The application of small unmanned aerial systems for precision agriculture: a review
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Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.
KeywordsLow altitude remote sensing Unmanned aerial systems (UAS) UAS platforms UAS cameras UAS aviation regulations UAS limitations Farmer participation
This research was supported by a Grant (project #920161) provided to John M. Kovacs from the Northern Ontario Heritage Fund Corporation of Canada.
- Aber, J. S., Aaviksoo, K., Karofeld, E., & Aber, S. W. (2002). Patterns in Estonian bogs as depicted in color kite aerial photographs. Suo, 53, 1–15.Google Scholar
- Aber, J. S., Marzolff, I., & Ries, J. B. (2010). Small-format aerial photography. Boston: Elsevier. 266.Google Scholar
- Amoroso, L., & Arrowsmith, R. (2000). Balloon photography of brush fire scars east of Carefree, AZ. Retrieved March 12, 2012 from http://activetectonics.asu.edu/Fires_and_Floods/10_24_00_photos/.
- Berni, J. A. J., Zarco-Tejada, P. J., Suarez, L., Gonzalez-Dugo, V., & Fereres, E. (2009a). Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors. Retrieved March 12, 2012 from http://www.ipi.uni-hannover.de/fileadmin/institut/pdf/isprs-Hannover2009/Jimenez_Berni-155.pdf.
- Castillejo-Gonzalez, I. L., Lopez-Granados, F., Garcia-Ferrer, A., Pena-Barragan, J. M., Jurado-Exposito, M., Orden, M. S., et al. (2009). Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery. Computers and Electronics in Agriculture, 68, 207–215.CrossRefGoogle Scholar
- Colewell, R. N. (1956). Determining the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia, 26, 223–286.Google Scholar
- De Tar, W. R., Chesson, J. H., Penner, J. V., & Ojala, J. C. (2008). Detection of soil properties with airborne hyperspectral measurements of bare fields. Transactions of the ASABE, 51, 463–470.Google Scholar
- Donoghue, D., Watt, P., Cox, N., & Wilson, J. (2006). Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data. International Workshop 3D remote sensing in Forestry. Retrieved March 12, 2012 form (http://www.rali.boku.ac.at/fileadmin/_/H857-VFL/workshops/3drsforestry/presentations/6a.5-donoghue.pdf).
- Eisenbeiss, H. (2004). A mini unmanned aerial vehicle (UAV): system over and image acquisition. In: A. Gruen, Sh. Murai, T. Fuse, F. Remondino (Eds.). Proceedings of International Workshop on Processing and Visualization Using High-Resolution Imagery, XXXVI(5/W1), Pitsanulok, Thailand. CDROM. Retrieved March 12, 2012 from http://www.isprs.org/proceedings/XXXVI/5-W1/papers/11.pdf.
- Erickson, B. J., Johannsen, C. J., Vorst, J. J., & Biehl, L. L. (2004). Using remote sensing to assess stand loss and defoliation in maize. Photogrammetric Engineering and Remote Sensing, 70, 717–722.Google Scholar
- Eugster, H., & Nebiker, S. (2007). Geo-registration of video sequences captured from Mini UAVs: Approaches and accuracy assessment. The 5th International Symposium on Mobile Mapping Technology, Padua, Italy. Retrieved March 12, 2012 from http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cts=1331769791050&ved=0CCYQFjAA&url=http%3A%2F%2Fwww.3dgi.ch%2Fpublications%2Feh%2F2007_MMT07_Padua_final.pdf&ei=rzFhT9LrN4aJtwe9w9W-BQ&usg=AFQjCNHlP4X-S3DkZib-OdlEap7T4JBtg.
