Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach

Plant Methods

Volumn 11, Issue 1, 2015, Pages

  Liebisch, Frank ^a   Kirchgessner, Norbert ^a   Schneider, David ^b   Walter, Achim ^a   Hund, Andreas ^a  

^a ETH ZURICH   (Switzerland)

^b Norddeutsche Pflanzenzucht   (Germany)

Author keywords

Aerial phenotyping; Image analysis; NDVI; Near infrared imaging; Remote sensing; Thermal imaging; Zea mays

Indexed keywords

ZEA MAYS;

EID: 84928266341     PISSN: None     EISSN: 17464811     Source Type: Journal    
DOI: 10.1186/s13007-015-0048-8     Document Type: Article

References (43)

1. White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, et al. Field-based phenomics for plant genetics research. Field Crop Res. 2012;133:101-12.
2. Araus JL, Cairns JE. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 2014;19:52-61.
3. Rajendran K, Tester M, Roy SJ. Quantifying the three main components of salinity tolerance in cereals. Plant Cell Environ. 2009;32:237-49.
4. Walter A, Studer B, Kölliker R. Advanced phenotyping offers opportunities for improved breeding of forage and turf species. Ann Bot. 2012;110:1271-9.
5. Montes JM, Technow F, Dhillon BS, Mauch F, Melchinger AE. High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crop Res. 2011;121:268-73.
6. Kipp S, Mistele B, Schmidhalter U. Identification of stay-green and early senescence phenotypes in high-yielding winter wheat, and their relationship to grain yield and grain protein concentration using high-throughput phenotyping techniques. Funct Plant Biol. 2013;41:227-35.
7. 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.
8. Zia S, Romano G, Spreer W, Sanchez C, Cairns J, Araus JL, et al. Infrared thermal imaging as a rapid tool for identifying water-stress tolerant maize genotypes of different phenology. J Agron Crop Sci. 2013;199:75-84.
9. Romano G, Zia S, Spreer W, Sanchez C, Cairns J, Luis Araus J, et al. Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress. Comput Electron Agric. 2011;79:67-74.
10. Cairns JE, Crossa J, Zaidi PH, Grudloyma P, Sanchez C, Araus JL, et al. Identification of drought, heat, and combined drought and heat tolerant donors in maize. Crop Sci. 2013;53:1335-46.
11. Cairns JE, Sanchez C, Vargas M, Ordonez R, Araus JL. Dissecting maize productivity: ideotypes associated with grain yield under drought stress and well-watered conditions. J Integr Plant Biol. 2012;54:1007-20.
12. Nelissen H, Moloney M, Inzé D. Translational research: from pot to plot. Plant Biotechnol J. 2014;12:277-85.
13. Furbank RT, Tester M. Phenomics - technologies relieve the phenotyping bottleneck. Trends Plant Sci. 2011;16:635-44.
14. Soudani K, Hmimina G, Delpierre N, Pontailler JY, Aubinet M, Bonal D, et al. Ground-based network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes. Remote Sens Environ. 2012;123:234-45.
15. Sakamoto T, Gitelson AA, Nguy-Robertson AL, Arkebauer TJ, Wardlow BD, Suyker AE, et al. An alternative method using digital cameras for continuous monitoring of crop status. Agr Forest Meteorol. 2012;154:113-26.
16. Jones HG, Vaughan RA. Remote Sensing of Vegetation, Principles, Techniques And Applications. Oxford: Oxford University Press; 2010.
17. Lee WS, Alchanatis V, Yang C, Hirafuji M, Moshou D, Li C. Sensing technologies for precision specialty crop production. Comput Electron Agric. 2010;74:2-33.
18. Zhang C, Kovacs JM. The application of small unmanned aerial systems for precision agriculture: a review. Precis Agric. 2012;13:693-712.
19. Watts AC, Ambrosia VG, Hinkley EA. Unmanned aircraft systems in remote sensing and scientific research: classification and considerations of use. Remote Sens. 2012;4:1671-92.
