Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives

Frontiers in Plant Science

Volumn 8, Issue , 2017, Pages

  Yang, Guijun ^a,b,c   Liu, Jiangang ^a,c   Zhao, Chunjiang ^a,b,c   Li, Zhenhong ^d   Huang, Yanbo ^e   Yu, Haiyang ^a,c   Xu, Bo ^a,c   Yang, Xiaodong ^a,b   Zhu, Dongmei ^f   Zhang, Xiaoyan ^g   Zhang, Ruyang ^h   Feng, Haikuan ^a   Zhao, Xiaoqing ^a   Li, Zhenhai ^a,b   Li, Heli ^a,b   Yang, Hao ^a,b  

^a Ministry of Agriculture Key Laboratory of Resource Remote Sensing and Digital Agriculture   (China)

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

^c AGRO ENVIRONMENTAL PROTECTION INSTITUTE   (China)

^d NEWCASTLE UNIVERSITY   (United Kingdom)

^e AGRICULTURAL RESEARCH SERVICE   (United States)

^f Institute of Agricultural Sciences for Lixiahe Region   (China)

^g NANJING AGRICULTURAL UNIVERSITY   (China)

^h INSTITUTE OF PLANT AND ENVIRONMENT PROTECTION   (China)

Author keywords

Crop breeding; Field phenotyping; High throughput; Remote sensing; UAV

Indexed keywords

EID: 85026443520     PISSN: None     EISSN: 1664462X     Source Type: Journal    
DOI: 10.3389/fpls.2017.01111     Document Type: Review

References (156)

