An automated field phenotyping pipeline for application in grapevine research

Sensors (Switzerland)

Volumn 15, Issue 3, 2015, Pages 4823-4836

  Kicherer, Anna ^a   Herzog, Katja ^a   Pflanz, Michael ^a,b   Wieland, Markus ^c   Rüger, Philipp ^d   Kecke, Steffen ^a   Kuhlmann, Heiner ^c   Töpfer, Reinhard ^a  

^a INSTITUTE FOR EPIDEMIOLOGY AND PATHOGEN DIAGNOSTICS   (Germany)

^b LEIBNIZ INSTITUTE FOR AGRICULTURAL ENGINEERING AND BIOECONOMY ATB   (Germany)

^c UNIVERSITY OF BONN   (Germany)

^d HOCHSCHULE GEISENHEIM UNIVERSITY   (Germany)

Author keywords

Geoinformation; Grapevine breeding; High throughput analysis; Image acquisition; Plant phenotyping; Robot; Vitis vinifera

Indexed keywords

AUTOMATION; FRUITS; IMAGE ANALYSIS; INFORMATION MANAGEMENT; PIPELINES; ROBOTS; THROUGHPUT;

GEO-INFORMATION; GRAPEVINE BREEDINGS; HIGH-THROUGHPUT ANALYSIS; PLANT PHENOTYPING; VITIS VINIFERA;

IMAGE ACQUISITION;

EID: 84928162620     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s150304823     Document Type: Article

References (36)

