Imaging from manned ultra-light and unmanned aerial vehicles for estimating properties of spring wheat

Precision Agriculture

Volumn 19, Issue 5, 2018, Pages 876-894

  Mozgeris, G ^a   Jonikavicius D ^a   Jovarauskas, D ^a   Zinkevicius R ^a   Petkevicius S ^a   Steponavicius D ^a  

^a VYTAUTAS MAGNUS UNIVERSITY   (Lithuania)

Author keywords

Colour infrared images; Hyperspectral imaging; Precision agriculture; Variable rate technology

Indexed keywords

AGRICULTURAL TECHNOLOGY; AIRCRAFT; CHLOROPHYLL; COMPARATIVE STUDY; ESTIMATION METHOD; IMAGE ANALYSIS; IMAGING METHOD; PRECISION AGRICULTURE; SPRING (SEASON); ULTRAVIOLET RADIATION; UNMANNED VEHICLE; WHEAT;

LITHUANIA;

TRITICUM AESTIVUM;

EID: 85044053926     PISSN: 13852256     EISSN: 15731618     Source Type: Journal    
DOI: 10.1007/s11119-018-9562-9     Document Type: Article

References (70)

1. Abdel-Rahman, E. M., Makori, D. M., Landmann, T., Piiroinen, R., Gasim, S., Pellikka, P., et al. (2015). The utility of AISA eagle hyperspectral data and random forest classifier for flower mapping. Remote Sensing, 7(10), 13298–13318. 10.3390/rs71013298.
2. Anderson, K., & Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3), 138–146. 10.1890/120150.
3. Aneece, I., & Epstein, H. (2015). Distinguishing early successional plant communities using ground-level hyperspectral data. Remote Sensing, 7(12), 16588–16606. 10.3390/rs71215850.
4. Bausch, W. C., & Khosla, R. (2010). QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize. Precision Agriculture, 11(3), 274–290. 10.1007/s11119-009-9133-1.
5. Beckman, J., Borchers, A., Jones, C.A. (2013). Agriculture’s supply and demand for energy and energy products, Economic information bulletin. U.S. Department of Agriculture. Economic Research Service, 1: 112.
6. Carter, G. A., & Knapp, A. K. (2001). Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany, 88(4), 677–684. 10.2307/2657068.
7. Chen, C., Pan, J., & Lam, S. K. (2014). A review of precision fertilization research. Environmental Earth Sciences, 71(9), 4073–4080. 10.1007/s12665-013-2792-2.
8. Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79–97. 10.1016/j.isprsjprs.2014.02.013.
9. Demotes-Mainard, S., Boumaza, R., Meyer, S., & Cerovic, Z. G. (2008). Indicators of nitrogen status for ornamental woody plants based on optical measurements of leaf epidermal polyphenol and chlorophyll contents. Scientia Horticulturae, 115(4), 377–385. 10.1016/j.scienta.2007.10.006.
10. Deng, W., Huang, Y. B., Zhao, C. J., & Wang, X. (2015). Identification of seedling cabbages and weeds using hyperspectral imaging. International Journal of Agricultural and Biological Engineering, 8(5), 65–72.
11. Devadas, R., Lamb, D. W., Backhouse, D., & Simpfendorfer, S. (2015). Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat. Precision Agriculture, 16(5), 477–491. 10.1007/s11119-015-9390-0.
12. Diacono, M., Castrignano, A., Vitti, C., Stellacci, A. M., Marino, L., Cocozza, C., et al. (2014). An approach for assessing the effects of site-specific fertilization on crop growth and yield of durum wheat in organic agriculture. Precision Agriculture, 15(5), 479–498. 10.1007/s11119-014-9347-8.
13. Estep, L., Terrie, G., & Davis, B. (2004). Crop stress detection using AVIRIS hyperspectral imagery and artificial neural networks. International Journal of Remote Sensing, 25(22), 4999–5004. 10.1080/01431160412331291242.
14. Garcia-Torres, L., Pena-Barragan, J. M., Lopez-Granados, F., Jurado-Exposito, M., & Fernandez-Escobar, R. (2008). Automatic assessment of agro-environmental indicators from remotely sensed images of tree orchards and its evaluation using olive plantations. Computers and Electronics in Agriculture, 61(2), 179–191. 10.1016/j.compag.2007.11.004.
15. Goel, P. K., Prasher, S. O., Landry, J. A., Patel, R. M., Bonnell, R. B., Viau, A. A., et al. (2003). Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and Electronics in Agriculture, 38(2), 99–124. 10.1016/S0168-1699(02)00138-2.
