Shape descriptors to characterize the shoot of entire plant from multiple side views of a motorized depth sensor

Machine Vision and Applications

Volumn 27, Issue 4, 2016, Pages 447-461

  Chéné, Yann ^a   Rousseau, David ^b   Belin, Étienne ^a   Garbez, Morgan ^c   Galopin, Gilles ^c   Chapeau Blondeau, François ^a  

^a UNIVERSITÉ D'ANGERS   (France)

^b UNIVERSITÉ DE LYON   (France)

^c UNIVERSITÉ D'ANGERS   (France)

Author keywords

Depth image; Fractal; Kinect; Phenotyping; Plant shoots; Shape descriptor

Indexed keywords

FRACTALS; IMAGE PROCESSING;

DEPTH IMAGE; KINECT; PHENOTYPING; PLANT SHOOTS; SHAPE DESCRIPTORS;

COSTS;

EID: 84964374694     PISSN: 09328092     EISSN: 14321769     Source Type: Journal    
DOI: 10.1007/s00138-016-0762-x     Document Type: Article

References (36)

1. Fiorani, F., Rascher, U., Jahnke, S.: Imaging plants dynamics in heterogenic environments. Curr. Opin. Biotechnol. 23, 227–235 (2012)
2. Subramanian, R., Spalding, E.P., Ferrier, N.J.: A high throughput robot system for machine vision based plant phenotype studies. Mach. Vis. Appl. 24, 619–636 (2013)
3. Hiremath, S., Tolpekin, V.A., van der Heijden, G., Stein, A.: Segmentation of Rumex obtusifolius using Gaussian Markov random fields. Mach. Vis. Appl. 24, 845–845 (2013)
4. Zeng, G., Birchfield, S.T., Wells, C.E.: Rapid automated detection of roots in minirhizotron images. Mach. Vis. Appl. 21, 309–317 (2013)
5. 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. Funct. Plant Biol. 39, 870–877 (2012)
6. Bellasio, C., Olejníčková, J., Tesař, R., Šebela, D., Nedbal, L.: Computer reconstruction of plant growth and chlorophyll fluorescence emission in three spatial dimensions. Sensors 12, 1052–1071 (2012)
7. Azzary, G., Goulden, M.L., Rusu, R.B.: Rapid characterization of vegetation structure with a Microsoft Kinect sensor. Sensors 13, 2384–2398 (2013)
8. Biskup, B., Scharr, H., Schurr, U., Rascher, U.: A stereo imaging system for measuring structural parameters of plant canopies. Plant Cell Environ. 10, 1299–1308 (2007)
9. Omasa, K., Hosoi, F., Konishi, A.: 3D lidar imaging for detecting and understanding plant responses and canopy structure. J. Exp. Bot. 158, 881–898 (2007)
10. Bucksch, A., Fleck, S.: Automated detection of branch dimensions in woody skeletons of fruit tree canopies. Photogramm. Eng. Remote Sens. 77, 229–240 (2011)
11. Klose, R., Penlington, J., Ruckelshausen, A.: Usability study of 3D time-of-flight cameras for automatic plant phenotyping. Bornimer Agrartechnische Berichte 69, 93–105 (2009)
12. Kraft, M., Saloma De Freitag, N., Munack, A.: Test of a 3D time of flight camera for shape measurements of plants. In: CIGR Workshop on Image Analysis in Agriculture, Budapest, Hungary, 26–27 Aug 2010 (2010)
13. 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. Comput. Electron. Agric. 82, 122–127 (2012)
14. Chéné, Y., Belin, E., Chapeau-Blondeau, F, Boureau, T., Caffier, V., Rousseau, D.: Anatomo-functional bimodality imaging for plant phenotyping: An insight through depth imaging coupled to thermal imaging. Chap. 9, in Dutta Gupta, S., Ibaraki, Y., (eds.) Plant Image Analysis: Fundamentals and Applications. CRC Press, Boca Raton (2015)
