Review of field-based phenotyping by unmanned aerial vehicle remote sensing platform

Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering

Volumn 32, Issue 24, 2016, Pages 98-106

  Liu, Jiangang ^a,b,c   Zhao, Chunjiang ^a,b,c   Yang, Guijun ^a,b,c   Yu, Haiyang ^a,b   Zhao, Xiaoqing ^a   Xu, Bo ^a,b   Niu, Qinglin ^a,d  

^a Beijing Research Center for Information Technology in Agriculture   (China)

^b NATIONAL ENGINEERING RESEARCH CENTER FOR INFORMATION TECHNOLOGY IN AGRICULTURE   (China)

^c AGRO ENVIRONMENTAL PROTECTION INSTITUTE   (China)

^d HENAN POLYTECHNIC UNIVERSITY   (China)

Author keywords

Crops; Phenotyping; Remote sensing; Sensors; Unmanned aerial vehicle

Indexed keywords

AGRICULTURAL MACHINERY; AGRICULTURE; CLIMATE MODELS; COST BENEFIT ANALYSIS; COST EFFECTIVENESS; COSTS; CROPS; DATA ACQUISITION; DATA HANDLING; EFFICIENCY; FIXED WINGS; INFORMATION MANAGEMENT; LAWS AND LEGISLATION; LEARNING SYSTEMS; OPTICAL RADAR; PHYSIOLOGICAL MODELS; SENSORS; SUSTAINABLE DEVELOPMENT; THROUGHPUT; TRACKING (POSITION); UNMANNED AERIAL VEHICLES (UAV); VEHICLES;

COMPREHENSIVE EVALUATION; CROP GROWTH AND DEVELOPMENT; LIGHT DETECTION AND RANGING SYSTEMS; MORPHOLOGICAL PARAMETERS; PHENOTYPING; QUANTITATIVE REMOTE SENSING; REMOTE SENSING PLATFORMS; TEXTURE CHARACTERISTICS;

REMOTE SENSING;

EID: 85007602687     PISSN: 10026819     EISSN: None     Source Type: Journal    
DOI: 10.11975/j.issn.1002-6819.2016.24.013     Document Type: Review

References (70)

