ПЕРСПЕКТИВЫ ПРИМЕНЕНИЯ БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ АППАРАТОВ В ТОЧНОМ ЗЕМЛЕДЕЛИИ: ОБЗОР
Аннотация
Об авторах
Б. У. СулейменовКазахстан
С. И. Танирбергенов
Казахстан
Список литературы
1. Sidorova V.A., Zhukovskii E.E., Lekomtsev P.V., Yakushev V.V. Geostatistical analysis of the soil and crop parameters in a field experiment on precision agriculture // Eurasian Soil Science. - 2012. - 8 (45). - Рр. 783-792.
2. Swain K.C., Jayasuriya H.P., Salokhe V.M. Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption [Electronic resource] Journal of Applied Remote Sensing. -2007. - №1. - Mode of access: https://doi.org/10.1117/1.2824287.
3. Stafford J.V. Implementing precision agriculture in the 21st century // Journal of Agricultural Engineering Research. - 2000. - Vol. 76. - Рр. 267-275.
4. Robertson M., Carberry P., Brennan L. The economic benefits of precision agriculture: cast studies from Australia grain farms / Controlled Traffic and Precision Agriculture Conference. - 2007. - Рр. 181-187.
5. McBratney A., Whelan B., Ancev T. Future directions of precision agriculture// Precision Agriculture. - 2005. - Vol. 6. - Рр. 7-23.
6. Moran M.S., Inoue Y., Barnes E.M. Opportunities and limitation for image-based remote sensing in precision crop Management // Remote Sensing of Environment. -1997. - Vol. 61. - Рр. 319-346.
7. Cook S.E., Bramley R.G. Precision agriculture: Opportunities, benefits and pitfalls of site specific crop management in Australia // Australian Journal of Experimental Agriculture. - 1998. - Vol. 38. - P. 753-763.
8. Warren G., Metternicht G. Agricultural applications of high-resolution digital multispectral imagery: Evaluating within-field spatial variability of canola (Brassica na-pus) in Western Australia // Photogrammetric Engineering and Remote Sensing. -2005.- Vol. 71. - Рр. 595-602.
9. Zhang J.H., Wang K., Bailey J.S., Wang R.C. Predicting nitrogen status of rice using multispectral data at canopy scale // Pedosphere. - 2006. - Vol. 16. - Рр. 108-117.
10. Berni J.A., Zarco-Tejada P.J., Suarez L., Fereres E. Thermal and narrowband mul-tispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle // IEEE Transactions on Geoscience and Remote Sensing. - 2009. - Vol. 47. - Рр. 722-738.
11. Rango A., Laliberte A.S., Herrick J.E., Winters C., Havstad K., Steele C. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management [Electronic resource] Journal of Applied Remote Sensing. - 2009. - Vol. 3. -Mode of access: https://doi.org/10.1117/1.3216822
12. Hunt E.R., Cavigelli M., Daughtry C.S., McMurtrey J.E., Walthall C.L. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status // Precision Agriculture. - 2005. - Vol. 6. - Рр. 359-378.
13. Lelong C.D., Burger P., Jubelin G., Roux B., Labbe S., Barett F. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots // Sensors. - 2008. - Vol. 8. - Рр. 3557-3585.
14. Nebiker S., Annen A., Scherrer M., Oesch D. A light-weight multispectral sensor for micro UAV: Opportunities for very high resolution airborne remote sensing//The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. - 2008. - Vol. XXXVII. Part B1. - Pp. 1193-1200.
15. Hardin P.J., Hardin TJ. Small-scale remotely piloted vehicles in environmental research // Geography Compass. - 2010. - Vol. 4. - Рр. 1297-1311.
16. Xiang H., Tian L. Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform // Biosystems Engineering. - 2011. - Vol. 108. - Рр. 104-113.
17. Colewell R.N. Determining the prevalence of certain cereal crop diseases by means of aerial photography // Hilgardia. - 1956. - Vol. 26. - Рр. 223-286.
18. Monmonier M. Aerial photography at the Agricultural Adjustment Administration: Acreage controls, conservation // Photogrammetric Engineering & Remote Sensing. - 2002. - Vol. 68. - Рр. 1257-1261.
19. Malthus TJ., Maderia A.C. High resolution spectroradiometry: Spectral reflectance of field bean leaves infected by Botrytis fabae // Remote Sensing of Environment.- 1993. - Vol. 45. - Рр. 107-116.
20. De Tar W.R., Chesson J.H., Penner J.V., Ojala J.C. Detection of soil properties with airborne hyperspectral measurements of bare fields // Transactions of the ASABE. -2008. - Vol. 51. -Рр. 463-470.
21. Gomez C., Rossel R.V., McBratney A.B. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study// Geoderma. -2008. - Vol. 146. - Рр. 403-411.