15. Chéné, Y., Belin, E., Rousseau, D., Chapeau-Blondeau, F.: Multiscale analysis of depth images from natural scenes: scaling in the depth of the woods. Chaos Solitons Fractals 54, 135–149 (2013)
16. Golbach, F., Kootstra, G., Damjanovic, S., Otten, G., van de Zedde, R.: Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping. Mach. Vis. Appl. 1–18 (2015)
17. Nguyen, T.T., Slaughter, D.C., Max, N., Maloof, J.N., Sinha, N.: Structured light-based 3D reconstruction system for plants. Sensors 15(8), 18587–18612 (2015)
18. Sansoni, G., Trebeschi, M., Docchio, F.: State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation. Sensors 9, 568–601 (2009)
19. Garbez, M., Chéné, Y., Belin, É., Sigogne, M., Labatte, J.L., Hunault, G., Rousseau, D., Galopin, G.: Predicting sensorial attribute scores of ornamental plants assessed in 3D through rotation on video by image analysis: a study on the morphology of virtual rose bushes. Comput. Electron. Agric. 121, 331–346 (2016)
20. Khoshelham, K., Elberink, S.O.: Accuracy and resolution of Kinect depth data for indoor mapping applications. Sensors 12, 1437–1454 (2012)
21. http://labjack.com/u3
22. http://qt.digia.com/
23. http://www.openni.org/
24. Schnabel, R., Wahl, R., Klein, R.: Efficient RANSAC for point cloud shape detection. Comput. Graph. Forum 26, 214–226 (2007). (Blackwell Publishing Ltd)
25. Chauveau, J., Rousseau, D., Richard, P., Chapeau-Blondeau, F.: Fractal structure in the color distribution of natural images. Chaos Solitons Fractals 42, 472–482 (2009)
26. Chauveau, J., Rousseau, D., Richard, P., Chapeau-Blondeau, F.: Multifractal analysis of three-dimensional histogram from color images. Chaos Solitons Fractals 43, 57–67 (2010)
27. Chauveau, J., Rousseau, D., Chapeau-Blondeau, F.: Fractal capacity dimension of three-dimensional histogram from color images. Multidimens. Syst. Signal Process. 21, 197–211 (2010)
28. Venus head point cloud. http://www.dirdim.com (2014)
29. Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11, 23–27 (1975)
30. Da Silva, D., Boudon, F., Godin, C., Sinoquet, H.: Multiscale framework for modeling and analyzing light interception by trees. Multiscale Model. Simul. 7, 910–933 (2008)
31. Paulus, S., Dupuis, J., Mahlein, A.K., Kuhlmann, H.: Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinform. 14, 238–249 (2013)
32. Paulus, S., Schumann, H., Kuhlmann, H., Léon, J.: High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants. Biosyst. Eng. 121, 1–11 (2014)
33. Paulus, S., Dupuis, J., Riedel, S., Kuhlmann, H.: Automated analysis of barley organs using 3D laser scanning: an approach for high throughput phenotyping. Sensors 14, 12670–12686 (2014)
34. http://lisabiblio.univ-angers.fr/PHENOTIC/Shape_descriptors.zip
35. Boumaza, R., Huché-Thélier, L., Demotes-Mainard, S., Coz, E.L., Leduc, N., Pelleschi-Travier, S., Qannari, E.M., Sakr, S., Santagostini, P., Symoneaux, R., Gurin, V.: Sensory profiles and preference analysis in ornamental horticulture: the case of the rosebush. Food Qual. Prefer. 21, 987–997 (2010)
36. Garbez, M., Galopin, G., Sigogne, M., Favre, P., Demotes-Mainard, S., Symoneaux, R.: Assessing the visual aspect of rotating virtual rose bushes by a labeled sorting task. Food Qual. Prefer. 40, 287–295 (2015)