1. Aasen, H., Burkart, A., Bolten, A., and Bareth, G. (2015). Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: from camera calibration to quality assurance. Isprs J. Photogram. Remote Sensing 108, 245–259. doi: 10.1016/j.isprsjprs.2015. 08.002
2. Acevo-Herrera, R., Aguasca, A., Bosch-Lluis, X., Camps, A., Martinez-Fernandez, J., Sanchez-Martin, N., et al. (2010). Design and first results of an UAV-Borne L-Band radiometer for multiple monitoring purposes. Remote Sensing 2, 1662–1679. doi: 10.3390/rs2071662
3. Araus, J. L., and Cairns, J. E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19, 52–61. doi: 10.1016/j.tplants.2013.09.008
4. Ballesteros, R., Ortega, J. F., Hernandez, D., and Moreno, M. A. (2014). Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: description of image acquisition and processing. Precision Agric. 15, 579–592. doi: 10.1007/s11119-014-9355-8
5. Baluja, J., Diago, M. P., Balda, P., Zorer, R., Meggio, F., Morales, F., et al. (2012). Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrig. Sci. 30, 511–522. doi: 10.1007/s00271-012-0382-9
6. Bareth, G., Bendig, J., Tilly, N., Hoffmeister, D., Aasen, H., and Bolten, A. (2016). A Comparison of UAV- and TLS-derived plant height for crop monitoring: using polygon grids for the analysis of crop surface models (CSMs). Photogrammetrie Fernerkundung Geoinformation 2016, 85–94. doi: 10.1127/pfg/2016/ 0289
7. Bellundagi, A., Singh, G. P., Prabhu, K. V., Arora, A., Jain, N., Ramya, P., et al. (2013). Early ground cover and other physiological traits as efficient selection criteria for grain yield under moisture deficit stress conditions in wheat (Triticum aestivum L.). Indian J. Plant Physiol. 18, 277–281. doi: 10.1007/s40502-013-0047-6
8. Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., and Bareth, G. (2014). Estimating biomass of barley using crop surface models (CSMs) derived from UAV-Based RGB imaging. Remote Sensing 6, 10395–10412. doi: 10.3390/rs61110395
9. Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., et al. (2015). Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Observ. Geoinformat. 39, 79–87. doi: 10.1016/j.jag.2015. 02.012
10. Berni, J. A. J., Zarco-Tejada, P. J., Sepulcre-Canto, G., Fereres, E., and Villalobos, F. (2009a). Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sens. Environ. 113, 2380–2388. doi: 10.1016/j.rse.2009.06.018
11. Berni, J. A. J., Zarco-Tejada, P. J., Suarez, L., and Fereres, E. (2009b). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans. Geosci. Remote Sensing 47, 722–738. doi: 10.1109/tgrs.2008.2010457
12. Booth, D. T., Cox, S. E., Fifield, C., Phillips, M., and Williamson, N. (2005). Image analysis compared with other methods for measuring ground cover. Arid Land Res. Manag. 19, 91–100. doi: 10.1080/15324980590916486
13. Bowman, W. D., and Strain, B. R. (1988). Physiological responses in two populations of Andropogon glomeratus Walter B.S.P. to short-term salinity. Oecologia 75, 78–82. doi: 10.1007/BF00378817
14. Burkart, A., Aasen, H., Alonso, L., Menz, G., Bareth, G., and Rascher, U. (2015). Angular dependency of hyperspectral measurements over wheat characterized by a novel UAV based goniometer. Remote Sensing 7, 725–746. doi: 10.3390/rs70100725
15. Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., and Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing 7, 4026–4047. doi: 10.3390/rs70404026
16. Capolupo, A., Kooistra, L., Berendonk, C., Boccia, L., and Suomalainen, J. (2015). Estimating plant traits of grasslands from UAV-acquired hyperspectral images: a comparison of statistical approaches. ISPRS Int. J. Geo Informat. 4, 2792–2820. doi: 10.3390/ijgi4042792
17. Chapman, S., Merz, T., Chan, A., Jackway, P., Hrabar, S., Dreccer, M., et al. (2014). Pheno-Copter: a low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy 4, 279–301. doi: 10.3390/agronomy4020279
18. Chaudhuri, U. N., and Kanemasu, E. T. (1982). Effect of water gradient on sorghum growth, water relations and yield. Can. J. Plant Sci. 62, 599–607. doi: 10.4141/cjps82-090
19. Chen, C. M. (2004). Searching for intellectual turning points: progressive knowledge domain visualization. Proc. Natl. Acad. Sci. U.S.A. 101, 5303–5310. doi: 10.1073/pnas.0307513100
20. Cobb, J. N., DeClerck, G., Greenberg, A., Clark, R., and McCouch, S. (2013). Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theor. Appl. Genet. 126, 867–887. doi: 10.1007/s00122-013-2066-0
21. Colomina, I., and Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J. Photogram. Remote Sensing 92, 79–97. doi: 10.1016/j.isprsjprs.2014.02.013
