Towards automated large-scale 3D phenotyping of vineyards under field conditions

Sensors (Switzerland)

Volumn 16, Issue 12, 2016, Pages

  Rose, Johann Christian ^a   Kicherer, Anna ^b   Wieland, Markus ^a   Klingbeil, Lasse ^a   Töpfer, Reinhard ^b   Kuhlmann, Heiner ^a  

^a UNIVERSITY OF BONN   (Germany)

^b INSTITUTE FOR EPIDEMIOLOGY AND PATHOGEN DIAGNOSTICS   (Germany)

Author keywords

3D point cloud; Berry diameter; Classification; Field phenotyping; Multi view stereo; Number of berries; Number of grape bunches; Viticulture

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPENSATION (PERSONNEL); CONTROL SYSTEM SYNTHESIS; DATA ACQUISITION; DIGITAL IMAGE STORAGE; DIGITAL STORAGE; FRUITS; IMAGE PROCESSING; SEMANTICS; WAGES;

3D POINT CLOUD; BERRY DIAMETER; MULTI-VIEW STEREO; NUMBER OF BERRIES; NUMBER OF GRAPE BUNCHES; PHENOTYPING; VITICULTURE;

STEREO IMAGE PROCESSING;

ALGORITHM; ANATOMY AND HISTOLOGY; AUTOMATION; FRUIT; ORGAN SIZE; PHENOTYPE; ROBOTICS; SHOOT; THREE DIMENSIONAL IMAGING; VITIS;

ALGORITHMS; AUTOMATION; FRUIT; IMAGING, THREE-DIMENSIONAL; ORGAN SIZE; PHENOTYPE; PLANT SHOOTS; ROBOTICS; VITIS;

EID: 85006741771     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s16122136     Document Type: Article

References (44)

