Image-based plant phenotyping with incremental learning and active contours

Ecological Informatics

Volumn 23, Issue , 2014, Pages 35-48

  Minervini, Massimo ^a   Abdelsamea, Mohammed M ^a   Tsaftaris, Sotirios A ^a  

^a IMT SCHOOL FOR ADVANCED STUDIES LUCCA   (Italy)

Author keywords

Active contour model; Agriculture; Gaussian mixture model; Machine learning; Phenotyping; Plant segmentation

Indexed keywords

GAUSSIAN METHOD; GENOME; IMAGE ANALYSIS; LEARNING; NUMERICAL MODEL; PHENOTYPE; PLANT COMMUNITY; SOFTWARE; STATISTICAL ANALYSIS;

ARABIDOPSIS;

EID: 84905095747     PISSN: 15749541     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecoinf.2013.07.004     Document Type: Article

References (61)

1. Abdelsamea M.M., Tsaftaris S.A. Active contour model driven by globally signed region pressure force. Proceedings of the 18th International Conference on Digital Signal Processing (DSP) 2013.
2. Alenya G., Dellen B., Torras C. 3D modelling of leaves from color and ToF data for robotized plant measuring. International Conference on Robotics and Automation (ICRA), IEEE 2011, 3408-3414.
3. Arbelaez P., Maire M., Fowlkes C., Malik J. Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2011, 33(5):898-916.
4. Arora A., Gupta A., Bagmar N., Mishra S., Bhattacharya A. A plant identification system using shape and morphological features on segmented leaflets: team IITK, CLEF 2012. CLEF (Online Working Notes/Labs/Workshop) 2012.
5. Arvidsson S., Pérez-Rodríguez P., Mueller-Roeber B. A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects. The New Phytologist 2011, 191(3):895-907.
6. Benfey P.N., Mitchell-Olds T. From genotype to phenotype: systems biology meets natural variation. Science 2008, 320(5875):495-497.
7. Cerutti G., Tougne L., Vacavant A., Coquin D. A parametric active polygon for leaf segmentation and shape estimation. Lecture Notes in Computer Science 2011, vol. 6938:202-213. Springer, Berlin Heidelberg. G. Bebis, R. Boyle, B. Parvin, D. Koracin, S. Wang, K. Kyungnam, B. Benes, K. Moreland, C. Borst, S. DiVerdi, C. Yi-Jen, J. Ming (Eds.).
8. Chan T.F., Sandberg B.Y., Vese L.A. Active contours without edges for vector-valued images. Journal of Visual Communication and Image Representation 2000, 11(2):130-141.
9. Chen S., Radke R.J. Level set segmentation with both shape and intensity priors. 12th International Conference on Computer Vision, IEEE 2009, 763-770.
10. Chéné Y., Rousseau D., Lucidarme P., Bertheloot J., Caffier V., Morel P., Belin E., Chapeau-Blondeau F. On the use of depth camera for 3D phenotyping of entire plants. Computers and Electronics in Agriculture 2012, 82:122-127.
11. Clément A., Vigouroux B. Unsupervised segmentation of scenes containing vegetation (Forsythia) and soil by hierarchical analysis of bi-dimensional histograms. Pattern Recognition Letters 2003, 24(12):1951-1957.
12. Colorimetry Tech. Rep. 15.2, CIE Publication 1986, Central Bureau of the CIE, Vienna.
13. Cremers D., Tischhäuser F., Weickert J., Schnörr C. Diffusion snakes: introducing statistical shape knowledge into the Mumford-Shah functional. International Journal of Computer Vision 2002, 50(3):295-313.
14. Cremers D., Osher S.J., Soatto S. Kernel density estimation and intrinsic alignment for knowledge-driven segmentation: teaching level sets to walk. International Journal of Computer Vision 2004, 36-44.
15. De Vylder J., Ochoa D., Philips W., Chaerle L., Van Der Straeten D. Leaf segmentation and tracking using probabilistic parametric active contours. Proceedings of the 5th International Conference on Computer Vision/Computer Graphics Collaboration Techniques, MIRAGE 2011 2011, 75-85. Springer-Verlag, Berlin Heidelberg.
16. De Vylder J., Vandenbussche F., Hu Y., Philips W., Van Der Straeten D. Rosette tracker: an open source image analysis tool for automatic quantification of genotype effects. Plant Physiology 2012, 160(3):1149-1159.
17. Declercq A., Piater J.H. Online learning of Gaussian mixture models: a two-level approach. Proceedings of the 3rd International Conference on Computer Vision Theory and Applications (VISAPP) 2008, vol. 1:605-611.
