Functional Plant Biology Functional Plant Biology Society
Plant function and evolutionary biology
RESEARCH ARTICLE

Image-based estimation of oat panicle development using local texture patterns

Roger Boyle A B , Fiona Corke A and Catherine Howarth A
+ Author Affiliations
- Author Affiliations

A National Plant Phenomics Centre, Institute of Biological and Environmental Research (IBERS), University of Aberystwyth, Aberystwyth, Cymru, SY23 3EB, UK.

B Corresponding author. Email: rob21@aber.ac.uk

Functional Plant Biology 42(5) 433-443 https://doi.org/10.1071/FP14056
Submitted: 19 February 2014  Accepted: 29 August 2014   Published: 26 November 2014

Abstract

Flowering time varies between and within species, profoundly influencing reproductive fitness in wild plants and productivity in crop plants. The time of flowering, therefore, is an important statistic that is regularly collected as part of breeding programs and phenotyping experiments to facilitate comparison of genotypes and treatments. Its automatic detection would be highly desirable. We present significant progress on an approach to this problem in oats (Avena sativa L.), an underdeveloped cereal crop of increasing importance. Making use of the many thousands of images of oat plants we have available, spanning different genotypes and treatments, we observe that during flowering, panicles (the flowering structures) betray particular intensity patterns that give an identifiable texture that is distinctive and discriminatory with respect to the main plant body and can be used to determine the time of flowering. This texture can be located by a filter, trained as a form of local pattern. This training phase identifies the best parameters of such a filter, which usefully discovers the scale of the panicle spikelets. The results demonstrate the success of the filter. We proceed to suggest and evaluate an approach to using the filter as a growth stage detector. Preliminary results show very good correspondence with hand-measured ground truth, and are amenable to improvement in several ways. Future work will build on this initial success and will go on to locate fully mature panicles, which have a different appearance, and assess whether this approach can be extended to a broader range of plants.

Additional keywords: Avena sativa L., filter, growth stages, spikelets.


References

Al-Tam F, Adam H, dos Anjos A, Lorieux M, Larmande P, Ghesquière A, Jouannic S, Shahbazkia HR (2013) P-TRAP: a panicle trait phenotyping tool. BMC Plant Biology 13, 122
P-TRAP: a panicle trait phenotyping tool.CrossRef |

Andrés F, Coupland G (2012) The genetic basis of flowering responses to seasonal cues. Nature Reviews. Genetics 13, 627–639.
The genetic basis of flowering responses to seasonal cues.CrossRef | 22898651PubMed |

Australian Plant Phenomics Facility (APPF) (2014) ‘Australian plant phenomics facility home page.’ (APPF: Adelaide) Available online at: http://www.plantphenomics.org.au/ [Verified 9 September 2014].

Campillo C, Garcia M, Daza C, Prieto M (2010) Study of a non-destructive method for estimating the leaf area index in vegetable crops using digital images. HortScience 45, 1459–1463.

Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 381–395.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.CrossRef |

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 | 1:CAS:528:DC%2BC3MXhsFOhu7%2FJ&md5=b65db152c2f1106c3dedfbf0aab2a85dCAS | 22074787PubMed |

Gertych A, Zhang A, Sayre J, Pospiech-Kurkowska S, Huang HK (2007) Bone age assessment of children using a digital hand atlas. Computerized Medical Imaging and Graphics 31, 322–331.
Bone age assessment of children using a digital hand atlas.CrossRef | 17387000PubMed |

Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F (2011) HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics 12, 148
HTPheno: an image analysis pipeline for high-throughput plant phenotyping.CrossRef | 21569390PubMed |

Hellinger E (1909) Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. Journal für die Reine und Angewandte Mathematik 136, 210–271.

Holland J B, Portyanko VA, Hoffman D, Lee M (2002) Genomic regions controlling vernalization and photoperiod responses in oat. Theoretical and Applied Genetics 105, 113–126.
Genomic regions controlling vernalization and photoperiod responses in oat.CrossRef | 1:CAS:528:DC%2BD38XmslGlt78%3D&md5=011387b3d059aec7dab7476a39bbff52CAS |

Huang C, Yang W, Duan L, Jiang N, Chen G, Xiong L, Liu Q (2013) Rice panicle length measuring system based on dual-camera imaging. Computers and Electronics in Agriculture 98, 158–165.
Rice panicle length measuring system based on dual-camera imaging.CrossRef |

IMAIOS (2014), ‘e-Anatomy.’ (IMAIOS: Montpellier). Available at: http://www.imaios.com/en/e-Anatomy [Verified 9 September 2014]

Jülich Forschungszentrum (2014) ‘Jülich plant phenotyping centre’. (Jülich Forschungszentrum:Jülich). Available at: http://www.fz-juelich.de/ibg/ibg-2/DE/Organisation/JPPC/JPPC_node.html [Verified 9 September 2014]. [in German].