22. Rao N.R., Garg P.K., Ghosh S.K., Dadhwal V.K. Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery // Journal of Agricultural Science. - 2008. - Vol. 146. - Рр. 65-75.
23. Lan Y., Huang Y., Martin D.E., Hoffmann W.C. Development of an airborne remote sensing system for crop pest management: System integration and verification // American Society of Agricultural and Biological Engineers. - 2009. - Vol. 25. - P. 607-615.
24. Erickson B.J., Johannsen C.J., Vorst J.J., Biehl L.L. Using remote sensing to assess stand loss and defoliation in maize // Photogrammetric Engineering and Remote Sensing. - 2004. - Vol. 70. - Рр. 717-722.
25. Wu J.D., Wang D., Bauer M.E. Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies // Field Crops Research. - 2007. - Vol. 102. - Рр. 33-42.
26. Gutierrez P.A., Lopez-Granados F., Jurado-Exposito J.M., Hervas-Martinez C. Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data // Computers and Electronics in Agriculture. - 2008. - Vol. 64. - Рр. 293-306.
27. Seelan S.K., Laguette S., Casady G.M., Seielstad G.A. Remote sensing applications for precision agriculture: A learning community approach // Remote Sensing of Environment. -2003. - Vol. 88. - Рр. 157-169.
28. Zhao D.H., Huang L.M., Li J.L., Qi J.G. A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy// ISPRS Journal of Photogrammetry and Remote Sensing. - 2007. - Vol. 62. - Рр. 25-33.
29. Godwin R.J., Richards T.E., Wood G.A., Welsh J.P., Knight S.M. An economic analysis of the potential for precision farming in UK cereal production//Biosystems Engineering. - 2003. - Vol. 84. - Рр. 533-545.
30. Du Q., Chang N.B., Yang C.H., Srilakshmi K.R. Combination of multispectral remote sensing, variable rate technology and environmental modeling for citrus pest management// Journal of Environmental Management. - 2008. - Vol. 86. - Рр. 14-26.
31. Gomez-Casero M.T., Castillejo-Gonzalez I.L., Garcia-Ferrer A., Pena-Barragan J.M., Jurado-Exposito M., Garcia-Torres L. Spectral discrimination of wild oat and canary grass in wheat fields for less herbicide application // Agronomy for Sustainable Development. - 2010. - Vol. 30. - Рр. 689-699.
32. McNairn H., Brisco B. The application of C-band polarimetric SAR for agriculture: A review // Canadian Journal of Remote Sensing. - 2004. - Vol. 30. - Рр. 525-542.
33. Sullivan D.G., Shaw J.N., Rickman D. IKONOS imagery to estimate surface soil property variability in two Alabama physiographies//Soil Science Society of America Journal. -2005. - Vol. 69. - Рр. 1789-1798.
34. Zarco-Tejada P.J., Gonzalez-Dugo V., Berni J.A. 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 of Environment.- 2012. - Vol. 117. -Рр. 322-337.
35. Laliberte A.S., Rango A., Fredrickson E.L. Multi-scale, object-oriented analysis of QuickBird imagery for determining percent cover in arid land vegetation. In: 20th Biennial Workshop on Aerial Photography, Videography, and High Resolution Digital Imagery for Resource Assessment [Electronic resource]. - Weslaco, TX, 2012. - Mode of access: https://jornada.nmsu.edu/bibliography/05-055Proc.pdf.
36. Beeri O., Phillips R., Carson P., Liebig M. Alternate satellite models for estimation of sugar beet residue nitrogen credit // Agriculture, Ecosystems & Environment. -2005. - Vol. 107. - Рр. 21-35.
37. Bausch W.C., Khosla R. QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize // Precision Agriculture. - 2010.- Vol. 11. - Рр. 274-290.
38. Donoghue D., Watt P., Cox N., Wilson J. Remote sensing ofspecies mixtures in coniferplantations using LiDAR height and intensity data. International Workshop 3Dremote sensing in Forestry [Electronic resource]. - 2012. - Mode of access: http://www.rali.boku.ac.at/fileadmin/_/H857-VFL/workshops/3drsforestry/presentations/6a.5-donoghue.pdf.
39. Castillejo-Gonzalez I.L., Lopez-Granados F., Garcia-Ferrer A., Pena-Barragan J.M., Jurado-Exposito M., Orden M.S. Object- and pixel-based analysis for mapping crops and their agro- environmental associated measures using QuickBird imagery // Computers and Electronics in Agriculture. - 2009. - Vol. 68. - Рр. 207-215.
40. Haboudane D., Miller J.R., Pattey E., Zarco-Tejada P.J., Strachan I.B. Hyperspec-tral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture // Remote Sensing of Environment. - 2004. - Vol. 90. - Рр. 337-352.