18. Dhondt S., Van Haerenborgh D., Van Cauwenbergh C., Merks R.M., Philips W., Beemster G.T., Inzé D. Quantitative analysis of venation patterns of Arabidopsis leaves by supervised image analysis. The Plant Journal 2012, 69(3):553-563.
19. Felzenszwalb P.F. Representation and detection of deformable shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005, 27(2):208-220.
20. Finkel E. With 'phenomics,' plant scientists hope to shift breeding into overdrive. Science 2009, 325(5939):380-381.
21. French A., Ubeda-Tomás S., Holman T.J., Bennett M.J., Pridmore T. High-throughput quantification of root growth using a novel image-analysis tool. Plant Physiology 2009, 150(4):1784-1795.
22. Furbank R.T., Tester M. Phenomics - technologies to relieve the phenotyping bottleneck. Trends in Plant Science 2011, 16(12):635-644.
23. Goëau H., Bonnet P., Joly A., Yahiaoui I., Barthélémy D., Nozha B., Molino J.-F. The ImageCLEF 2012 plant identification task. CLEF 2012 2012.
24. Goff, S.A., Vaughn, M., McKay, S., Lyons, E., Stapleton, A.E., Gessler, D., Matasci, N., Wang, L., Hanlon, M., Lenards, A., Muir, A., Merchant, N., Lowry, S., Mock, S., Helmke, M., Kubach, A., Narro, M., Hopkins, N., Micklos, D., Hilgert, U., Gonzales, M., Jordan, C., Skidmore, E., Dooley, R., Cazes, J., McLay, R., Lu, Z., Pasternak, S., Koesterke, L., Piel, W.H., Grene, R., Noutsos, C., Gendler, K., Feng, X., Tang, C., Lent, M., Kim, S.-J., Kvilekval, K., Manjunath, B.S., Tannen, V., Stamatakis, A., Sanderson, M., Welch, S.M., Cranston, K.A., Soltis, P., Soltis, D., O'Meara, B., Ane, C., Brutnell, T., Kleibenstein, D.J., White, J.W., Leebens-Mack, J., Donoghue, M.J., Spalding, E.P., Vision, T.J., Myers, C. R., Lowenthal, D., Enquist, B.J., Boyle, B., Akoglu, A., Andrews, G., Ram, S., Ware, D., Stein, L., Stanzione, D. The iPlant Collaborative: Cyberinfrastructure for Plant Biology, Frontiers in Plant Science 2 (34). http://dx.doi.org/10.3389/fpls.2011.00034.
25. Golzarian M., Frick R., Rajendran K., Berger B., Roy S., Tester M., Lun D. Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 2011, 7(1):2+.
26. Golzarian M.R., Lee M.K., Desbiolles J.M.A. Evaluation of color indices for improved segmentation of plant images. Transactions of the American Society of Agricultural and Biological Engineers 2012, 55(1):261-273.
27. Granier C., Aguirrezabal L., Chenu K., Cookson S.J., Dauzat M., Hamard P., Thioux J.-J., Rolland G., Bouchier-Combaud S., Lebaudy A., Muller B., Simonneau T., Tardieu F. 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 2006, 169(3):623-635.
28. Hartmann A., Czauderna T., Hoffmann R., Stein N., Schreiber F. HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics 2011, 12(1):148+.
29. Holtorf H., Guitton M.-C.C., Reski R. Plant functional genomics. Die Naturwissenschaften 2002, 89(6):235-249.
30. Howarth P., Rüger S. Evaluation of texture features for content-based image retrieval. Lecture Notes in Computer Science 2004, vol. 3115:326-334. Springer, Berlin Heidelberg.
31. Kim G., Xing E.P., Fei-Fei L., Kanade T. Distributed cosegmentation via submodular optimization on anisotropic diffusion. 13th International Conference on Computer Vision (ICCV) 2011, 169-176.
32. Kokorian J., Polder G., Keurentjes J.J.B., Vreugdenhil D., Olortegui Guzman M. An ImageJ based measurement setup for automated phenotyping of plants. Proceedings of the ImageJ User and Developer Conference 2010, 178-182.
33. Lee C.P., Snyder W., Wang C. Supervised multispectral image segmentation using active contours. Proceedings of the International Conference on Robotics and Automation (ICRA), IEEE 2005, 4242-4247.
34. Leister D., Varotto C., Pesaresi P., Niwergall A., Salamini F. Large-scale evaluation of plant growth in Arabidopsis thaliana by non-invasive image analysis. Plant Physiology and Biochemistry 1999, 37(9):671-678.
35. Leventon M., Faugeras O., Grimson E., Wells W. Level set based segmentation with intensity and curvature priors. Proceedings of IEEE Workshop on Mathematical Methods in Biomedical Image Analysis 2000, 4-11.
36. Levinshtein A., Stere A., Kutulakos K., Fleet D., Dickinson S., Siddiqi K. TurboPixels: fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 2009, 31(12):2290-2297.