16. Gomez-Casero, M. T., Castillejo-Gonzalez, I. L., Garcia-Ferrer, A., Pena-Barragan, J. M., Jurado-Exposito, M., Garcia-Torres, L., et al. (2010). Spectral discrimination of wild oat and canary grass in wheat fields for less herbicide application. Agronomy for Sustainable Development, 30(3), 689–699. 10.1051/agro/2009052.
17. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3), 416–426. 10.1016/S0034-4257(02)00018-4.
18. Hakala, T., Honkavaara, E., Saari, H., Mäkynen, J., Kaivosoja, J., Pesonen, L., et al. (2013). Spectral imaging from UAVs under varying illumination conditions. International archives of the photogrammetry, Remote Sensing and Spatial Information Sciences. 10.5194/isprsarchives-xl-1-w2-189-2013.
19. Honkavaara, E., Saari, H., Kaivosoja, J., Pölönen, 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(10), 5006–5039. 10.3390/rs5105006.
20. Hunt, E. R., Jr., Hively, W. D., Fujikawa, S. J., Linden, D. S., Daughtry, C. S. T., & McCarty, G. W. (2010). Acquisition of NIR-Green-Blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing, 2(1), 290–305. 10.3390/rs2010290.
21. Im, J., & Jensen, J. R. (2008). Hyperspectral remote sensing of vegetation. Geography Compass, 2(6), 1943–1961. 10.1111/j.1749-8198.2008.00182.x.
22. Key, T., Warner, T. A., McGraw, J. B., & Fajvan, M. A. (2001). A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest. Remote Sensing of Environment, 75(1), 100–112. 10.1016/S0034-4257(00)00159-0.
23. Kokaly, R. F., & Clark, R. N. (1999). Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67(3), 267–287. 10.1016/S0034-4257(98)00084-4.
24. Krienke, B., Ferguson, R. B., Schlemmer, M., Holland, K., Marx, D., & Eskridge, K. (2017). Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor. Precision Agriculture, 18(6), 900–915. 10.1007/s11119-017-9534-5.
25. Kriščiukaitienė, I., Tamošaitienė, A., & Andrikienė, S. (2007). Racionalaus dydžio ūkių modeliavimas (Simulation of rational size farms). Žemės ūkio mokslai, 14, 78–85.
26. Lamsodienė, I., Kaulakys, R., Sankalas, V., & Sankalas, V. (Eds.). (2010). Profesijos istorija 1990–2010 (The history of profession 1990–2010) (pp. 278–284). Lithuania: Vilnius.
27. Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Labbé, S., & Baret, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8(5), 3557–3585. 10.3390/s8053557.
28. Li, H., Lee, W. S., Wang, K., Ehsani, R., & Yang, C. (2014). ‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture, 15(2), 162–183. 10.1007/s11119-013-9325-6.
29. Lillesand, T., Kiefer, R., & Chipman, J. (2008). In Remote sensing and image interpretation, 6th ed. (p. 756). New York, NY: John Wiley & Sons.
30. Luther, J. E., & Carroll, A. L. (1999). Development of an Index of balsam fir vigor by foliar spectral reflectance. Remote Sensing of Environment, 69(3), 241–252. 10.1016/S0034-4257(99)00016-4.
31. Mahajan, G. R., Sahoo, R. N., Pandey, R. N., Gupta, V. K., & Kumar, D. (2014). Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.). Precision agriculture, 15(5), 499–522. 10.1007/s11119-014-9348-7.
32. Moorthy, I., Miller, J., & Noland, T. L. (2008). Estimating chlorophyll concentration in conifer needles with hyperspectral data: An assessment at the needle and canopy level. Remote Sensing of Environment, 112(6), 2824–2838. 10.1016/j.rse.2008.01.013.
33. Mozgeris, G. (2008). Estimation and use of continuous surfaces of forest parameters: Options for lithuanian forest inventory. Baltic Forestry, 14(2), 176–184.
34. Mozgeris, G., & Augustaitis, A. (2013). Estimating crown defoliation of Scots pine (Pinus sylvestris L.) trees using small format digital aerial images. IForest-Biogeosciences and Forestry, 6, 15–22. 10.3832/ifor0705-006.
35. Mozgeris, G., Gadal, S., Jonikavičius, D., Straigyte, L., Ouerghemmi, W., Juodkiene, V. (2016). Hyperspectral and color-infrared imaging from ultra-light aircraft: Potential to recognize tree species in urban environments. Proceedings of the 8th Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Los Angeles: University of California. pp. 542–546. 10.1109/whispers.2016.8071756.