41. Tenkorang F., DeBoer L. On-farm profitability of remote sensing in agriculture// Journal of Terrestrial Observation. - 2007. - Vol. 1. - Рр. 50-59.
42. Whipker L.D., Akridge J.T. Precision agricultural services dealership survey results [Electronic resource]. - 2012. - Mode of access: http://www.agecon.purdue.edu/ cab/research_articles/articles/2009_crop_life_precision_report.pdf.
43. Robert P.C. Use of remote sensing imagery for precision farming/In: Proceedings of 26th International Symposium on Remote Sensing of Environment and 18th symposium of the Canadian Remote Sensing Society, Ontario, Canada. - 1996. - Pp. 596599.
44. Murakami E., Saraiva A.M., Ribeiro L.C., Cugnasca C.E., Hirakawa A.R., Correa P. L. An infrastructure for the development of distributed service-oriented information systems for precision agriculture // Computers and Electronics in Agriculture. - 2007. -Vol. 58. - Рр. 37-48.
45. Griffin T.W., Lowenberg-Deboer J., Lambert D.M., Peone J., Payne T, Daberkow S. G. Adoption, profitability, and making better use of precision farming data [Electronic resource] / Staff paper.- West Lafayette, IN, USA: Department of Agricultural Economics, Purdue University, 2004. - No. 04-06. - Mode of access: http://ageconsearch.umn.edu/ record/28615.
46. Lamb D.W., Frazier P., Adams P. Improving pathways to adoption: Putting the right P's in precision agriculture // Computers and Electronics in Agriculture. - 2008. -Vol. 61. - Рр. 4-9.
47. Swain K.C., Thomson S.J., Jayasuriya H.P. Adoption of an unmanned helicopter for low- altitude remote sensing to estimate yield and total biomass of a rice crop// American Society of Agricultural and Biological Engineers. -2010. - Vol. 53. - Рр. 21-27.
48. Laliberte A.S., Rango A. Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands // GIScience & Remote Sensing. - 2011. - Vol. 48. - Рр. 4-23.
49. Eisenbeiss H. A mini unmanned aerial vehicle (UAV): system over and image acquisition. In: A. Gruen, Sh. Murai, T. Fuse,F. Remondino (Eds.) [Electronic resource] / Proceedings of International Workshop on Processing and Visualization Using High-Resolution Imagery, XXXVI(5/W1), Pitsanulok, Thailand. - 2012. - Mode of access: http://www.isprs.org/proceedings/XXXVI/5-W1/papers/11.pdf.
50. Hunt E.R., Daughtry C.S., Walthall C.L., McMurtrey J.E., Dulaney W.P. Agricultural remote sensing using radio-controlled aircraft. In: T. VanToai, D. Major, M. McDonald, J. Schepers & L. Tarpley (Eds.). Digital image and spectral techniques: Applications to precision agriculture and crop physiology // American Society of Agronomy - 2003. - Vol. 66. - Pp. 197-205.
51. Johnson L.F., Herwitz S.R., Lobitz B.M., Dunagan S.E. Feasibility of monitoring coffee field ripeness with airborne multispectral imagery // Applied Engineering in Agriculture. -2004. - Vol. 20. - Pp. 845-849.
52. Laliberte A.S., Herrick J.E., Rango A. Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring // Photogrammetric Engineering and Remote Sensing. - 2010. - Vol. 76. - Pp. 661672.
53. Kendoul F., Lara D., Fantoni-Coichot I., Lozano R. Real-time nonlinear embedded control for an autonomous quadrotor helicopter // Journal of Guidance Control and Dynamics. -2007. - Vol. 30. - Pp. 1049-1061.
54. Primicerio J., Gennaro S.D., Fiorillo E., Genesio L., Lugato E., Matese A., et al. A flexible unmanned aerial vehicle for precision agriculture // Precision Agriculture. -2012. - Vol. 13. -Рр. 517-523.
55. Aber J.S., Marzolff I., Ries J.B. Small-format aerial photography // Boston: Elsevier, 2010. - 266 p.
56. Lewis G. Evaluating the use of a low-cost unmanned aerial vehicle platform in acquiring digital imagery for emergency response. In J. Li, S. Zlatanova, & A. Fabbri (Eds.), Geomatics solutions for disaster management. - Berlin: Springer, 2007. - Pp. 117133.
57. Hunt E.R., Hively W.D., Fujikawa S.J., Linden D.S., Daughtry C.S.T., McCarty G. W. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring // Remote Sensing. - 2010. - Vol. 2. - Рр. 290-305.