- Fisher, P. D., Abuzar, M., Rab, M. A., Best, F., & Chandra, S. (2009). 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, 60, 844–858.CrossRefGoogle Scholar
- 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, 689–699.CrossRefGoogle Scholar
- Griffin, T. W., Lowenberg-Deboer, J., Lambert, D. M., Peone, J., Payne, T., & Daberkow, S. G. (2004). Adoption, profitability, and making better use of precision farming data. Staff paper No. 04–06 West Lafayette, IN, USA: Department of Agricultural Economics, Purdue University.Google Scholar
- Gutierrez, P. A., Lopez-Granados, F., Jurado-Exposito, J. M. P. M., & Hervas-Martinez, C. (2008). Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data. Computers and Electronics in Agriculture, 64, 293–306.CrossRefGoogle Scholar
- Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral 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, 90, 337–352.CrossRefGoogle Scholar
- Huang, Y., Lan, Y., Hoffmann, W. C., & Fritz, B. K. (2008). Development of an unmanned aerial vehicle-based remote sensing system for site-specific management in precision agriculture. In Proceedings of the 9th International Symposium on Precision Agriculture. Denver, CO. CDROM.Google Scholar
- Hunt, E. R., Daughtry, C. S., Walthall, C. L., McMurtrey, J. E., & Dulaney, W. P. (2003). 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. ASA Special Publications Number 66. Madison, WI, USA: American Society of Agronomy, pp. 197–205.Google Scholar
- Hunt, E. R., Hively, W. D., Daughtry, C. S., McCarty, G. W., Fujikawa, S. J., Ng, T. L., Tranchitella, M., Linden, D. S., & Yoel, D. W. (2008). Remote sensing of crop leaf area index using unmanned airborne vehicles. In ASPRS Pecora 17 Conference Proceeding, Bethesda, MD: American Society for Photogrammetry and Remote Sensing. CDROM. Retrieved March 12, 2012 from http://www.asprs.org/a/publications/proceedings/pecora17/0018.pdf.
- Jackson, R. D. (1984). Remote sensing of vegetation characteristics for farm management. Proceedings of the Society of Photo-Optical Instrumentation Engineers, 475, 81–96.Google Scholar
- Johnson, L. F., Herwitz, S. R., Lobitz, B. M., & Dunagan, S. E. (2004). Feasibility of monitoring coffee field ripeness with airborne multispectral imagery. Applied Engineering in Agriculture, 20, 845–849.Google Scholar
- Laliberte, A. S., Herrick, J. E., & Rango, A. (2010). Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring. Photogrammetric Engineering and Remote Sensing, 76, 661–672.Google Scholar
- Laliberte, A. S., & Rango, A. (2009). Texture and scale in object-based analysis of sub-decimeter resolution unmanned aerial vehicle (UAV) imagery. IEEE Transactions on Geoscience and Remote Sensing, Special Issue on UAV Sensing Systems in Earth Observation, 47, 761–770.Google Scholar
- Laliberte, A. S., Rango, A., & Fredrickson, E. L. (2005). 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. Weslaco, TX. CDROM. Retrieved March 12, 2012 from https://jornada.nmsu.edu/bibliography/05-055Proc.pdf.
- Laliberte, A. S., Rango, A., & Herrick, J. (2007). Unmanned aerial vehicles for rangeland mapping and monitoring: a comparison of two systems. In Proceeding of ASPRS 2007 Annual Conference. Tampa, FL. CDROM. Retrieved March 12, 2012 from http://www.asprs.org/a/publications/proceedings/tampa2007/0039.pdf.
- Lamb, J. A., Anderson, J. L., Malzer, G. L., Vetch, J. A., Dowdy, R. H., Onken, D. S., et al. (1995). Perils of monitoring grain yield on-the-go. In P. C. Robert, R. H. Rust, & W. E. Larson (Eds.), Site-specific management for agricultural systems (pp. 87–90). Madison: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.Google Scholar
- Lambert, D., & Lowenberg-Deboer, J. (2000). Precision agriculture profitability review (p. 154). Purdue, USA: Site Specific Management Center.Google Scholar
- Lan, Y., Huang, Y., Martin, D. E., & Hoffmann, W. C. (2009). Development of an airborne remote sensing system for crop pest management: System integration and verification. Transactions of the ASABE, 25, 607–615.Google Scholar
- Long, D. S., Carlson, G. R., & DeGloria, S. D. (1995). Quality of field management maps. In P. C. Robert (Ed.), Proceedings of Site-Specific Management for Agriculture Systems (pp. 251–271). Madison: American Society of Agronomy.Google Scholar
- MacArthur, E. Z., MacArthur, D., & Crane, C. (2005). Use of cooperative unmanned air and ground vehicles for detection and disposal of mines. Proceedings of SPIE-The International Society for Optical Engineering, 5999, 94–101.Google Scholar