1. Thomas, M.R.; Scott, N.S. Microsatellite repeats in grapevine reveal DNA polymorphisms when analysed as sequence-tagged sites (STSS). Theoret. Appl. Genetics 1993, 86, 985–990.
2. Eibach, R.; Zyprian, E.; Welter, L.; Töpfer, R. The use of molecular markers for pyramiding resistance genes in grapevine breeding. Vitis 2007, 46, 120–124.
3. Barba, P.; Cadle-Davidson, L.; Harriman, J.; Glaubitz, J.; Brooks, S.; Hyma, K.; Reisch, B. Grapevine powdery mildew resistance and susceptibility loci identified on a high-resolution SNP map. Theoret. Appl. Genet. 2014, 127, 73–84.
4. Granier, C.; Aguirrezabal, L.; Chenu, K.; Cookson, S.J.; Dauzat, M.; Hamard, P.; Thioux, J.J.; Rolland, G.; Bouchier-Combaud, S.; Lebaudy, A., et al. 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 Phytol. 2006, 169, 623–635.
5. Walter, A.; Scharr, H.; Gilmer, F.; Zierer, R.; Nagel, K.; Ernst, M.; Wiese, A.; Virnich, O.; Christ, M.; Uhlig, B., et al. 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 Phytol. 2007, 174, 447–455.
6. Reuzeau, C.; Pen, J.; Frankard, V.; de Wolf, J.; Peerbolte, R.; Broekaert, W.; van Camp, W. Traitmilltm: A discovery engine for identifying yield-enhancement genes in cereals. Mol. Plant Breed. 2005, 5, 753–759.
7. Clark, R.T.; MacCurdy, R.B.; Jung, J.K.; Shaff, J.E.; McCouch, S.R.; Aneshansley, D.J.; Kochian, L.V. Three-dimensional root phenotyping with a novel imaging and software platform. Plant Physiol. 2011, 156, 455–465.
8. Hartmann, A.; Czauderna, T.; Hoffmann, R.; Stein, N.; Schreiber, F. Htpheno: An image analysis pipeline for high-throughput plant phenotyping. BMC Bioinf. 2011, 12, 148–156.
9. Bellin, D.; Peressotti, E.; Merdinoglu, D.; Wiedemann-Merdinoglu, S.; Adam-Blondon, A.F.; Cipriani, G.; Morgante, M.; Testolin, R.; di Gaspero, G. Resistance to plasmopara viticola in grapevine “bianca” is controlled by a major dominant gene causing localised necrosis at the infection site. Theor. Appl. Genet. 2009, 120, 163–176.
10. Mazzetto, F.; Calcante, A.; Mena, A.; Vercesi, A. Integration of optical and analogue sensors for monitoring canopy health and vigour in precision viticulture. Precis. Agric. 2010, 11, 636–649.
11. Llorens, J.; Gil, E.; Llop, J.; Queralto, M. Georeferenced lidar 3D vine plantation map generation. Sensors 2011, 11, 6237–6256.
12. Braun, T.; Koch, H.; Strub, O.; Zolynski, G.; Berns, K. Improving pesticide spray application in vineyards by automated analysis of the foliage distribution pattern in the leaf wall. In Commercial Vehicle Technology 2010, Proceedings of the 1st Commercial Vehicle Technology Symposium (CVT 2010), Kaiserslautern, Germany, 16–18 March 2010; Berns;, K., Schindler, C., Dreßler, K., Jörg, B., Kalmar, R., Hirth, J., Eds.; pp. 539–548.
13. Berenstein, R.; Shahar, O.B.; Shapiro, A.; Edan, Y. Grape clusters and foliage detection algorithms for autonomous selective vineyard sprayer. Intell. Serv. Robot. 2010, 3, 233–243.
14. Corbett-Davies; Sam Botterill; Tom Green, R.; Saxton, V. An expert system for automatically pruning vines. In Proceedings of the 27th Conference on Image and Vision Computing New Zealand, Dunedin, New Zealand, 26–28 November 2012; pp. 55–60.
15. Nuske, S.; Achar, S.; Bates, T.; Narasimhan, S.; Singh, S. Yield estimation in vineyards by visual grape detection. In Proceedings of the International Conference on Itelligent Robots and Systems, San Francisco, CA, USA, 25–30 September 2011; IEEE: San Francisco, CA, USA, 2011.
16. 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.
17. 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; UNSW: Sydney, Australia, 2013.
18. 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.
19. Diago, M.P.; Sanz-Garcia, A.; Millan, B.; Blasco, J.; Tardaguila, J. Assessment of flower number per inflorescence in grapevine by image analysis under field conditions. J. Sci. Food Agric. 2014, 94, 1981–1987.
20. Fuentes, S.; Bei, R.; Pech, J.; Tyerman, S. Computational water stress indices obtained from thermal image analysis of grapevine canopies. Irrig. Sci. 2012, 30, 523–536.
21. Fuentes, S.; Poblete-Echeverría, C.; Ortega-Farias, S.; Tyerman, S.; de Bei, R. Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methods. Aust. J. Grape Wine Res. 2014, 20, 465–473.
22. Möller, M.; Alchanatis, V.; Cohen, Y.; Meron, M.; Tsipris, J.; Naor, A.; Ostrovsky, V.; Sprintsin, M.; Cohen, S. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J. Exp. Bot. 2007, 58, 827–838.
23. Calcante, A.; Mena, A.; Mazzetto, F. Evaluation of “ground sensing” optical sensors for diagnosis of plasmopara viticola on vines. Span. J. Agric. Res. 2012, 10, 619–630.
24. Mazzetto, F.; Calcante, A.; Mena, A.; Sacco, P. Test of ground-sensing devices for monitoring canopy vigour and downy mildew presence in vineyards: First qualitative traits. J. Agric. Eng. Riv. Ing. Agrar. 2011, 42, 1–9.
25. Grocholsky, B.; Nuske, S.; Aasted, M.; Achar, S.; Bates, T. A camera and laser system for automatic vine balance assessment. In Proceedings of the ASABE Annual International Meeting, Louisville, Kentucky, 7–10 August 2011; ASABE: Louisville, Kentucky, 2011.
26. Ruckelshausen, A.; Biber, P.; Dorna, M.; Gremmes, H.; Klose, R.; Linz, A.; Rahe, F.; Resch, R.; Thiel, M.; Trautz, D., et al. Bonirob—An autonomous field robot platform for individual plant phenotyping. In Proceedings of the 7th European Conference on Precision Agriculture, Wageningen, The Netherlands, 6–8 July 2009; Academic Publishers:Wageningen, The Netherlands, 2009; pp. 841–847.
27. Busemeyer, L.; Mentrup, D.; Moller, K.; Wunder, E.; Alheit, K.; Hahn, V.; Maurer, H.P.; Reif, J.C.; Wurschum, T.; Muller, J., et al. Breedvision—A multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 2013, 13, 2830–2847.
28. Longo, D.; Pennissi, A.; Bonsignore, R.; Muscato, G.; Schillaci, G. A multifunctional tracked vehicle able to operate in vineyards using gps and laser range-finder technology. In Proceedings of the International Conference Ragusa SHWA2010, Ragusa Ibla Campus, Italy, 16–18 September 2010; pp. 487–492.
29. Longo, D.; Muscato, G.; Caruso, L.; Conti, A.; Schillaci, G. Design of a remotely operable sprayer for precision farming application. In Proceedings of the International Conference RAGUSA SHWA, Ragusa, Italy, 3–6 September 2012.
30. Lorenz, D.H.; Eichhorn, K.W.; Bleiholder, H.; Klose, R.; Meier, U.; Weber, E. Growth stages of the grapevine: Phenological growth stages of the grapevine (Vitis vinifera L. ssp. Vinifera)—Codes and descriptions according to the extended BBCH scale. Aust. J. Grape Wine Res. 1995, 1, 100–103.
31. Anonymous. 2nd Edition of the Oiv Descriptor List for Grape Varieties and Vitis Species. Available online: http://www.oiv.int/oiv/info/enplubicationoiv#grape (accessed on 6 January 2015).
32. Herzog, K.; Roscher, R.; Wieland, M.; Kicherer, A.; Läbe, T.; Förstner, W.; Kuhlmann, H.; Töpfer, R. Initial steps for high-throughput phenotyping in vineyards. Vitis 2014, 53, 1–8.
33. Schwarz, H.P.; Rüger, P.; Kicherer, A.; Töpfer, R. Development of an autonomous driven robotic platform used for ht-phenotyping in viticulture. Mech. Eng. Lett. Szent István Univ. 2013, 10, 153–160.
34. 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.
35. Doligez, A.; Bouquet, A.; Danglot, Y.; Lahogue, F.; Riaz, S.; Meredith, C.P.; Edwards, K.J.; This, P. Genetic mapping of grapevine (Vitis vinifera L.) applied to the detection of QTLs for seedlessness and berry weight. Theor. Appl. Genet. 2002, 105, 780–795.
36. Doligez, A.; Audiot, E.; Baumes, R.; This, P. QTLs for muscat flavor and monoterpenic odorant content in grapevine (Vitis vinifera L.). Mol. Breed. 2006, 18, 109–125.