58. Gomez-Candon D., Lopez-Granados F., Caballero-Novella J.J., Gomez-Casero M.T., Jurado-Exposito M., Garcia-Torres L. Geo-referencing remote images for precision agriculture using arti-ficial terrestrial targets // Precision Agriculture. - 2011. - Vol. 12. -Рр. 876-891.
59. Quilter M.C., Anderson V.J. A proposed method for determining shrub utilization using (LA/ LS) imagery // Journal of Range Management. - 2001. - Vol. 54. - Рр. 378-381.
60. Hardin P., Jackson M. An unmanned aerial vehicle for rangeland photography // Rangeland Ecology & Management. - 2005. - Vol. 58. - Рр. 439-442.
61. Hinkleya E.A., Zajkowski T. USDA forest service-NASA: Unmanned aerial systems demon- strations-pushing the leading edge in fire mapping // Geocarto International. - 2011.- Vol. 26. - Рр. 103-111.
62. Laliberte A.S., Rango A. Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery // IEEE Transactions on Geoscience and Remote Sensing, Special Issue on UAV Sensing Systems in Earth Observation. - 2009. - Vol. 47. - Рр. 761-770.
63. Hardin P.J., Jackson M.W., Anderson V.J., Johnson R. Detecting squarrose knapweed (Centaurea virgata Lam. Ssp. Squarrosa Gugl.) using a remotely piloted vehicle: A Utah case study // GIScience & Remote Sensing. - 2007. - Vol. 44. - Рр. 203-219.
64. Fisher P.D., Abuzar M., Rab M.A., Best F., Chandra S. Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers' historical paddock yields and normalised difference vegetation index // Crop & Pasture Science. - 2009 .- Vol. 60. - Рр. 844-858.
65. Silva C.B., Vale S.R., Pinto F.C., Muller C.S., Moura A.D. The economic feasibility of precision agriculture in Mato Grosso do Sul State, Brazil: A case study // Precision Agriculture. - 2007. - Vol. 8. - Рр. 255-265.
66. Adrian A.M., Norwood S.H., Mask P.L. Producers' perceptions and attitudes toward precision agriculture technologies // Computer and Electronics in Agriculture. -2005. - Vol. 48. - Рр. 256-271.
67. Torbett J.C., Roberts R.K., Larson J.A., English B.C. Perceived improvements in nitrogen fertilizer efficiency from cotton precision farming // Computers and Electronics in Agriculture. - 2008. - Vol. 64. - Рр. 140-148.
68. Rango A., Laliberte A.S. Impact of flight regulations on effective use of unmanned aerial vehicles for natural resources applications [Electronic resource] Journal of Applied Remote Sensing. - 2010. - Vol. 4. - Mode of access: https:// doi.org/10.1117/1.3474649.
69. Flowers M., Weisz R., White J.G. Yield-based management zones and grid sampling strategies: Describing soil test and nutrient variability // Agronomy Journal. -2005. - Vol. 97. -Р р. 968-982.
70. McBratney A.B., Whelan B.M., Shatar T. Variability and uncertainty in spatial, temporal and spatio-temporal crop yield and related data // In: Precision agriculture: Spatial and temporal variability of environmental quality. Chichester: Wiley - 1997. -Pp. 141-160.
71. Long D.S., Carlson G.R., DeGloria S.D. Quality of field management maps. In P. C. Robert (Ed.), Proceedings of Site-Specific Management for Agriculture Systems Madison: American Society of Agronomy - 1995. - Pp. 251-271.
72. Diker K., Heermann D.F., Bordahl M.K. Frequency analysis of yield for delineating yield response zones // Precision Agriculture. - 2004. - Vol. 5. - Рр. 435-444.
73. Yang C., Bradford J.M., Wiegand C.L. Airborne multispectral imagery for mapping variable growing conditions and yields of cotton, grain sorghum, and corn. American Society of Agricultural and Biological Engineers. - 2001. - Vol. 44. - Рр. 1983-1994.
74. Beeri O., Peled A. Geographical model for precise agriculture monitoring with real-time remote sensing // Journal of Photogrammetry and Remote Sensing. - 2009. -Vol. 64. - Рр. 47-54.
Рецензия
Для цитирования:
Сулейменов Б.У., Танирбергенов С.И. ПЕРСПЕКТИВЫ ПРИМЕНЕНИЯ БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ АППАРАТОВ В ТОЧНОМ ЗЕМЛЕДЕЛИИ: ОБЗОР. Почвоведение и агрохимия. 2018;(2):85-100.
For citation:
Suleymenov B.U., Tanirbergenov S.I. PERSPECTIVES OF APPLICATION OF UNMANNED AERIAL VEHICLE IN THE PRECISION FARMING: REVIEW. Soil Science and Agrichemistry. 2018;(2):85-100. (In Russ.)