22. Comba, L., Gay, P., Primicerio, J., and Aimonino, D. (2017). Vineyard detection from unmanned aerial systems images. Comput. Electron. Agric. 114, 78–87. doi: 10.1016/j.compag.2015.03.011
23. Corcoles, J. I., Ortega, J. F., Hernandez, D., and Moreno, M. A. (2013). Use of digital photography from unmanned aerial vehicles for estimation of leaf area index in onion (Allium cepa L.) (Retracted article. See vol. 51, pg. 140, 2013). Eur. J. Agron. 45, 96–104. doi: 10.1016/j.eja.2012.11.001
24. Curran, P. J. (1985). Principles of Remote Sensing Longman Scientific and Technical. London: ELBS.
25. Danks, S. M., Evans, E. H., and Whittaker, P. A. (1984). Photosynthetic Systems: Structure: Function and Assembly. New York, NY: Wiley.
26. Deery, D., Jimenez-Berni, J., Jones, H., Sirault, X., and Furbank, R. (2014). Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4, 349–379. doi: 10.3390/agronomy4030349
27. De Souza, C. H. W., Lamparelli, R. A. C., Rocha, J. V., and Magalhaes, P. S. G. (2017). Height estimation of sugarcane using an unmanned aerial system(UAS) based on structure from motion (SfM) point clouds. Int. J. Remote Sensing 38, 2218–2230. doi: 10.1080/01431161.2017.1285082
28. Diaz-Varela, R. A., Zarco-Tejada, P. J., Angileri, V., and Loudjani, P. (2014). Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. J. Environ. Manag. 134, 117–126. doi: 10.1016/j.jenvman.2014.01.006
29. Du, M. M., and Noguchi, N. (2017). Monitoring of wheat growth status and mapping of wheat yield’s within-field spatial variations using color images acquired from UAV-camera system. Remote Sensing 9, 14. doi: 10.3390/rs9030289
30. Duan, T., Zheng, B. Y., Guo, W., Ninomiya, S., Guo, Y., and Chapman, S. C. (2017). 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. 44, 169–183. doi: 10.1071/fp16123
31. Filella, I., Serrano, L., Serra, J., and Penuelas, J. (1995). Evaluating Wheat Nitrogen Status with Canopy Reflectance Indices and Discriminant Analysis. Madison, WI, ETATS-UNIS: Crop Science Society of America.
32. Fischer, R. A. T., and Edmeades, G. O. (2010). Breeding and cereal yield progress. Crop Science 50, S85–S98. doi: 10.2135/cropsci2009.10.0564
33. Furbank, R. T., and Tester, M. (2011). Phenomics - technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16, 635–644. doi: 10.1016/j.tplants.2011.09.005
34. Gao, L., Yang, G., Yu, H., Xu, B., Zhao, X., Dong, J., et al. (2016). Retrieving winter wheat leaf area index based on unmanned aerial vehicle hyperspectral remote sensing. Trans. Chin. Soc. Agric. Eng. 32, 113–120. doi: 10.11975/j.issn.1002-6819.2016.22.016
35. Garcia-Ruiz, F., Sankaran, S., Maja, J. M., Lee, W. S., Rasmussen, J., and Ehsani, R. (2013). Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Comput. Electron. Agric. 91, 106–115. doi: 10.1016/j.compag.2012.12.002
36. Geipel, J., Link, J., and Claupein, W. (2014). Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system. Remote Sensing 6, 10335–10355. doi: 10.3390/rs61110335
37. Gevaert, C. M., Suomalainen, J., Tang, J., and Kooistra, L. (2015). Generation of spectral-temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications. IEEE J. Select. Topics Appl. Earth Observ. Remote Sensing 8, 3140–3146. doi: 10.1109/JSTARS.2015.2406339
38. Gitelson, A. A., Kaufman, Y. J., and Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing Environ. 58, 289–298. doi: 10.1016/S0034-4257(96)00072-7
39. Gomez-Candon, D., De Castro, A. I., and Lopez-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agric. 15, 44–56. doi: 10.1007/s11119-013-9335-4
40. Gomez-Candon, D., Virlet, N., Labbe, S., Jolivot, A., and Regnard, J. L. (2016). Field phenotyping of water stress at tree scale by UAV-sensed imagery: new insights for thermal acquisition and calibration. Precision Agric. 17, 786–800. doi: 10.1007/s11119-016-9449-6
41. Gong, P. (1999). Inverting a canopy reflectance model using a neural network. Int. J. Remote Sensing 20, 111–122. doi: 10.1080/014311699213631
42. Gonzalez-Dugo, V., Hernandez, P., Solis, I., and Zarco-Tejada, P. J. (2015). Using high-resolution hyperspectral and thermal airborne imagery to assess physiological condition in the context of wheat phenotyping. Remote Sensing 7, 13586–13605. doi: 10.3390/rs71013586
43. Gonzalez-Dugo, V., Zarco-Tejada, P. J., and Fereres, E. (2014). Applicability and limitations of using the crop water stress index as an indicator of water deficits in citrus orchards. Agric. Forest Meteorol. 198, 94–104. doi: 10.1016/j.agrformet.2014.08.003