1. Gai Junyi, Liu Kang, Zhao Jinming. Review of Chinese science and technology industry for species[J]. Chinese Agricultural Sciences, 2015, 48(17): 3303-3315. (in Chinese with English abstract)
2. Sadras V O, Rebetzke G J, Edmeades G O. The phenotype and the components of phenotypic variance of crop traits[J]. Field Crops Research, 2014, 154(3): 255-259.
3. Pajares G. Overview and current status of remote sensing applications based on Unmanned aerial vehicles (UAVs) photogramm[J]. Eng Remote Sens, 2015, 81(4): 281-330.
4. Grosskinsky D K, Svensgaard J, Christensen S, et al. Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap[J]. Journal of Experimental Botany, 2015, 66(18): 5429-5440.
5. Luis Araus J, Cairns J E. Field high-throughput phenotyping: The new crop breeding frontier[J]. Trends in Plant Science, 2014, 19(1): 52-61.
6. Colomina I, Molina P. Unmanned aerial systems for photogrammetry and remote sensing: A review [J]. Isprs Journal of Photogrammetry and Remote Sensing, 2014, 92(2): 79-97.
7. Herwitz S R, Johnson L F, Dunagan S E, et al. Imaging from an unmanned aerial vehicle: Agricultural surveillance and decision support[J]. Computers and Electronics in Agriculture, 2004, 44(1): 49-61.
8. Barabaschi D, Tondelli A, Desiderio F, et al. Next generation breeding[J]. Plant Science, 2016, 242: 3-13.
9. Cobb J N, DeClerck G, Greenberg A, et al. Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement[J]. Theoretical and Applied Genetics, 2013, 126(4): 867-887.
10. Sankaran S, Khot L R, Carter A H. Field-based crop phenotyping: Multispectral aerial imaging for evaluation of winter wheat emergence and spring stand[J]. Computers and Electronics in Agriculture, 2015, 118: 372-379.
11. Zhao Chunjiang. The development of agricultural remote sensing research and application[J]. Journal of Agricultural Machinery, 2014, 45(12): 277-293. (in Chinese with English abstract)
12. Wei X, Xu J, Guo H, et al. DTH8 suppresses flowering in rice, influencing plant height and yield potential simultaneously[J]. Plant Physiology, 2010, 153: 1747-1758.
13. Alheit K V, Busemeyer L, Liu W, et al. Multiple-line cross QTL mapping for biomass yield and plant height in triticale (×Triticosecale Wittmack)[J]. Theoretical and Applied Genetics, 2014, 127(1): 251-260.
14. Ilker E, Tonk F A, Tosun M, et al. Effects of direct selection process for plant height on some yield components in common wheat (Triticum aestivum) genotypes[J]. International Journal of Agriculture & Biology, 2013, 15(4): 795-797.
15. Zhang C, Kovacs J M. The application of small unmanned aerial systems for precision agriculture: A review[J]. Precision Agriculture, 2012, 13(6): 693-712.
16. Araus J L, Cairns J E. Field high-throughput phenotyping: The new crop breeding frontier[J]. Trends in Plant Science, 2014, 19(1): 52-61.
17. Sugiura R, Noguchi N, Ishii K. Remote-sensing Technology for Vegetation Monitoring using an Unmanned Helicopter[J]. Biosystems Engineering, 2005, 90(4): 369-379.
18. Diaz-Varela R A, Zarco-Tejada P J, Angileri V, et al. Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle[J]. Journal of Environmental Management, 2014, 134C(4): 117-126.
19. Pavuluri K, Chim B K, Griffey C A, et al. Canopy spectral reflectance can predict grain nitrogen use efficiency in soft red winter wheat[J]. Precision Agriculture, 2015, 16(4): 405-424.
20. Guo W, Fukatsu T, Ninomiya S. Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images[J]. Plant Methods, 2015, 11(1): 1-15.
21. Bendig J, Bolten A, Bennertz S, et al. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-Based RGB Imaging[J]. Remote Sensing, 2014, 6(11): 10395-10412.
22. Zaman-Allah M, Vergara O, Araus J L, et al. Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize[J]. Plant Methods, 2015, 11(1): 1-10.
23. Ballesteros R, Ortega J F, Hernandez D, et al. Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: Application to maize and onion crops of a semi-arid region in Spain[J]. Precision Agriculture, 2014, 15(6): 593-614.
24. Berni J A J, Zarco-Tejada P J, Suarez L, et al. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle[J]. Ieee Transactions on Geoscience and Remote Sensing, 2009, 47(3): 722-738.
25. Gonzalez-Dugo V, Hernandez P, Solis I, et al. Using high-resolution hyperspectral and thermal airborne imagery to assess physiological condition in the context of wheat phenotyping[J]. Remote Sensing, 2015, 7(10): 13586-13605.
26. Deery D, Jimenez-Berni J, Jones H, et al. Proximal remote sensing buggies and potential applications for field-based phenotyping[J]. Agronomy, 2014, 5(3): 349-379.
27. Li J, Zhang F, Qian X, et al. Quantification of rice canopy nitrogen balance index with digital imagery from unmanned aerial vehicle[J]. Remote Sensing Letters, 2015, 6(3): 183-189.
28. Rahaman M M, Chen D J, Gillani Z, et al. Advanced phenotyping and phenotype data analysis for the study of plant growth and development[J]. Frontiers in Plant Science, 2015, 6(15): 619.
29. Ma B L, Dwyer L M, Costa C, et al. Early prediction of soybean yield from canopy reflectance measurements[J]. Agronomy Journal, 2001, 93(6): 1227-1234.
30. Campbell J B, Wynne R H. Introduction to Remote Sensing[M]. New York: Guilford Press, 2011.
31. Siebert S, Ewert F, Rezaei E E, et al. Impact of heat stress on crop yield-on the importance of considering canopy temperature[J]. Environmental Research Letters, 2014, 9(4): 8.
32. Sullivan D G, Fulton J P, Shaw J N, et al. Evaluating the sensitivity of an unmanned thermal infrared aerial system to detect water stress in a cotton canopy[J]. International Journal of Sociology & Social Policy, 2007, 50(6): 708-724.
33. Lelong C C D. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots[J]. Sensors, 2008, 8(5): 3557-3585.
34. Jones H G, Serraj R, Loveys B R, et al. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field [J]. Functional Plant Biology, 2009, 36(11): 978-989.
35. Costa J M, Grant O M, Chaves M M. Thermography to explore plant-environment interactions[J]. Journal of Experimental Botany, 2013, 64(13): 3937-3949.
36. Kipp S, Mistele B, Schmidhalter U. The performance of active spectral reflectance sensors as influenced by measuring distance, device temperature and light intensity[J]. Computers and Electronics in Agriculture, 2014, 100(2): 24-33.