1. Furbank, R.T.; Tester, M. Phenomics–technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 2011, 16, 635-644.
2. Fanizza, G.; Lamaj, F.; Costantini, L.; Chaabane, R.; Grando, M. QTL analysis for fruit yield components in table grapes (Vitis vinifera). Theor. Appl. Genet. 2005, 111, 658-664.
3. Nuske, S.; Achar, S.; Bates, T.; Narasimhan, S.; Singh, S. Yield estimation in vineyards by visual grape detection. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, San Francisco, CA, USA, 25-30 September 2011; pp. 2352-2358.
4. Matthews, M.; Nuzzo, V. Berry size and yield paradigms on grapes and wines quality. Acta Hortic. 2007, 754, 423-436.
5. Tardaguila, J.; de Toda, F.M. Assessment of ’Tempranillo’grapes quality in the vineyard by vitur score-sheet. J. Int. Sci. Vigne Vin 2008, 42, 59-65.
6. Dunn, G.M.; Martin, S.R. Yield prediction from digital image analysis: A technique with potential for vineyard assessments prior to harvest. Aust. J. Grape Wine Res. 2004, 10, 196-198.
7. Font, D.; Pallejà, T.; Tresanchez, M.; Teixidó, M.; Martinez, D.; Moreno, J.; Palacín, J. Counting red grapes in vineyards by detecting specular spherical reflection peaks in RGB images obtained at night with artificial illumination. Comput. Electron. Agric. 2014, 108, 105-111.
8. Liu, S.; Marden, S.; Whitty, M. Towards automated yield estimation in viticulture. In Proceedings of the Australasian Conference on Robotics and Automation, Sydney, Australia, 2-4 December 2013; Volume 24.
9. Diago, M.P.; Correa, C.; Millán, B.; Barreiro, P.; Valero, C.; Tardaguila, J. Grapevine yield and leaf area estimation using supervised classification methodology on RGB images taken under field conditions. Sensors 2012, 12, 16988-17006.
10. Roscher, R.; Herzog, K.; Kunkel, A.; Kicherer, A.; Töpfer, R.; Förstner, W. Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields. Comput. Electron. Agric. 2014, 100, 148-158.
11. Kicherer, A.; Herzog, K.; Pflanz, M.; Wieland, M.; Rüger, P.; Kecke, S.; Kuhlmann, H.; Töpfer, R. An automated field phenotyping pipeline for application in grapevine research. Sensors 2015, 15, 4823-4836.
12. Nuske, S.; Wilshusen, K.; Achar, S.; Yoder, L.; Narasimhan, S.; Singh, S. Automated visual yield estimation in vineyards. J. Field Robot. 2014, 31, 837-860.
13. Dornbusch, T.; Lorrain, S.; Kuznetsov, D.; Fortier, A.; Liechti, R.; Xenarios, I.; Fankhauser, C. Measuring the diurnal pattern of leaf hyponasty and growth in Arabidopsis a novel phenotyping approach using laser scanning. Funct. Plant Biol. 2012, 39, 860-869.
14. Lumme, J.; Karjalainen, M.; Kaartinen, H.; Kukko, A.; Hyyppä, J.; Hyyppä, H.; Jaakkola, A.; Kleemola, J. Terrestrial laser scanning of agricultural crops. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2008, 37, 563-566.
15. Kraft, M.; Regina, S.; Freitas, D.; Munack, A. Test of a 3D time of flight camera for shape measurements of plants. In Proceedings of the CIGR Workshop on Image Analysis in Agriculture, Budapest, Hungary, 26-27 August 2010; pp. 26-27.
16. Kizma, W.; Foix, S.; Alenyà, G. Plant leaf analalysis using Time of Flight camera under sun, shadow and room conditions. In Proceedings of the IEEE International Symposium on Robotic and Sensors Environments, Magdeburg, Germany, 16-18 November 2012.
17. Paulus, S.; Behmann, J.; Mahlein, A.K.; Plümer, L.; Kuhlmann, H. Low-cost 3D systems: Suitable tools for plant phenotyping. Sensors 2014, 14, 3001-3018.
18. Rose, J.C.; Paulus, S.; Kuhlmann, H. Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level. Sensors 2015, 15, 9651-9665.
19. Jay, S.; Rabatel, G.; Gorrettta, N. In-Field Crop Row Stereo-Reconsruction for Plant Phenotyping. In Proceedings of the Second International Conference on Robotics and Associated High-Technologies and Equipment for Agriculture and forestry (RHEA), Bergamo, Italy, 20-23 October 2014; pp. 21-23.
20. Herrero-Huerta, M.; González-Aguilera, D.; Rodriguez-Gonzalvez, P.; Hernández-López, D. Vineyard yield estimation by automatic 3D bunch modelling in field conditions. Comput. Electron. Agric. 2015, 110, 17-26.
21. Remondino, F.; Spera, M.G.; Nocerino, E.; Menna, F.; Nex, F. State of the art in high density image matching. Photogramm. Rec. 2014, 29, 144-166.
22. Weinmann, M.; Schwartz, C.; Ruiters, R.; Klein, R. A multi-camera, multi-projector super-resolution framework for structured light. In Proceedings of the 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, Hangzhou, China, 16-19 May 2011; pp. 397-404.
23. Dey, D.; Mummert, L.; Sukthankar, R. Classification of plant structures from uncalibrated image sequences. In Proceedings of the IEEE Workshop on Applications of Computer Vision, Breckenridge, CO, USA, 9-11 Janurary 2012; pp. 329-336.
24. Bora, D.J.; Gupta, A.K.; Khan, F.A. Comparing the Performance of L*A*B* and HSV Color Spaces with Respect to Color Image Segmentation. Int. J. Emerg. Technol. Adv. Eng. 2015, 5, 192-203.
25. Font, D.; Tresanchez, M.; Martínez, D.; Moreno, J.; Clotet, E.; Palacín, J. Vineyard Yield Estimation Based on the Analysis of High Resolution Images Obtained with Artificial Illumination at Night. Sensors 2015, 15, 8284-8301.
26. Dhondt, S.; Wuyts, N.; Inzé, D. Cell to whole-plant phenotyping: The best is yet to come. Trends Plant Sci. 2013, 18, 433-444.
27. Danielgm. Cloud Compare. 2016. Available online: http://www.danielgm.net/cc/ (accessed on 12 August 2016).
28. Alexa, M.; Behr, J.; Cohen-or, D.; Fleishman, S.; Levin, D.; Silva, C.T. Computing and Rendering Point Set Surfaces. IEEE Trans. Vis. Comput. Graph. 2003, 9, 3-15.
29. Paulus, S.; Dupuis, J.; Mahlein, A.K.; Kuhlmann, H. Surface feature based classiffication of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinform. 2013, 14, 238.
30. Rusu, R.B.; Marton, Z.C.; Blodow, N.; Dolha, M.; Beetz, M. Towards 3D Point cloud based object maps for household environments. Robot. Auton. Syst. 2008, 56, 927-941.
31. Sural, S.; Qian, G.; Pramanik, S. Segmentation and histogram generation using the HSV color space for image retrieval. In Proceedings of the International Conference on Image Processing, Rochester, NY, USA, 22-25 September 2002; Volume 2, pp. II-589-II-592.
32. Roscher, R.; Förstner, W.; Waske, B. I2VM: Incremental import vector machines. Image Vis. Comput. 2012, 30, 263-278.
33. Zhu, J.; Hastie, T. Kernel Logistic Regression and the Import Vector Machine. J. Comput. Graph. Stat. 2005, 14, 185-205.
34. Kotsiantis, S.B. Supervised machine learning: A review of classification techniques. Informatica 2007, 31, 249-268.
35. Braun, A.C.; Weidner, U.; Hinz, S. Support vector machines, import vector machines and relevance vector machines for hyperspectral classification—A comparison. In Proceedings of the 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, Portugal, 6-9 June 2011.
36. Vapnik, V. The Nature of Statistical Learning Theory; Springer: New York, NY, USA, 2013.
37. Platt, J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 1999, 10, 61-74.
38. Delong, A. GCO. 2016. Available online: http://www.psi.toronto.edu/~andrew/ (accessed on 12 August 2016).
39. Delong, A.; Osokin, A.; Isack, H.N.; Boykov, Y. Fast Approximate Energy Minimization with Label Cost. Int. J. Comput. Vis. 2012, 96, 1-27.
40. Kolmogorov, V.; Zabin, R. What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26, 147-159.
41. Kicherer, A.; Rose, J.C. Riesling Grapevine Canopy Images for 3D Reconstruction. Julius Kühn-Institut. Open Agrar Repository. 2016. Available online: https://openagrar.bmel-forschung.de/receive/openagrar_ mods_00022533 (accessed on 13 Dcember 2016).
42. Minervini, M.; Scharr, H.; Tsaftaris, S.A. Image Analysis: The New Bottleneck in Plant Phenotyping. IEEE Signal Process. Mag. 2015, 32, 126-131.
43. Busemeyer, L.; Mentrup, D.; Möller, K.; Wunder, E.; Alheit, K.; Hahn, V.; Maurer, H.P.; Reif, J.C.; Würschum, T.; Müller, J.; et al. Breedvision—A multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 2013, 13, 2830-2847.
44. Araus, J.L.; Cairns, J.E. Field high-throughput phenotyping: The new crop breeding frontier. Trends Plant Sci. 2014, 19, 52-61.