44. Gonzalez-Dugo, V., Zarco-Tejada, P., Nicolas, E., Nortes, P. A., Alarcon, J. J., Intrigliolo, D. S., et al. (2013). Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precision Agric. 14, 660–678. doi: 10.1007/s11119-013-9322-9
45. Gonzalez-Recio, O., Coffey, M. P., and Pryce, J. E. (2014). On the value of the phenotypes in the genomic era. J. Dairy Sci. 97, 7905–7915. doi: 10.3168/jds.2014-8125
46. Grieder, C., Hund, A., and Walter, A. (2015). Image based phenotyping during winter: a powerful tool to assess wheat genetic variation in growth response to temperature. Funct. Plant Biol. 42, 387–396. doi: 10.1071/Fp14226
47. Guillen-Climent, M. L., Zarco-Tejada, P. J., Berni, J. A. J., North, P. R. J., and Villalobos, F. J. (2012). Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV. Precision Agric. 13, 473–500. doi: 10.1007/s11119-012-9263-8
48. Guillen-Climent, M. L., Zarco-Tejada, P. J., and Villalobos, F. J. (2014). Estimating radiation interception in heterogeneous orchards using high spatial resolution airborne imagery. IEEE Geosci. Remote Sensing Lett. 11, 579–583. doi: 10.1109/Lgrs.2013.2284660
49. Guo, W., Fukatsu, T., and Ninomiya, S. (2015). Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images. Plant Methods 11, 14. doi: 10.1186/s13007-015-0047-9
50. Gutierrez, M., Reynolds, M. P., Raun, W. R., Stone, M. L., and Klatt, A. R. (2010). Spectral water indices for assessing yield in elite bread wheat genotypes under well-irrigated, water-stressed, and high-temperature conditions. Crop Sci. 50, 197–214. doi: 10.2135/cropsci2009.07.0381
51. Haghighattalab, A., Perez, L. G., Mondal, S., Singh, D., Schinstock, D., Rutkoski, J., et al. (2016). Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods 12, 15. doi: 10.1186/s13007-016-0134-6
52. Hall, A., Louis, J., and Lamb, D. (2003). Characterising and mapping vineyard canopy using high-spatial-resolution aerial multispectral images. Comput. Geosci. 29, 813–822. doi: 10.1016/S0098-3004(03)00082-7
53. Hardin, P. J., and Jensen, R. R. (2011). Small-scale unmanned aerial vehicles in environmental remote sensing: challenges and opportunities. Gisci. Remote Sensing 48, 99–111. doi: 10.2747/1548-1603.48.1.99
54. Hernandez-Lopez, D., Felipe-Garcia, B., Sanchez, N., Gonzalez-Aguilera, D., and Gomez-Lahoz, J. (2012). Testing the radiometric performance of digital photogrammetric images: vicarious vs. laboratory calibration on the Leica ADS40, a Study in Spain. Photogrammetrie Fernerkundung Geoinformation 2012, 557–571. doi: 10.1127/1432-8364/2012/0139
55. Herwitz, S. R., Johnson, L. F., Dunagan, S. E., Higgins, R. G., Sullivan, D. V., Zheng, J., et al. (2004). Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Comp. Electron. Agric. 44, 49–61. doi: 10.1016/j.compag.2004.02.006
56. Holman, F. H., Riche, A. B., Michalski, A., Castle, M., Wooster, M. J., and Hawkesford, M. J. (2016). High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sensing 8:1031. doi: 10.3390/rs8121031
57. Honkavaara, E., Saari, H., Kaivosoja, J., Polonen, I., Hakala, T., Litkey, P., et al. (2013). Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sensing 5, 5006–5039. doi: 10.3390/rs5105006
58. Horler, D. N. H., Dockray, M., and Barber, J. (1983). The red edge of plant leaf reflectance. Int. J. Remote Sensing 4, 273–288. doi: 10.1080/01431168308948546
59. Huete, A. R., Liu, H. Q., Batchily, K., and Leeuwen, W. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing Environ. 59, 440–451. doi: 10.1016/s0034-4257(96)00112-5
60. Hunt, E. R., Cavigelli, M., Daughtry, C. S. T., Mcmurtrey, J. E., and Walthall, C. L. (2005). Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agric. 6, 359–378. doi: 10.1007/s11119-005-2324-5
61. Hunt, E. R., Hively, W. D., Fujikawa, S. J., Linden, D. S., Daughtry, C. S. T., and McCarty, G. W. (2010). Acquisition of NIR-Green-Blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing 2, 290–305. doi: 10.3390/rs2010290
62. Issei, H.-Y., Ishii, K., and Noguchi, N. (2010). Satellite and aerial remote sensing for production estimates and crop assessment. Environ. Control Biol. 48, 51–58. doi: 10.2525/ecb.48.51
63. Jones, H. G., Serraj, R., Loveys, B. R., Xiong, L., Wheaton, A., and Price, A. H. (2009). Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct. Plant Biol. 36, 978–989. doi: 10.1071/fp09123
64. Jordan, C. F. (1969). Derivation of leaf-area index from quality of light on the forest floor. Ecology, 50, 663–666. doi: 10.2307/1936256
65. Keep, N. R. (2013). Characterization of Physiological Parameters in Soybean with Genetic Improvement in Seed Yield. Master of Science, Kansas state university.
