Biscoe, P.: A physiological analysis of cereal yield. II: Partitioning of dry matter. Agric. Prog. 53, 51–70 (1978)Google Scholar
- 11.Hartmann, A., Czauderna, T., Hoffmann, R., Stein, N., Schreiber, F.: HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinform. 12(1), 148 (2011)Google Scholar
- 13.Home-Grown Cereals Authority: Optimum winter wheat plant production. http://archive.hgca.com/publications/documents/cropresearch/topic36.pdf (2000)
- 14.Jülich Plant Phenotyping Centre. http://www.fz-juelich.de/ibg/ibg-2/DE/Organisation/JPPC/JPPC_node.html (2014)
- 19.Satorre, E., Slafer, G. (eds.): Wheat: Ecology and Physiology of Yield Determination. The Haworth Press, New York (1999)Google Scholar
- 20.Sirault, X., Fripp, J., Paproki, A., Guo, J., Kuffner, P., Daily, H., Li, R., Furbank, R.: PlantScan: a three-dimensional phenotyping platform for capturing the structural dynamic of plant development and growth. In: 7th International Conference on Functional–Structural Plant Models, 75 (2013)Google Scholar
- 22.Šonka, M., Hlaváč, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, 4th edn. Cengage Learning, Boston (2014)Google Scholar
- 23.The UK National Plant Phenomics Centre. http://www.plant-phenomics.ac.uk/en/ (2014)