37. Chapman S, Merz T, Chan A, et al. Pheno-copter: A low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping[J]. Agronomy, 2015, 4(2): 279-301.
38. Zhang C H, Walters D, Kovacs J M. Applications of low altitude remote sensing in agriculture upon farmers' requests: A case study in Northeastern Ontario, Canada[J]. Plos One, 2013, 9(11): e112894-e112894.
39. Geipel J, Link J, Claupein W. Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system[J]. Remote Sensing, 2014, 6(11): 10335-10355.
40. Gao Lin, Yang Guijun, Wang Baoshan, et al. Soybean leaf area index retrieval with UAV (unmanned aerial vehicle) remote sensing imagery[J]. Chinese Journal of Eco-Agriculture, 2015, 23(7): 868-876. (in Chinese with English abstract)
41. Verger A, Vigneau N, Chéron C, et al. Green area index from an unmanned aerial system over wheat and rapeseed crops[J]. Remote Sensing of Environment, 2014, 152: 654-664.
42. Aasen H, Burkart A, Bolten A, et al. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2015, 108(5): 251-254.
43. Overgaard S I, Isaksson T, Kvaal K, et al. Comparisons of two hand-held, multispectral field radiometers and a hyperspectral airborne imager in terms of predicting spring wheat grain yield and quality by means of powered partial least squares regression[J]. Journal of near Infrared Spectroscopy, 2010, 18(18): 247-261.
44. Uto K, Seki H, Saito G, et al. Characterization of rice paddies by a UAV-mounted miniature hyperspectral sensor system[J]. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2): 851-860.
45. Gevaert C M, Suomalainen J, Tang J, et al. Generation of spectral-temporal response surfaces by combining multispectral satellite and hyperspectral UAV Imagery for precision agriculture applications[J]. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 1(8): 3140-3146.
46. Li W, Niu Z, Chen H, et al. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system[J]. Ecological Indicators, 2016, 67: 637-648.
47. Yang Guijun, Li Changchun, Yu Haiyang, et al. UAV based multi-load remote sensing technologies for wheat breeding information acquirement[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(21): 184-190. (in Chinese with English abstract)
48. Zhao Chunxia, Qian Lexiang. Comparative study ofsupervised and unsupervised classification in remote sensing image[J]. Journal of Henan University: Natural Science, 2004, 34(3): 90-93. (in Chinese with English abstract)
49. Kokaly R F, Clark R N. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression[J]. Remote Sensing of Environment, 1999, 67(3): 267-287.
50. Bannari A, Morin D, Bonn F, et al. A review of vegetation indices[J]. Remote Sens Rev, 1995, 13(1/2): 95-120.
51. Raun W R, Solie J B, Johnson G V, et al. In-season prediction of potential grain yield in winter wheat using canopy reflectance[J]. Agronomy Journal, 2001, 93(1): 131-138.
52. Erdle K, Mistele B, Schmidhalter U. Spectral high-throughput assessments of phenotypic differences in biomass and nitrogen partitioning during grain filling of wheat under high yielding Western European conditions[J]. Field Crops Research, 2013, 141(1): 16-26.
53. Hunt J E R, Hively W D, Fujikawa S J, et al. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring[J]. Remote Sensing, 2010, 2(1): 290-305.
54. Bendig J, Yu K, Aasen H, et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 39: 79-87.
55. Olivaresvillegas J, Reynolds P, Mcdonald K. Drought-adaptive attributes in the Seri/Babax hexaploid wheat population[J]. Functional Plant Biology, 2007, 34(3): 189-203.
56. Masuka B, Luis Araus J, Das B, et al. Phenotyping for abiotic stress tolerance in maize[J]. Journal of Integrative Plant Biology, 2012, 54(4): 238-249.
57. Reynolds M, Manes Y, Izanloo A, et al. Phenotyping approaches for physiological breeding and gene discovery in wheat[J]. Annals of Applied Biology, 2009, 155(3): 309-320.
58. Humplik J F, Lazar D, Husickova A, et al. Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses- a review[J]. Plant Methods, 2015, 11(1): 1-10.
59. Heaton E, Voigt T, Long S P. A quantitative review comparing the yields of two candidate C 4, perennial biomass crops in relation to nitrogen, temperature and water[J]. Biomass & Bioenergy, 2004, 27(1): 21-30.
60. Chen D J, Neumann K, Friedel S, et al. Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis[J]. Plant Cell, 2014, 26(12): 4636-4655.
61. Munns R, James R A, Sirault X R R, et al. New phenotyping methods for screening wheat and barley for beneficial responses to water deficit[J]. Journal of Experimental Botany, 2010, 61(13): 3499-3507.
62. Curran P J, Dungan J L, Peterson D L. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies[J]. Remote Sensing of Environment, 2001, 76(3): 349-359.
63. Babar M A, Reynolds M P, Ginkel M V, et al. Spectral reflectance to estimate genetic variation for In-season biomass, leaf chlorophyll, and canopy temperature in wheat[J]. Crop Science, 2006, 46(3): 76-85.
64. Suomalainen J, Anders N, Iqbal S, et al. A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles[J]. Remote Sensing, 2014, 6(11): 11013-11030.
65. Hruska R, Mitchell J, Anderson M, et al. Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle[J]. Remote Sensing, 2012, 4(9): 2736-2752.
66. Laliberte A S, Goforth M A, Steele C M, et al. Multispectral remote sensing from unmanned aircraft: Image processing workflows and applications for rangeland environments[J]. Remote Sensing, 2011, 3(3): 2529-2551.
67. Capolupo A, Kooistra L, Berendonk C, et al. Estimating plant traits of grasslands from UAV-acquired hyperspectral images: a comparison of statistical approaches[J]. Isprs International Journal of Geo-Information, 2015, 4(4): 2792-2820.
68. Corcoles J I, Ortega J F, Hernandez D, et al. Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle[J]. Biosystems Engineering, 2013, 115(1): 31-42.
69. Thorp K R, Gore M A, Andrade-Sanchez P, et al. Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics[J]. Computers and Electronics in Agriculture, 2015, 118(C): 225-236.
70. Gutierrez-Rodriguez M, Reynolds M P, Escalante-Estrada J A, et al. Association between canopy reflectance indices and yield and physiological traits in bread wheat under drought and well-irrigated conditions[J]. Australian Journal of Agricultural Research, 2004, 55(11): 1139-1147.