66. Kirchgessner, N., Liebisch, F., Yu, K., Pfeifer, J., Friedli, M., Hund, A., et al. (2016). The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. Funct. Plant Biol. 44, 154–168. doi: 10.1071/F. P.16165
67. Laliberte, A. S., Goforth, M. A., Steele, C. M., and Rango, A. (2011). Multispectral remote sensing from unmanned aircraft: image processing workflows and applications for rangeland environments. Remote Sensing 3, 2529–2551. doi: 10.3390/rs3112529
68. Lebourgeois, V., Begue, A., Labbe, S., Houles, M., and Martine, J. F. (2012). A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring. Precision Agric. 13, 525–541. doi: 10.1007/s11119-012-9262-9
69. Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Labbe, S., and Baret, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors 8, 3557–3585. doi: 10.3390/s8053557
70. Li, J. W., Zhang, F., Qian, X. Y., Zhu, Y. H., and Shen, G. X. (2015). Quantification of rice canopy nitrogen balance index with digital imagery from unmanned aerial vehicle. Remote Sensing Lett. 6, 183–189. doi: 10.1080/2150704X.2015.1021934
71. Li, L., Zhang, Q., and Huang, D. F. (2014). Areviewof imaging techniques for plant phenotyping. Sensors 14, 20078–20111. doi: 10.3390/s141120078
72. Li, W., Niu, Z., Chen, H. Y., Li, D., Wu, M. Q., and Zhao, W. (2016). Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecol. Indic. 67, 637–648. doi: 10.1016/j.ecolind.2016.03.036
73. Li, W., Niu, Z., Huang, N., Wang, C., Gao, S., and Wu, C. Y. (2015). Airborne LiDAR technique for estimating biomass components of maize: a case study in Zhangye City, Northwest China. Ecol. Indic. 57, 486–496. doi: 10.1016/j.ecolind.2015.04.016
74. Li, X. (2012). Feature extracting methods in spectrum data mining. Chin. Astron. Astrophys. 30, 94–105.
75. Li, Z., Chen, Z., Wang, L., Liu, J., and Zhou, Q. (2014). Area extraction of maize lodging based on remote sensing by small unmanned aerial vehicle. Trans. Chin. Soc. Agric. Eng. 30, 207–213. doi: 10.3969/j.issn.1002-6819.2014.19.025
76. Li, Z., Nie, C., Wei, C., Xu, X., Song, X., and Wang, J. (2016). Comparison of four chemometric techniques for estimating leaf nitrogen concentrations in winter wheat (Triticum Aestivum) based on hyperspectral features. J. Appl. Spectros. 83, 240–247. doi: 10.1007/s10812-016-0276-3
77. Liang, S. (2005). Quantitative Remote Sensing of Land Surfaces. New York, NY: Wiley.
78. Liang, S. (2008). Advances in Land Remote Sensing: System, Modeling, Inversion and Application. New York, NY: Springer.
79. Liang, S., Li, X., and Wang, J. (2013). Quantitative Remote Sensing: Concepts and Algorithms. Beijing: Science Press.
80. Liebisch, F., Kirchgessner, N., Schneider, D., Walter, A., and Hund, A. (2015). Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 11:9. doi: 10.1186/s13007-015-0048-8
81. Link, J., Senner, D., and Claupein, W. (2013). Developing and evaluating an aerial sensor platform (ASP) to collect multispectral data for deriving management decisions in precision farming. Comp. Electron. Agric. 94, 20–28. doi: 10.1016/j.compag.2013.03.003
82. Lu, G., Li, C., Yang, G., Yu, H., Zhao, X., and Zhang, X. (2016). Retrieving soybean leaf area index based on high imaging spectrometer. Soybean Sci. 35, 599–608.
83. Ma, B. L., Dwyer, L. M., Carlos, C., Cober, E. R., and Morrison, M. J. et al. (2001). Early prediction of soybean yield from canopy reflectance measurements. Agron. J. 93, 1227–1234. doi: 10.2134/agronj2001.1227
84. Mathews, A. J. (2014). Object-based spatiotemporal analysis of vine canopy vigor using an inexpensive unmanned aerial vehicle remote sensing system. J. Appl. Remote Sensing 8:17. doi: 10.1117/1.jrs.8.085199
85. Mathews, A. J., and Jensen, J. L. R. (2013). Visualizing and quantifying vineyard canopy LAI using an unmanned aerial vehicle (UAV) collected high density structure from motion point cloud. Remote Sensing 5, 2164–2183. doi: 10.3390/rs5052164
86. McNeil, B. E., Pisek, J., Lepisk, H., and Flamenco, E. A. (2016). Measuring leaf angle distribution in broadleaf canopies using UAVs. Agric. For. Meteorol. 218, 204–208. doi: 10.1016/j.agrformet.2015.12.058
87. Mullan, D. J., and Reynolds, M. P. (2010). Quantifying genetic effects of ground cover on soil water evaporation using digital imaging. Funct. Plant Biol. 37, 703–712. doi: 10.1071/Fp09277
88. Nasi, R., Honkavaara, E., Lyytikainen-Saarenmaa, P., Blomqvist, M., Litkey, P., Hakala, T., et al. (2015). Using UAV-Based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sensing 7, 15467–15493. doi: 10.3390/rs71115467