20. Jones HG, Serraj R, Loveys BR, Xiong L, Wheaton A, Price AH. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct Plant Biol. 2009;36:978-89.
21. Jähne B. Digital Image Processing. 5th ed. Berlin: Springer; 2002.
22. Thomas H. Senescence, ageing and death of the whole plant. New Phytol. 2013;197:696-711.
23. Costa JM, Grant OM, Chaves MM. Thermography to explore plant-environment interactions. J Exp Bot. 2013;64:3937-49.
24. Farsiu S, Robinson MD, Elad M, Milanfar P. Fast and robust multiframe super resolution. IEEE Trans Image Process. 2004;13:1327-44.
25. Escobar-Gutierrez AJ, Combe L. Senescence in field-grown maize: from flowering to harvest. Field Crop Res. 2012;134:47-58.
26. Verhulst N, Govaerts B, Nelissen V, Sayre KD, Crossa J, Raes D, et al. The effect of tillage, crop rotation and residue management on maize and wheat growth and development evaluated with an optical sensor. Field Crop Res. 2011;120:58-67.
27. Colomb B, Kiniry JR, Debaeke P. Effect of soil phosphorus on leaf development and senescence dynamics of field-grown maize. Agron J. 2000;92:428-35.
28. Fletcher AL, Johnstone PR, Chakwizira E, Brown HE. Radiation capture and radiation use efficiency in response to N supply for crop species with contrasting canopies. Field Crop Res. 2013;150:126-34.
29. Liebisch F, Küng G, Damm A, Walter A. Characterization of Crop Vitality and Resource Use Efficiency By Means of Combining Imaging Spectroscopy Based Plant Traits. In: Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, vol. 6. Lausanne, Switzerland: IEEE International; 2014.
30. Hunt ER, Doraiswamy PC, McMurtrey JE, Daughtry CST, Perry EM, Akhmedov B. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int J Appl Earth Observation Geoinform. 2013;21:103-12.
31. Maydup ML, Graciano C, Guiamet JJ, Tambussi EA. Analysis of early vigour in twenty modern cultivars of bread wheat (Triticum aestivum L.). Crop Pasture Sci. 2012;63:987-96.
32. Lopes MS, Araus JL, van Heerden PDR, Foyer CH. Enhancing drought tolerance in C4 crops. J Exp Bot. 2011;62:3135-53.
33. Zheng HJ, Wu AZ, Zheng CC, Wang YF, Cai R, Shen XF, et al. QTL mapping of maize (Zea mays) stay-green traits and their relationship to yield. Plant Breed. 2009;128:54-62.
34. Schymanski SJ, Or D, Zwieniecki MA. Stomatal control and leaf thermal and hydraulic capacitances under rapid environmental fluctuations. PLoS One. 2013;8:e54231.
35. Alchanatis V, Cohen Y, Cohen S, Moller M, Sprinstin M, Meron M, et al. Evaluation of different approaches for estimating and mapping crop water status in cotton with thermal imaging. Precis Agric. 2010;11:27-41.
36. FAO. World Reference Base for Soil Resources. Rome: FAO, ISRIC and ISSS; 1998.
37. Lizaso JI, Batchelor WD, Westgate ME. A leaf area model to simulate cultivar-specific expansion and senescence of maize leaves. Field Crop Res. 2003;80:1-17.
38. Basics of infrared photography, infrared light. [http://www.ir-photo.net/ir_imaging.html, access date 12.05.2013], 12.05.2013
39. Lewis J. Fast Normalized Cross-Correlation, Vision Interface. 1995. p. 120-3.
40. Fox RH, Piekielek WP, Macneal KE. Comparison of late-season diagnostic tests for predicting nitrogen status of corn. Agron J. 2001;93:590-7.
41. Calderini DF, Dreccer MF, Slafer GA. Consequences of breeding on biomass, radiation interception and radiation-use efficiency in wheat. Field Crop Res. 1997;52:271-81.
42. R-Development-Core-Team. R Foundation for Statistical Computing, Vienna, Austria ISBN 3-900051-07-0. 2008. URL http://www.r-project.org/.
43. Butler D. asreml: asreml() Fits the Linear Mixed Mode. R package version 3.00. R package version 3.00. 2006.