Kubassova OA, Boyle RD, Radjenovic A (2007) Quantitative analysis of dynamic contrast-enhanced MRI datasets of the metacarpophalangeal joints. Academic Radiology 14, 1189–1200.
Quantitative analysis of dynamic contrast-enhanced MRI datasets of the metacarpophalangeal joints.CrossRef | 17889336PubMed |

Li Y, Fan X, Mitra NJ, Chamovitz D, Cohen-Or D, Chen B (2013) Analyzing growing plants from 4D point cloud data. ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2013) 32, 157

Locatelli AB, Federizzi LC, Milach SCK, Wight CP, Molnar SJ, Chapados JT, Tinker NA (2006) Loci affecting flowering time in oat under short-day conditions. Genome 49, 1528–1538.
Loci affecting flowering time in oat under short-day conditions.CrossRef | 1:CAS:528:DC%2BD2sXkvFeitro%3D&md5=5aca2991586149d29bb9c52ce48cbe92CAS | 17426767PubMed |

Locatelli AB, Federizzi LC, Milach SCK, McElroy AR (2008) Flowering time in oat: genotype characterization for photoperiod and vernalization response. Field Crops Research 106, 242–247.
Flowering time in oat: genotype characterization for photoperiod and vernalization response.CrossRef |

Meier, U (ed) (2001) ‘Growth stages of mono-and dicotyledonous plants.’ Technical report (Federal Biological Research Centre for Agriculture and Forestry: Berlin). Available at: http://www.bba.de/veroeff/bbch/bbcheng.pdf [Verified 10 September 2014].

Nava IC, Wight CP, Pacheco MT, Federizzi LC, Tinker NA (2012) Tagging and mapping candidate loci for vernalization and flower initiation in hexaploid oat. Molecular Breeding 30, 1295–1312.
Tagging and mapping candidate loci for vernalization and flower initiation in hexaploid oat.CrossRef |

UK National Plant Phenomics Centre (2014) ‘The National Plant Phenomics Centre at Aberystwyth University brings plant biology into the 21st century.’ (Aberystwyth University: Ceredigion). Available at: http://www.plant-phenomics.ac.uk/en/ [Verified 10 September 2014].

Ojala T, Pietikainen M, Maenpaa M (2002) Multiresolution gray-scale and rotation invariant texture classification with locally binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987.
Multiresolution gray-scale and rotation invariant texture classification with locally binary patterns.CrossRef |

Reis M, Morais R, Peres E, Pereira C, Contente O, Soares S, Valente A, Baptista J, Ferreira P, Bulas Cruz J (2012) Automatic detection of bunches of grapes in natural environment from color images. Journal of Applied Logic 10, 285–290.
Automatic detection of bunches of grapes in natural environment from color images.CrossRef |

Sirault X, Fripp J, Paproki A, Kuffner P, Nguyen C, Li R, Daily H, Guo J, Furbank R (2013) PlantScan: a three-dimensional phenotyping platform for capturing the structural dynamic of plant development and growth. In ‘Proceedings of the 7th International Conference on Functional–Structural Plant Models, Saariselkä, Finland, 9–14 June 2013. (Eds R Sievänen, E Nikinmaa, C Godin, A Lintunen, P Nygren) pp. 45–48. Available at http://www.metla.fi/fspm2013/proceedings

Song Y, Glasbey C, Horgan G, Polder G, Dieleman J, van der Heijden G (2014) Automatic fruit recognition and counting from multiple images. Biosystems Engineering 118, 203–215.
Automatic fruit recognition and counting from multiple images.CrossRef |

Sonka M, Hlaváč V, Boyle R (2014) ‘Image processing, analysis, and machine vision.’ 4th edn. (CEngage: Stamford, CT).

Tanner JM, Whitehouse RH (1975) ‘Assessment of skeletal maturity and prediction of adult height (TW2 method).’ (Academic: London).

Tinker NA, Kilian A, Wight CP, Heller-Uszynska K, Wenzl P, Rines HW, Bjornstad A, Howarth CJ, Jannink J-L, Anderson JM, Rossnagel BG, Stuthman DD, Sorrells ME, Jackson EW, Tuvesson S, Kolb FL, Olsson O, Federizzi LC, Carson ML, Ohm HH, Molnar SJ, Scoles GJ, Eckstein PE, Bonman JM, Ceplitis A, Langdon T (2009) New DArT markers for oat provide enhanced map coverage and global germplasm characterization. BMC Genomics 10, 39
New DArT markers for oat provide enhanced map coverage and global germplasm characterization.CrossRef | 19159465PubMed |

Worland AJ (1996) The influence of flowering time genes on environmental adaptability in European wheats. Euphytica 89, 49–57.
The influence of flowering time genes on environmental adaptability in European wheats.CrossRef |

Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Research 14, 415–421.
A decimal code for the growth stages of cereals.CrossRef |



Rent Article (via Deepdyve) Export Citation Cited By (2)