89. Neilson, E. H., Edwards, A. M., Blomstedt, C. K., Berger, B., Moller, B. L., and Gleadow, R. M. (2015). Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C-4 cereal crop plant to nitrogen and water deficiency over time. J. Exp. Bot. 66, 1817–1832. doi: 10.1093/jxb/eru526
90. Nigon, T. J., Mulla, D. J., Rosen, C. J., Cohen, Y., Alchanatis, V., Knight, J., et al. (2015). Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars. Comp. Electron. Agric. 112, 36–46. doi: 10.1016/j.compag.2014. 12.018
91. Olivares-Villegas, J. J., Reynolds, M. P., and McDonald, G. K. (2007). Drought-adaptive attributes in the Seri/Babax hexaploid wheat population. Funct. Plant Biol. 34, 189–203. doi: 10.1071/Fp06148
92. Ortega-Farias, S., Ortega-Salazar, S., Poblete, T., Kilic, A., Allen, R., Poblete-Echeverria, C., et al. (2016). Estimation of energy balance components over a drip-irrigated olive orchard using thermal and multispectral cameras placed on a helicopter-based unmanned aerial vehicle (UAV). Remote Sensing 8:18. doi: 10.3390/rs8080638
93. Ota, T., Ogawa, M., Shimizu, K., Kajisa, T., Mizoue, N., Yoshida, S., et al. (2015). Aboveground biomass estimation using structure from motion approach with aerial photographs in a seasonal tropical forest. Forests 6, 3882–3898. doi: 10.3390/f6113882
94. Overgaard, S. I., Isaksson, T., Kvaal, K., and Korsaeth, A. (2010). 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. Near Infr. Spectr. 18, 247–261. doi: 10.1255/jnirs.892
95. Pajares, G. (2015). Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogram. Eng. Remote Sensing 81, 281–329. doi: 10.14358/PERS.81.4.281
96. Pask, A. D., Pietragalla, J., Mullan, D. M., and Reynolds, M. P. (2012). Physiological Breeding II: A Field Guide to Wheat Phenotyping. Mexico City: CIMMYT.
97. Pena, J. M., Torres-Sanchez, J., de Castro, A. I., Kelly, M., and Lopez-Granados, F. (2013). Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE 8:77151. doi: 10.1371/journal.pone.0077151
98. PeÑUelas, J., Filella, I., Biel, C., Serrano, L., and SavÉ, R. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. Int. J. Remote Sensing 14, 1887–1905. doi: 10.1080/01431169308954010
99. Prasad, B., Carver, B. F., Stone, M. L., Babar, M. A., Raun, W. R., and Klatt, A. R. (2007). Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under great plains conditions. Crop Sci. 47, 1426–1440. doi: 10.2135/cropsci2006.07.0492
100. Rahaman, M. M., Chen, D. J., Gillani, Z., Klukas, C., and Chen, M. (2015). Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Front. Plant Sci. 6:15. doi: 10.3389/fpls.2015.00619
101. Rajan, N., and Maas, S. J. (2009). Mapping crop ground cover using airborne multispectral digital imagery. Precision Agric. 10, 304–318. doi: 10.1007/s11119-009-9116-2
102. Rashid, A., Stark, J. C., Tanveer, A., and Mustafa, T. (1999). Use of canopy temperature measurements as a screening tool for drought tolerance in spring wheat. J. Agron. Crop Sci. 182, 231–238. doi: 10.1046/j.1439-037x.1999.00335.x
103. Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thomason, W. E., et al. (2001). In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 93, 131–138. doi: 10.2134/agronj2001.931131x
104. Ray, D. K., Mueller, N. D., West, P. C., and Foley, J. A. (2013). Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8:e66428. doi: 10.1371/journal.pone.0066428
105. Reynolds, M., Balota, M. M., Delgado, M. I. B., and Fischer, R. A. (1994). Physiological and Morphological Traits Associated with Spring Wheat Yield under Hot, Irrigated Conditions. [Workshop paper]. v. 21.
106. Reynolds, M., Dreccer, F., and Trethowan, R. (2007). Drought-adaptive traits derived from wheat wild relatives and landraces. J. Exp. Bot. 58, 177–186. doi: 10.1093/jxb/erl250
107. Richards, J. A. (1990). Computer processing of remotely-sensed images: an introduction. Earth Sci. Rev. 27, 392–394. doi: 10.1016/0012-8252(90)90075-7
108. Richardson, A. J., and Wiegand, C. L. (1977). Distinguishing vegetation from soil background information. Photogram. Eng. Remote Sensing 43, 1541–1552.
109. Rondeaux, G., Steven, M., and Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing Environ. 55, 95–107. doi: 10.1016/0034-4257(95)00186-7
110. Rosen, P. A., Hensley, S., Wheeler, K., Sadowy, G., Miller, T., Shaffer, S., et al. (2006). “UAVSAR: a new NASA airborne SAR system for science and technology research,” in 2006 IEEE Conference on Radar (New York, NY), 8. doi: 10.1109/RADAR.2006.1631770
111. Salami, E., Barrado, C., and Pastor, E. (2014). UAV flight experiments applied to the remote sensing of vegetated areas. Remote Sensing 6, 11051–11081. doi: 10.3390/rs61111051
