From image processing to computer vision: plant imaging grows up
Hannah Dee A C and Andrew French BA Computer Science, Aberystwyth University, Penglais, Aberystwyth SY23 3DB, UK.
B Schools of Biosciences and Computer Science, Centre for Plant Integrative Biology, University of Nottingham, Nottingham, UK.
C Corresponding author. Email: hmd1@aber.ac.uk
Functional Plant Biology 42(5) iii-v https://doi.org/10.1071/FPv42n5_FO
Published: 13 April 2015
Abstract
Image analysis is a field of research which, combined with novel methods of capturing images, can help to bridge the genotype–phenotype gap, where our understanding of the genotype has until now been leaps and bounds ahead of our ability to work with the phenotype. Methods of automating image capture in plant science research have increased in usage recently, as has the need to provide objective and highly accurate measures on large image datasets, thereby bringing the phenotype back to the centre of interest. In this special issue of Functional Plant Biology, we present some recent advances in the field of image analysis, and look at examples of different kinds of image processing and computer vision, which is occurring with increasing frequency in the plant sciences.
References
Atkinson JA, Wingen LU, Griffiths M, Pound MP, Gaju O, Foulkes MJ, Le Gouis J, Griffiths S, Bennett MJ, King J, Wells DM (2015) Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat. Journal of Experimental Botany| Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat.Crossref | GoogleScholarGoogle Scholar |
Boyle R, Corke F, Howarth C (2015) Image-based estimation of oat panicle development using local texture patterns. Functional Plant Biology 42, 433–443.
| Image-based estimation of oat panicle development using local texture patterns.Crossref | GoogleScholarGoogle Scholar |
Dhondt S, Wuyts N, Inzé D (2013) Cell to whole-plant phenotyping: the best is yet to come. Trends in Plant Science 18, 428–439.
| Cell to whole-plant phenotyping: the best is yet to come.Crossref | GoogleScholarGoogle Scholar |
Furbank RT, Tester M (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16, 635–644.
| Phenomics – technologies to relieve the phenotyping bottleneck.Crossref | GoogleScholarGoogle Scholar |
Horgan GW, Song Y, Glasbey CA, van der Heijden GWAM, Polder G, Dieleman JA, Bink MCAM, van Eeuwijk FA (2015) Automated estimation of leaf area development in sweet pepper plants from image analysis. Functional Plant Biology 42, 486–492.
| Automated estimation of leaf area development in sweet pepper plants from image analysis.Crossref | GoogleScholarGoogle Scholar |
Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nature Reviews. Genetics 11, 855–866.
| Phenomics: the next challenge.Crossref | GoogleScholarGoogle Scholar |
Kempthorne DM, Turner IW, Belward JA, McCue SW, Barry M, Young J, Dorr GJ, Hanan J, Zabkiewicz JA (2015) Surface reconstruction of wheat leaf morphology from three-dimensional scanned data. Functional Plant Biology 42, 444–451.
| Surface reconstruction of wheat leaf morphology from three-dimensional scanned data.Crossref | GoogleScholarGoogle Scholar |
Li L, Zhang Q, Huang D (2014) A Review of Imaging Techniques for Plant Phenotyping. Sensors 14, 20078–20111.
| A Review of Imaging Techniques for Plant Phenotyping.Crossref | GoogleScholarGoogle Scholar |
Mairhofer S, Sturrock C, Wells DM, Bennett MJ, Mooney SJ, Pridmore TP (2015) On the evaluation of methods for the recovery of plant root systems from X-ray computed tomography images. Functional Plant Biology 42, 460–470.
| On the evaluation of methods for the recovery of plant root systems from X-ray computed tomography images.Crossref | GoogleScholarGoogle Scholar |
Nelson CJ, Duckney P, Hawkins TJ, Deeks MJ, Laissue PP, Hussey PJ, Obara B (2015) Blobs and curves: object-based colocalisation for plant cells. Functional Plant Biology 42, 471–485.
| Blobs and curves: object-based colocalisation for plant cells.Crossref | GoogleScholarGoogle Scholar |
Pridmore TP, French AP, Pound MP (2012) What lies beneath: underlying assumptions in bioimage analysis. Trends in Plant Science 17, 688–692.
| What lies beneath: underlying assumptions in bioimage analysis.Crossref | GoogleScholarGoogle Scholar |
Strange H, Zwiggelaar R, Sturrock C, Mooney SJ, Doonan JH (2015) Automatic estimation of wheat grain morphometry from computed tomography data. Functional Plant Biology 42, 452–459.
| Automatic estimation of wheat grain morphometry from computed tomography data.Crossref | GoogleScholarGoogle Scholar |