- Maldonado-Ramirez, S. L., Schmale, D. G., Shields, E. J., & Bergstrom, G. C. (2005). The relative abundance of viable spores of Gibberella zeae in the planetary boundary layer suggests the role of long-distance transport in regional epidemics of Fusarium head blight. Agricultural and Forest Meteorology, 132, 20–27.CrossRefGoogle Scholar
- McBratney, A. B., Whelan, B. M., & Shatar, T. (1997). 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, pp. 141–160Google Scholar
- Monmonier, M. (2002). Aerial photography at the Agricultural Adjustment Administration: Acreage controls, conservation. Photogrammetric Engineering & Remote Sensing, 68, 1257–1261.Google Scholar
- Murakami, E., Saraiva, A. M., Ribeiro, L. C. M., Cugnasca, C. E., Hirakawa, A. R., & Correa, P. L. P. (2007). An infrastructure for the development of distributed service-oriented information systems for precision agriculture. Computers and Electronics in Agriculture, 58, 37–48.CrossRefGoogle Scholar
- Price, P. (2004). Spreading the PA message. Ground Cover, Issue 51 Grains Research and Development Corporation: Canberra, Australia Capital Territory, Australia.Google Scholar
- Quilter, M. C. (1997). Vegetation monitoring using low altitude, large scale imagery from radio controlled drones. PhD dissertation, Department of Botany and Range Science, Brigham Young University, Provo, UT, USAGoogle Scholar
- Quilter, M. C., & Anderson, V. J. (2000). Low altitude/large scale aerial photographs: A tool for range and resource managers. Rangelands, 22, 13–17.Google Scholar
- Rao, N. R., Garg, P. K., Ghosh, S. K., & Dadhwal, V. K. (2008). Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery. Journal of Agricultural Science, 146, 65–75.Google Scholar
- Robert, P.C. (1996). 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, pp. 596–599.Google Scholar
- Robertson, M., Carberry, P., & Brennan, L. (2007). The economic benefits of precision agriculture: cast studies from Australia grain farms. Retrieved March 12, 2012 from http://www.grdc.com.au/uploads/documents/Economics%20of%20Precision%20agriculture%20Report%20to%20GRDC%20final.pdf.
- Nebiker, S. Annen, A., Scherrer, M., & Oesch, D. (2008). 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. Vol. XXXVII. Part B1., pp. 1193–1200Google Scholar
- Seang, T. P., & Mund, J. (2006). Balloon based geo-referenced digital photo technique: a low cost high-resolution option for developing countries. In Proceedings of XXIII FIG Congress. Munich, Germany. CDROM. Retrieved March 12, 2012 from http://www.fig.net/pub/fig2006/papers/ts73/ts73_02_mund_peng_0425.pdf.
- Sugiura, R., Ishii, K., & Noguchi, N. (2004). Remote sensing technology for field information using an unmanned helicopter. In Proceedings of Automation Technology for Off-road Equipment. Paper No. 701P1004. ASABE, St Joseph, MI, USA.Google Scholar
- Sugiura, R., Noguchi, N., Ishii, K., & Terao, H. (2002). The development of remote sensing system using unmanned helicopter. In Proceedings of Automation Technology for Off-road Equipment, 120–128. Paper No. 701P0502. ASABE, St Joseph, MI, USA.Google Scholar
- Swain, K. C., Thomson, S. J., & 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. Transactions of the ASABE, 53, 21–27.Google Scholar
- Tenkorang, F., & DeBoer, L. (2007). On-farm profitability of remote sensing in agriculture. Journal of Terrestrial Observation, 1, 50–59.Google Scholar
- Tomlins, G. F. (1983). Some considerations in the design of low-cost remotely-piloted aircraft for civil remote sensing applications. The Canadian Surveyor, 37, 157–167.Google Scholar
- Warren, G., & Metternicht, G. (2005). Agricultural applications of high-resolution digital multispectral imagery: Evaluating within-field spatial variability of canola (Brassica napus) in Western Australia. Photogrammetric Engineering and Remote Sensing, 71, 595–602.Google Scholar
- Whipker, L. D., & Akridge, J. T. (2009). Precision agricultural services dealership survey results. Retrieved March 12, 2012 from http://www.agecon.purdue.edu/cab/research_articles/articles/2009_crop_life_precision_report.pdf.
- Wundram, D., & Loffler, J. (2007). Kite aerial photography in high mountain ecosystem research. Grazer Schriften der Geographie und Raumforschung, 43, 15–22.Google Scholar
- Yang, C., Bradford, J. M., & Wiegand, C. L. (2001). Airborne multispectral imagery for mapping variable growing conditions and yields of cotton, grain sorghum, and corn. Transactions of the ASAE, 44, 1983–1994.Google Scholar