112. Samseemoung, G., Jayasuriya, H. P. W., and Soni, P. (2011). Oil palm pest infestation monitoring and evaluation by helicopter-mounted, low altitude remote sensing platform. J. Appl. Remote Sensing 5:16. doi: 10.1117/1.3609843
113. Samseemoung, G., Soni, P., Jayasuriya, H. P. W., and Salokhe, V. M. (2012). Application of low altitude remote sensing (LARS) platform for monitoring crop growth and weed infestation in a soybean plantation. Precision Agric. 13, 611–627. doi: 10.1007/s11119-012-9271-8
114. Sankaran, S., Khot, L. R., and Carter, A. H. (2015a). Field-based crop phenotyping: multispectral aerial imaging for evaluation of winter wheat emergence and spring stand. Comp. Electron. Agric. 118, 372–379. doi: 10.1016/j.compag.2015.09.001
115. Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J., et al. (2015b). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. Eur. J. Agron. 70, 112–123. doi: 10.1016/j.eja.2015.07.004
116. Saskia, G., Ruedi, B., and Daniel, F. (2017). Accuracy assessment of digital surface models from unmanned aerial vehicles’ imagery on glaciers. Remote Sensing 9:186. doi: 10.3390/rs9020186
117. Sayed, M. A., Schumann, H., Pillen, K., Naz, A. A., and Leon, J. (2012). AB-QTL analysis reveals new alleles associated to proline accumulation and leaf wilting under drought stress conditions in barley (Hordeum vulgare L.). BMC Genetics 13:61. doi: 10.1186/1471-2156-13-61
118. Shi, Y. Y., Thomasson, J. A., Murray, S. C., Pugh, N. A., Rooney, W. L., Shafian, S., et al. (2016). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PLoS ONE 11:e0159781. doi: 10.1371/journal.pone.0159781
119. Singh, A., Ganapathysubramanian, B., Singh, A. K., and Sarkar, S. (2016). Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci. 21, 110–124. doi: 10.1016/j.tplants.2015.10.015
120. Stöcker, C., Bennett, R., Nex, F., Gerke, M., and Zevenbergen, J. (2017). Review of the current state of UAV regulations. Remote Sensing 9:459. doi: 10.3390/rs9050459
121. Suarez, L., Zarco-Tejada, P. J., Berni, J. A. J., Gonzalez-Dugo, V., and Fereres, E. (2009). Modelling PRI for water stress detection using radiative transfer models. Remote Sensing Environ. 113, 730–744. doi: 10.1016/j.rse.2008.12.001
122. Sugiura, R., Noguchi, N., and Ishii, K. (2005). Remote-sensing technology for vegetation monitoring using an unmanned helicopter. Biosys. Eng. 90, 369–379. doi: 10.1016/j.biosystemseng.2004.12.011
123. Sugiura, R., Noguchi, N., and Ishii, K. (2007). Correction of low-altitude thermal images applied to estimating soil water status. Biosys. Eng. 96, 301–313. doi: 10.1016/j.biosystemseng.2006.11.006
124. Swain, K. C., Jayasuriya, H. P. W., and Salokhe, V. M. (2007). Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption. J. Appl. Remote Sensing 1:11. doi: 10.1117/1.2824287
125. Swain, K. C., Thomson, S. J., and Jayasuriya, H. P. W. (2010). Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. Trans. Asabe 53, 21–27. doi: 10.13031/2013.29493
126. Tamouridou, A. A., Alexandridis, T. K., Pantazi, X. E., Lagopodi, A. L., Kashefi, J., and Moshou, D. (2017). Evaluation of UAV imagery for mapping Silybum marianum weed patches. Int. J. Remote Sensing 38, 2246–2259. doi: 10.1080/01431161.2016.1252475
127. Tattaris, M., Reynolds, M. P., and Chapman, S. C. (2016). A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front. Plant Sci. 7:1131. doi: 10.3389/fpls.2016.01131
128. Thorp, K. R., Gore, M. A., Andrade-Sanchez, P., Carmo-Silva, A. E., Welch, S. M., White, J. W., et al. (2015). Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics. Comp. Electron. Agric. 118, 225–236. doi: 10.1016/j.compag.2015.09.005
129. Torres-Sanchez, J., Lopez-Granados, F., and Pena, J. M. (2015). An automatic object-based method for optimal thresholding in UAV images: application for vegetation detection in herbaceous crops. Comp. Electron. Agric. 114, 43–52. doi: 10.1016/j.compag.2015.03.019
130. Torres-Sanchez, J., Pena, J. M., de Castro, A. I., and Lopez-Granados, F. (2014). Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comp. Electron. Agric. 103, 104–113. doi: 10.1016/j.compag.2014.02.009
131. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing Environ. 8, 127–150. doi: 10.1016/0034-4257(79)90013-0
132. Turner, D., Lucieer, A., and Watson, C. (2012). An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM) point clouds. Remote Sensing 4, 1392–1410. doi: 10.3390/rs4051392