36. Mulla, D. J. (2013). Twenty-five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358–371. 10.1016/j.biosystemseng.2012.08.009.
37. Muñoz-Huerta, R. F., Guevara-Gonzalez, R. G., Contreras-Medina, L. M., Torres-Pacheco, I., Prado-Olivarez, J., & Ocampo-Velazquez, R. V. (2013). A review of methods for sensing the nitrogen status in plants: Advantages, disadvantages and recent advances. Sensors, 13(8), 10823–10843. 10.3390/s130810823.
38. Ouerghemmi, W., Gadal, S., Mozgeris, G., Jonikavičius, D., & Weber, C. (2017). Urban objects classification by spectral library: Feasibility and applications. Urban Remote Sensing Event (JURSE). 10.1109/JURSE.2017.7924629.
39. Pascucci, S., Casa, R., Belviso, C., Palombo, A., Pignatti, S., & Castaldi, F. (2014). Estimation of soil organic carbon from airborne hyperspectral thermal infrared data: a case study. European Journal of Soil Science, 65(6), 865–875. 10.1111/ejss.12203.
40. Pullanagari, R. R., Yule, I. J., Tuohy, M. P., Hedley, M. J., Dynes, R. A., & King, W. M. (2012). In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture. Precision Agriculture, 13(3), 351–369. 10.1007/s11119-011-9251-4.
41. Rama, R. N. (2008). Development of a crop-specific spectral library and discrimination of various agricultural crop varieties using hyperspectral imagery. International Journal of Remote Sensing, 29(1), 131–144. 10.1080/01431160701241779.
42. Ranjan, R., Chopra, U. K., Sahoo, R. N., Singh, A. K., & Pradhan, S. (2012). Assessment of plant nitrogen stress in wheat (Triticum aestivum L.) through hyperspectral indices. International Journal of Remote Sensing, 33(20), 6342–6360. 10.1080/01431161.2012.687473.
43. Rasmussen, J., Ntakos, G., Nielsen, J., Svensgaard, J., Poulsen, R. N., & Christensen, S. (2016). Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? European Journal of Agronomy, 74, 75–92. 10.1016/j.eja.2015.11.026.
44. Richardson, A. D., Duigan, S. P., & Berlyn, G. P. (2002). An evaluation of non-invasive methods to estimate foliar chlorophyll content. New Phytologist, 153(1), 185–194. 10.1046/j.0028-646x.2001.00289.x.
45. Schepers, J. S., Francis, D. D., Vigil, M., & Below, F. E. (1992). Comparison of corn leaf nitrogen concentration and chlorophyll meter readings. Communications in Soil Science and Plant Analysis, 23(17–20), 2173–2187. 10.1080/00103629209368733.
46. Seelan, S. K., Laguette, S., Casady, G. M., & Seielstad, G. A. (2003). Remote sensing applications for precision agriculture: a learning community approach. Remote Sensing of Environment, 88(1–2), 157–169. 10.1016/j.rse.2003.04.007.
47. Siegmann, B., Jarmer, T., Beyer, F., & Ehlers, M. (2015). The potential of pan-sharpened EnMAP data for the assessment of wheat LAI. Remote Sensing, 7(10), 12737–12762. 10.3390/rs71012737.
48. Sovoe, S. (2011). Mapping irrigated area fragments for crop water use assessment using handheld spectroradiometer. International Journal of Agronomy, 2011, 8. 10.1155/2011/974040.
49. Stone, M. L., Solie, J. B., Raun, W. R., Whitney, R. W., Taylor, S. L., & Ringer, J. D. (1996). Use of spectral radiance for correcting inseason fertilizer nitrogen deficiencies in winter wheat. Transactions of the ASAE, 39(5), 1623–1631. 10.13031/2013.27678.
50. Suomalainen, J., Anders, N., Iqbal, S., Roerink, G., Franke, J., Wenting, P., et al. (2014). A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles. Remote Sensing, 6(11), 11013–11030. 10.3390/rs61111013.
51. Tanaka, S., Kawamura, K., Maki, M., Muramoto, Y., Yoshida, K., & Akiyama, T. (2015). Spectral index for quantifying leaf area index of winter wheat by field hyperspectral measurements: A case study in gifu prefecture. Central Japan Remote Sensing, 7(5), 5329–5346. 10.3390/rs70505329.
52. Thenkabail, P. S., Smith, R. B., & Pauw, E. D. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71(2), 158–182. 10.1016/S0034-4257(99)00067-X.
