Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Functional Plant Biology Functional Plant Biology Society
Plant function and evolutionary biology
RESEARCH ARTICLE

A segmentation procedure using colour features applied to images of Arabidopsis thaliana

Ruben Ispiryan A C , Igor Grigoriev A , Wolfgang zu Castell A and Anton R. Schäffner B
+ Author Affiliations
- Author Affiliations

A Scientific Computing Research Unit, Helmholtz Zentrum München – German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany.

B Institute of Biochemical Plant Pathology, Helmholtz Zentrum München – German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany.

C Corresponding author. Email: ruben.ispiryan@helmholtz-muenchen.de

Functional Plant Biology 40(10) 1065-1075 https://doi.org/10.1071/FP12323
Submitted: 30 October 2012  Accepted: 20 April 2013   Published: 3 June 2013

Abstract

In studies of environmental effects on plant growth, the images of plants are often used for non-destructive measurements in phenotyping. In this work, a computational procedure has been developed to segment images of plants allowing an improved separation of plants and other types of objects in the frame such as moss or soil. The proposed procedure is based on colour analysis and image morphology. The red-green-blue (RGB) values are transformed into a colour space as ratios of R, G and B vs the sum of R, G, and B channels. We introduce an approach to render the training set of pixels on a Microsoft Excel two-dimensional graph and a technique to determine the discriminant regions of pixel classes. Two approaches for the classification based on colour analysis are shown: an automatic method using support vector machines and a procedure based on visual inspection. The segmentation procedure is designed to classify more than two object types utilising flexibly curved boundaries of discriminant regions that can also be non-convex. We propose a machine-vision algorithm to detect plant features – leaf anthocyanin accumulation and trichomes. The procedures of segmentation and feature detection are applied to images of Arabidopsis thaliana (L.) Heynh. that grow under either normal or drought stress conditions.

Additional keywords: anthocyanin accumulation, plant discrimination, surface traits, trichome density.


References

Arvidsson S, Pérez-Rodríiguez P, Mueller-Roeber B (2011) A growth phenotyping pipeline for Arabidopsis thaliana integrating analysis and rosette area modeling for robust quantification of genotype effects. New Phytologist 191, 895–907.
A growth phenotyping pipeline for Arabidopsis thaliana integrating analysis and rosette area modeling for robust quantification of genotype effects.Crossref | GoogleScholarGoogle Scholar | 21569033PubMed |

Backes AR, Bruno OM (2009) Plant leaf identification using multi-scale fractal dimension. Lecture Notes in Computer Science 5716, 143–150.
Plant leaf identification using multi-scale fractal dimension.Crossref | GoogleScholarGoogle Scholar |

Clark RT, MacCurdy RB, Jung JK, Shaff JE, McCouch SR, Aneshansley DJ, Kochian LV (2011) Three-dimensional root phenotyping with a novel imaging and software platform. Plant Physiology 156, 455–465.
Three-dimensional root phenotyping with a novel imaging and software platform.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXnvFWrs7k%3D&md5=1fda422b449c771af408888818d721a3CAS | 21454799PubMed |

Dengkui A, Minzan L, Zhang L (2010) Measurement of tomato leaf area using computer image processing technology. Sensor Letters 8, 56–60.
Measurement of tomato leaf area using computer image processing technology.Crossref | GoogleScholarGoogle Scholar |

Dornbusch T, Andrieu B (2010) Lamina2Shape-An Image processing tool for an explicit description of lamina shape tested on winter wheat (Triticum aestivum L.). Computers and Electronics in Agriculture 70, 217–224.
Lamina2Shape-An Image processing tool for an explicit description of lamina shape tested on winter wheat (Triticum aestivum L.).Crossref | GoogleScholarGoogle Scholar |

Du J-X, Wang X-F, Zhang G-J (2007) Leaf shape based plant species recognition. Applied Mathematics and Computation 185, 883–893.
Leaf shape based plant species recognition.Crossref | GoogleScholarGoogle Scholar |

El-Ghazal A, Basir OA, Belkasim S (2007) Shape-based image retrieval using pair-wise candidate co-ranking. In ‘Lecture notes in computer science, vol. 4633’. (Eds MS Kamel, AC Campilho) pp. 650–661. (Springer: Berlin)

Gebhardt S, Schellberg J, Lock R, Kühbauch W (2006) Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing. Precision Agriculture 7, 165–178.
Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing.Crossref | GoogleScholarGoogle Scholar |

Granier CH, Aguirrezabal L, Chenu K, Cookson SJ, Duzat M, Hamard PH, Thioux J-J, Rolland G, Bouchier-Combaud S, Lebaudy A, Muller B, Simonneau T, Tardieu F (2006) PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytologist 169, 623–635.
PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit.Crossref | GoogleScholarGoogle Scholar |

Guru DS, Sharath YH, Manjunath S (2010) Texture features and KNN in classification of flower images. IJCA. Special Issue on RTIPPR 1, 21–29.

Handley R, Ekbom B, Agren J (2005) Variation in trichome density and resistance against a specialist insect herbivore in natural populations of Arabidopsis thaliana. Ecological Entomology 30, 284–292.
Variation in trichome density and resistance against a specialist insect herbivore in natural populations of Arabidopsis thaliana.Crossref | GoogleScholarGoogle Scholar |

Hemming J, Rath T (2001) Computer-vision-based weed identification under field conditions using controlled lighting. Journal of Agricultural Engineering Research 78, 223–243.

