An image analysis pipeline for automated classification of imaging light conditions and for quantification of wheat canopy cover time series in field phenotyping

Plant Methods

Volumn 13, Issue 1, 2017, Pages

  Yu, Kang ^a   Kirchgessner, Norbert ^a   Grieder, Christoph ^b   Walter, Achim ^a   Hund, Andreas ^a  

^a ETH ZURICH   (Switzerland)

^b AGROSCOPE RECKENHOLZ TÄNIKON RESEARCH STATION ART   (Switzerland)

Author keywords

Canopy cover; Color vegetation index; High throughput field phenotyping; Image analysis; Image segmentation; Light contrast; Machine learning

Indexed keywords

EID: 85016112131     PISSN: None     EISSN: 17464811     Source Type: Journal    
DOI: 10.1186/s13007-017-0168-4     Document Type: Article

References (30)

1. Furbank RT, Tester M. Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 2011;16:635-44. doi: 10.1016/j.tplants.2011.09.005.
2. White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, et al. Field-based phenomics for plant genetics research. Field Crops Res. 2012;133:101-12. doi: 10.1016/j.fcr.2012.04.003.
3. Scharr H, Dee H, French AP, Tsaftaris SA. Special issue on computer vision and image analysis in plant phenotyping. Mach Vis Appl. 2016;27:607-9. doi: 10.1007/s00138-016-0787-1.
4. Kelly D, Vatsa A, Mayham W, Ngô L, Thompson A, Kazic T. An opinion on imaging challenges in phenotyping field crops. Mach Vis Appl. 2016;27:681-94. doi: 10.1007/s00138-015-0728-4.
5. Duan T, Zheng B, Guo W, Ninomiya S, Guo Y, Chapman SC. Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV. Funct Plant Biol. 2017;44:169-83. doi: 10.1071/FP16123.
6. Dee H, French A. From image processing to computer vision: plant imaging grows up. Funct Plant Biol. 2015;42:3-5.
7. Virlet N, Sabermanesh K, Sadeghi-Tehran P, Hawkesford M. Field Scanalyser: an automated robotic field phenotyping platform for detailed crop monitoring. Funct Plant Biol. 2016.
8. Kirchgessner N, Liebisch F, Yu K, Pfeifer J, Friedli M, Hund A, et al. The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. Funct Plant Biol. 2017;44:154-68. doi: 10.1071/FP16165.
9. Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R. Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy. 2014;4:349-79. doi: 10.3390/agronomy4030349.
10. Minervini M, Scharr H, Tsaftaris SA. Image analysis: the new bottleneck in plant phenotyping. IEEE Signal Process Mag. 2015;32:126-31. doi: 10.1109/MSP.2015.2405111.
11. Rahaman MM, Chen D, Gillani Z, Klukas C, Chen M. Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Front Plant Sci. 2015;6:619. doi: 10.3389/fpls.2015.00619.
12. Walter A, Liebisch F, Hund A. Plant phenotyping: from bean weighing to image analysis. Plant Methods. 2015;11:14. doi: 10.1186/s13007-015-0056-8.
13. Li L, Zhang Q, Huang D. A review of imaging techniques for plant phenotyping. Sensors. 2014;14:20078-111. doi: 10.3390/s141120078.
14. Sankaran S, Khot LR, Carter AH. Field-based crop phenotyping: Multispectral aerial imaging for evaluation of winter wheat emergence and spring stand. Comput Electron Agric. 2015;118:372-9. doi: 10.1016/j.compag.2015.09.001.
15. Lootens P, Ruttink T, Rohde A, Combes D, Barre P, Roldán-Ruiz I. High-throughput phenotyping of lateral expansion and regrowth of spaced Lolium perenne plants using on-field image analysis. Plant Methods. 2016;12:32. doi: 10.1186/s13007-016-0132-8.
16. Meyer GE, Neto JC. Verification of color vegetation indices for automated crop imaging applications. Comput Electron Agric. 2008;63:282-93. doi: 10.1016/j.compag.2008.03.009.
17. Grieder C, Hund A, Walter A. Image based phenotyping during winter: a powerful tool to assess wheat genetic variation in growth response to temperature. Funct Plant Biol. 2015;42:387-96.
18. Liebisch F, Kirchgessner N, Schneider D, Walter A, Hund A. Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods. 2015;11:9. doi: 10.1186/s13007-015-0048-8.
19. Wang Y, Cao Z, Bai X, Yu Z, Li Y. An automatic detection method to the field wheat based on image processing. Proc SPIE. 2013. 89180F. doi: 10.1117/12.2031139.
20. Singh A, Ganapathysubramanian B, Singh AK, Sarkar S. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci. 2016;21:110-24. doi: 10.1016/j.tplants.2015.10.015.
21. Navarro PJ, Pérez F, Weiss J, Egea-Cortines M. Machine learning and computer vision system for phenotype data acquisition and analysis in plants. Sensors. 2016;16:641. doi: 10.3390/s16050641.
22. Guo W, Rage UK, Ninomiya S. Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model. Comput Electron Agric. 2013;96:58-66. doi: 10.1016/j.compag.2013.04.010.
23. Otsu Nobuyuki. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9:62-6. doi: 10.1109/TSMC.1979.4310076.
24. LDP LLC. Remote sensing NDVI. http://www.maxmax.com/maincamerapage/remote-sensing. Accessed 8 Jun 2016.
25. McLAREN K. The development of the CIE 1976 (L*a*b*) uniform colour space and colour-difference formula. J Soc Dye Colour. 1976;92:338-41. doi: 10.1111/j.1478-4408.1976.tb03301.x.
26. Lim JS. Two-dimensional signal and image processing. 1st ed. Englewood Cliffs: Prentice Hall PTR; 1989.
27. Core R. Team, R: a language and environment for statistical computing. Vienna: R Foundation for Statistical, Computing; 2015.
28. Walter A, Scharr H, Gilmer F, Zierer R, Nagel KA, Ernst M, 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;174:447-55. doi: 10.1111/j.1469-8137.2007.02002.x.
29. Scharr H, Minervini M, French AP, Klukas C, Kramer DM, Liu X, et al. Leaf segmentation in plant phenotyping: a collation study. Mach Vis Appl. 2015;27:585-606. doi: 10.1007/s00138-015-0737-3.
30. Yu K, Kirchgessner N, Grieder C, Walter A, Hund A. Plant image segmentation: reference images wheat. figshare. 2016. doi: 10.6084/m9.figshare.4176573.