37. Licausi F., Kosmacz M., Weits D.A., Giuntoli B., Giorgi F.M., Voesenek L.A.C.J., Perata P., van Dongen J.T. Oxygen sensing in plants is mediated by an N-end rule pathway for protein destabilization. Nature 2011, 479(7373):419-422.
38. Mansilla L.A.C., Miranda P.A.V. Image segmentation by oriented image foresting transform: handling ties and colored images. Proceedings of the 18th International Conference on Digital Signal Processing (DSP) 2013.
39. Mardis E.R. Next-generation DNA sequencing methods. Annual Review of Genomics and Human Genetics 2008, 9(1):387-402.
40. Micol J.L. Leaf development: time to turn over a new leaf?. Current Opinion in Plant Biology 2009, 12(1):9-16.
41. Ning J., Zhang L., Zhang D., Wu C. Interactive image segmentation by maximal similarity based region merging. Pattern Recognition 2010, 43(2):445-456.
42. O'Malley R.C., Ecker J.R. Linking genotype to phenotype using the Arabidopsis unimutant collection. The Plant Journal 2010, 61(6):928-940.
43. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 1979, 9(1):62-66.
44. Peleman J.D., van der Voort J.R.R. Breeding by design. Trends in Plant Science 2003, 8(7):330-334.
45. Pereyra-Irujo G.A., Gasco E.D., Peirone L.S., Aguirrezábal L.A.N. GlyPh: a low-cost platform for phenotyping plant growth and water use. Functional Plant Biology 2012, 39(11):905-913.
46. Price C.A., Symonova O., Mileyko Y., Hilley T., Weitz J.S. LEAF GUI: segmenting and analyzing the structure of leaf veins and areoles. Plant Physiology 2010, 155(1):236-245.
47. Spalding E.P., Miller N.D. Image analysis is driving a renaissance in growth measurement. Current Opinion in Plant Biology 2013, 16(1):100-104.
48. Tang X., Liu M., Zhao H., Tao W. Leaf extraction from complicated background. 2nd International Congress on Image and Signal Processing (CISP), IEEE 2009, 1-5.
49. Titterington D.M., Smith A.F.M., Makov U.E. Statistical Analysis of Finite Mixture Distributions 1985, Wiley.
50. Tsaftaris S.A., Noutsos C. Plant phenotyping with low cost digital cameras and image analytics. Information Technologies in Environmental Engineering, Environmental Science and Engineering 2009, 238-251. Springer, Berlin Heidelberg.
51. Tuceryan M., Jain A.K. Texture analysis, Ch. Texture Analysis 1993, 235-276.
52. van der Heijden G., Song Y., Horgan G., Polder G., Dieleman A., Bink M., Palloix A., van Eeuwijk F., Glasbey C. SPICY: towards automated phenotyping of large pepper plants in the greenhouse. Functional Plant Biology 2012, 39(11):870-877.
53. Walter A., Scharr H., Gilmer F., Zierer R., Nagel K.A., Ernst M., Wiese A., Virnich O., Christ M.M., Uhlig B., Jünger S., Schurr U. Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytologist 2007, 174(2):447-455.
54. Walter A., Studer B., Kölliker R. Advanced phenotyping offers opportunities for improved breeding of forage and turf species. Annals of Botany 2012, 110(6):1271-1279.
55. Wang L., Uilecan I.V., Assadi A.H., Kozmik C.A., Spalding E.P. HYPOTrace: image analysis software for measuring hypocotyl growth and shape demonstrated on Arabidopsis seedlings undergoing photomorphogenesis. Plant Physiology 2009, 149(4):1632-1637.
56. Wang X.-F., Huang D.-S., Xu H. An efficient local Chan-Vese model for image segmentation. Pattern Recognition 2010, 43(3):603-618.
57. White J.W., Andrade-Sanchez P., Gore M.A., Bronson K.F., Coffelt T.A., Conley M.M., Feldmann K.A., French A.N., Heun J.T., Hunsaker D.J., Jenks M.A., Kimball B.A., Roth R.L., Strand R.J., Thorp K.R., Wall G.W., Wang G. Field-based phenomics for plant genetics research. Field Crops Research 2012, 133:101-112.
58. Yuille A.L., Cohen D.S., Hallinan P.W. Feature extraction from faces using deformable templates. Conference on Computer Vision and Pattern Recognition (CVPR), IEEE 1989, 104-109.
59. Zhang K., Zhang L., Song H., Zhou W. Active contours with selective local or global segmentation: a new formulation and level set method. Image and Vision Computing 2010, 28(4):668-676.
60. Zhang L., Gu Z., Li H. SDSP: a novel saliency detection method by combining simple priors. International Conference on Image Processing (ICIP) 2013.
61. Zhao H.-K., Chan T., Merriman B., Osher S. A variational level set approach to multiphase motion. Journal of Computational Physics 1996, 127(1):179-195.