133. Uto, K., Seki, H., Saito, G., and Kosugi, Y. (2013). Characterization of rice paddies by a UAV-mounted miniature hyperspectral sensor system. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 6, 851–860. doi: 10.1109/Jstars.2013.2250921
134. Vega, F. A., Ramirez, F. C., Saiz, M. P., and Rosua, F. O. (2015). Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop. Biosys. Eng. 132, 19–27. doi: 10.1016/j.biosystemseng.2015.01.008
135. Verhoeven, G. J. J. (2009). Providing an archaeological bird’s-eye view - an overall picture of ground-based means to execute low-altitude aerial photography (LAAP) in archaeology. Archaeol. Prosp. 16, 233–249. doi: 10.1002/arp.354
136. Virlet, N., Sabermanesh, K., Sadeghi-Tehran, P., and Hawkesford, M. J. (2017). Field Scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring. Funct. Plant Biol. 44, 143–153. doi: 10.1071/Fp16163
137. Wallace, L., Lucieer, A., Watson, C., and Turner, D. (2012). Development of a UAV-LiDAR system with application to forest inventory. Remote Sensing 4, 1519–1543. doi: 10.3390/rs4061519
138. Wang, D., Zhou, Q., Chen, Z., and Liu, J. (2014). Research advances on crop identification using synthetic aperture radar. Trans. Chin. Soc. Agric. Eng. 30, 203–212. doi: 10.3969/j.issn.1002-6819.2014.16.027
139. Wang, J., Zhao, C., and Huang, W. (2008). Foundation and Application of Quantitative Remote Sensing in Agriculture. Beijing: Science Press.
140. Wang, W., Vinocur, B., and Altman, A. (2003). Plant responses to drought, salinity and extreme temperatures: towards genetic engineering for stress tolerance. Planta 218, 1–14. doi: 10.1007/s00425-003-1105-5
141. Watanabe, K., Guo, W., Arai, K., Takanashi, H., Kajiya-Kanegae, H., Kobayashi, M., et al. (2017). High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front. Plant Sci. 8:421. doi: 10.3389/fpls.2017.00421
142. Weiss, M., and Baret, F. (2017). Using 3D point clouds derived from UAV RGB imagery to describe vineyard 3D Macro- structure. Remote Sensing 9:17. doi: 10.3390/rs9020111
143. White, J. W., Andrade-Sanchez, P., Gore, M. A., Bronson, K. F., Coffelt, T. A., Conley, M. M., et al. (2012). Field-based phenomics for plant genetics research. Field Crops Res. 133, 101–112. doi: 10.1016/j.fcr.2012.04.003
144. Xiang, H. T., and Tian, L. (2011). Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosys. Eng. 108, 174–190. doi: 10.1016/j.biosystemseng.2010.11.010
145. Xiong, X., Yu, L. J., Yang, W. N., Liu, M., Jiang, N., Wu, D., et al. (2017). A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage. Plant Methods 13:7. doi: 10.1186/s13007-017-0157-7
146. Yang, G., Li, C., Yu, H., Xu, B., Feng, H., Gao, L., et al. (2015). UAV based multi-load remote sensing technologies for wheat breeding information acquirement. Trans. Chin. Soc. Agric. Eng. 31, 184–190. doi: 10.11975/j.issn.1002-6819.2015.21.024
147. Yang, W. N., Duan, L. F., Chen, G. X., Xiong, L. Z., and Liu, Q. (2013). Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies. Curr. Opin. Plant Biol. 16, 180–187. doi: 10.1016/j.pbi.2013.03.005
148. Yu, K., Kirchgessner, N., Grieder, C., Walter, A., and Hund, A. (2017). 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 13:15. doi: 10.1186/s13007-017-0168-4
149. Zaman-Allah, M., Vergara, O., Araus, J. L., Tarekegne, A., Magorokosho, C., Zarco-Tejada, P. J., et al. (2015). Unmanned aerial platform-based multispectral imaging for field phenotyping of maize. Plant Methods 11:10. doi: 10.1186/s13007-015-0078-2
150. Zarco-Tejada, P. J., Diaz-Varela, R., Angileri, V., and Loudjani, P. (2014). Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur. J. Agron. 55, 89–99. doi: 10.1016/j.eja.2014.01.004
151. Zarco-Tejada, P. J., Gonzalez-Dugo, V., and Berni, J. A. J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing Environ. 117, 322–337. doi: 10.1016/j.rse.2011.10.007
152. Zarco-Tejada, P. J., Morales, A., Testi, L., and Villalobos, F. J. (2013). Spatiotemporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance. Remote Sensing Environ. 133, 102–115. doi: 10.1016/j.rse.2013.05.011
153. Zhang, C. H., and Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agric. 13, 693–712. doi: 10.1007/s11119-012-9274-5
154. Zhang, C., Walters, D., and Kovacs, J. M. (2014). Applications of low altitude remote sensing in agriculture upon farmers’ requests–a case study in northeastern Ontario, Canada. PLoS ONE 9:e112894. doi: 10.1371/journal.pone.0112894
155. Zhao, C., and Qian, L. (2004). Comparative study of supervised and unsupervised classification in remote sensing image. J. Henan Uni. 34, 90–93.
156. Zhao, Z., Wang, Y., and Wang, C. (2014). Remote Sensing Image Processing. Beijing: Science Press.