Hu MK (1962) Visual pattern recognition by moment invariants. I.R.E. Transactions on Information Theory IT-8, 179–187.

Jansen M, Gilmer F, Biskup B, Nagel KA, Rascher U, Fischbach A, Briem S, Dreissen G, Tittmann S, Braun S, De Jaeger I, Metzlaff M, Schurr U, Scharr H, Walter A (2009) Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWNSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Functional Plant Biology 36, 902–914.
Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWNSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhtlOgs7rF&md5=9313e93eb4947d7d2e0c86e62d837fe6CAS |

Kebapci H, Yanikoglu B, Unal G (2011) Plant image retrieval using color, shape and texture features. The Computer Journal 54, 1475–1490.
Plant image retrieval using color, shape and texture features.Crossref | GoogleScholarGoogle Scholar |

Lee KM, Li Q, Daley W (2007) Effects of classification method on color-based feature detection with food processing applications. IEEE Transactions on Automation Science and Engineering 4, 40–51.
Effects of classification method on color-based feature detection with food processing applications.Crossref | GoogleScholarGoogle Scholar |

Leister D, Varotto C, Pesaresi P, Niwerganall A, Salamini F (1999) Large-scale evaluation of plant growth in Arabidopsis thaliana by non-invasive image analysis. Plant Physiology and Biochemistry 37, 671–678.
Large-scale evaluation of plant growth in Arabidopsis thaliana by non-invasive image analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1MXms1ahsL8%3D&md5=2dc2025c5cfc6a0b318a5a4ed2974b84CAS |

Lobet G, Pagés L, Draye X (2011) A novel image-analysis toolbox enabling quantitative analysis of root system architecture. Plant Physiology 157, 29–39.
A novel image-analysis toolbox enabling quantitative analysis of root system architecture.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXht1Sit77P&md5=1a2e443f664bfda05bd36f59eff63013CAS | 21771915PubMed |

Namías R, Gallo C, Craviotto RM, Arango MR, Granitto PM (2012) Automatic grading of green intensity in soybean seeds. In ‘Proceedings of ASAI 2012, 13th Argentine symposium on artificial intelligence, Buenos Aires, Argentina, August 27–28’ . (Org G Stegmayer, MA Falappa) pp. 96–104. (SADIO: Buenos Aires)

Neto JC, Meyer EG, Jones DD, Samal AK (2006) Plant species identification using elliptic Fourier leaf shape analysis. Computers and Electronics in Agriculture 50, 121–134.
Plant species identification using elliptic Fourier leaf shape analysis.Crossref | GoogleScholarGoogle Scholar |

Ohta Y, Kanade T, Sakai T (1980) Colour information for region segmentation. Computer Graphics and Image Processing 13, 222–241.
Colour information for region segmentation.Crossref | GoogleScholarGoogle Scholar |

Philipp I, Rath T (2002) Improving plant discrimination in image processing by use of different colour space transformations. Computers and Electronics in Agriculture 35, 1–15.
Improving plant discrimination in image processing by use of different colour space transformations.Crossref | GoogleScholarGoogle Scholar |

Seckmeyer G, Payer HD (1993) A new sunlight simulator for ecological research on plants. Journal of Photochemistry and Photobiology. B, Biology 21, 175–181.
A new sunlight simulator for ecological research on plants.Crossref | GoogleScholarGoogle Scholar |

Thiel S, Dohring T, Kofferlein M, Kosak A, Martin P, Seidlitz HK (1996) A phytotron for plant stress research: how far can artificial lighting compare to natural sunlight? Journal of Plant Physiology 148, 456–463.
A phytotron for plant stress research: how far can artificial lighting compare to natural sunlight?Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK28XjslWjsL4%3D&md5=a5120123093fbedd1c2c93675a2e3f54CAS |

Tian LF, Slaughter DC (1998) Environmentally adaptive segmentation algorithm for outdoor image segmentation. Computers and Electronics in Agriculture 21, 153–168.
Environmentally adaptive segmentation algorithm for outdoor image segmentation.Crossref | GoogleScholarGoogle Scholar |

Trooien TP, Hermann DF (1992) Measurement and simulation of potato leaf area using image processing. Transactions of the ASABE 35, 1709–1718.

Walter A, Scharr H, Gilmer F, Zierer R, Nagel KA, Ernst M, Wiese A, Virnich O, Christ MM, Uhlig B, Jünger S, Schurr U (2007) Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCEEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytologist 174, 447–455.
Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCEEN: a setup and procedure designed for rapid optical phenotyping of different plant species.Crossref | GoogleScholarGoogle Scholar | 17388907PubMed |

Wang Q-P, Du J-X, Zhai C-M (2010) Recognition of leaf image based on ring projection wavelet fractal feature. Lecture Notes in Computer Science 6216, 240–246.
Recognition of leaf image based on ring projection wavelet fractal feature.Crossref | GoogleScholarGoogle Scholar |

Woebbecke DM, Meyer GE, Von Bargen K, Mortensen DA (1995) Color indices for weed identification under various soil, residue and lighting conditions. Transactions of the ASAE. American Society of Agricultural Engineers 38, 259–269.

Yao Q, Guan Z, Zhou Y, Tang J, Hu Y, Yang B (2009) Application of support vector machine for detecting rice diseases using shape and color texture features. In ‘International conference on engineering computation, ICEC ‘09’, Hong Kong, China, May 2–3’. pp. 79–83. (Curran Associates, Inc.: Red Hook, NY)