53. Thoren, D., & Schmidhalter, U. (2009). Nitrogen status and biomass determination of oilseed rape by laser-induced chlorophyll fluorescence. European Journal of Agronomy, 30(3), 238–242. 10.1016/j.eja.2008.12.001.
54. Tilly, N., Aasen, H., & Bareth, G. (2015). Fusion of plant height and vegetation indices for the estimation of barley biomass. Remote Sensing, 7(9), 11449–11480. 10.3390/rs70911449.
55. Tremblay, N., Wang, Z., Ma, B. L., Belec, C., & Vigneault, P. A. (2009). Comparison of crop data measured by two commercial sensors for variable-rate nitrogen application. Precision Agriculture, 10, 145. 10.1007/s11119-008-9080-2.
56. Van Beek, J., Tits, L., Somers, B., Deckers, T., Verjans, W., Bylemans, D., et al. (2015). Temporal Dependency of yield and quality estimation through spectral vegetation indices in pear orchards. Remote Sensing, 7(8), 9886–9903. 10.3390/rs70809886.
57. Wang, F., Huang, J., Wang, Y., Liu, Z., Peng, D., & Cao, F. (2013). Monitoring nitrogen concentration of oilseed rape from hyperspectral data using radial basis function. International Journal of Digital Earth, 6(6), 550–562. 10.1080/17538947.2011.628414.
58. Wang, Q., & Li, P. (2012). Hyperspectral indices for estimating leaf biochemical properties in temperate deciduous forests: Comparison of simulated and measured reflectance data sets. Ecological Indicators, 14(1), 56–65. 10.1016/j.ecolind.2011.08.021.
59. Wilson, J. H., Zhang, C., & Kovacs, J. M. (2014). Separating crop species in northeastern ontario using hyperspectral data. Remote Sensing, 6(2), 925–945. 10.3390/rs6020925.
60. Wu, C., Han, X., Niu, Z., & Dong, J. (2010). An evaluation of EO-1 hyperspectral Hyperion data for chlorophyll content and leaf area index estimation. International Journal of Remote Sensing, 31(4), 1079–1086. 10.1080/01431160903252335.
61. Xu, C., Zeng, W., Huang, J., Wu, J., & van Leeuwen, W. J. D. (2016). Prediction of soil moisture content and soil salt concentration from hyperspectral laboratory and field data. Remote Sensing, 8(1), 42. 10.3390/rs8010042.
62. Yang, G., Li, C., Wang, Y., Yuan, H., Feng, H., Xu, B., et al. (2017). The DOM generation and precise radiometric calibration of a UAV-mounted miniature snapshot hyperspectral imager. Remote Sensing, 9(7), 642. 10.3390/rs9070642.
63. Yao, X., Jia, W., Si, H., Guo, Z., Tian, Y., Liu, X., et al. (2014). Exploring novel bands and key index for evaluating leaf equivalent water thickness in wheat using hyperspectra influenced by nitrogen. PLoS ONE, 9(6), e96352. 10.1371/journal.pone.0096352.
64. Yao, H. L., Tang, L., Tian, Y., Brown, R. L., Bhatnagar, D., & Cleveland, T. E. (2010). Using hyperspectral data in precision farming applications. Ch. 25. In P. S. Thenkabail, J. G. Lyon, & A. Huete (Eds.), Hyperspectral remote sensing of vegetation (p. 705). Boca Raton, FL: CRC Press.
65. Ye, X., Sakai, K., Sasao, A., & Asada, S. I. (2009). Estimation of citrus yield from canopy spectral features determined by airborne hyperspectral imagery. International Journal of Remote Sensing, 30(18), 4621–4642. 10.1080/01431160802632231.
66. Yu, K., Leufen, G., Hunsche, M., Noga, G., Chen, X., & Bareth, G. (2014). Investigation of leaf diseases and estimation of chlorophyll concentration in seven barley varieties using fluorescence and hyperspectral indices. Remote Sensing, 6(1), 64–86. 10.3390/rs6010064.
67. Zarco-Tejada, P. J., Miller, J. R., Morales, A., Berjon, A., & Aguera, J. (2004). Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing of Environment, 90(4), 463–476. 10.1016/j.rse.2004.01.017.
68. Zhang, C., Walters, D., & 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(11), e112894. 10.1371/journal.pone.0112894.
69. Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture: A worldwide overview. Computers and Electronics in Agriculture, 36(2–3), 113–132. 10.1016/S0168-1699(02)00096-0.
70. Zulhaidi, H., Shafri, M., Hamdan, N., & Anuar, M. I. (2012). Detection of stressed oil palms from an airborne sensor using optimized spectral indices. International Journal of Remote Sensing, 33(14), 4293–4311. 10.1080/01431161.2011.619208.