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The application of small unmanned aerial systems for precision agriculture: a review

Precision Agriculture volume 13, pages 693–712 (2012)Cite this article

Abstract

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.

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References

  • 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., Aber, S. W., Buster, L., Jensen, W. E., & Sleezer, R. O. (2009). Challenge of infrared kite aerial photography: A digital update. Kansas Academy of Science Transactions, 112, 31–39.

    Article  Google Scholar 

  • Aber, J. S., Marzolff, I., & Ries, J. B. (2010). Small-format aerial photography. Boston: Elsevier. 266.

    Google Scholar 

  • Adrian, A. M., Norwood, S. H., & Mask, P. L. (2005). Producers’ perceptions and attitudes toward precision agriculture technologies. Computer and Electronics in Agriculture, 48, 256–271.

    Article  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/.

  • Aylor, D. E., Boehm, M. T., & Shields, E. J. (2006). Quantifying aerial concentrations of maize pollen in the atmospheric surface layer using remotely-piloted airplanes and Lagrangian stochastic modeling. Journal of Applied Meteorology and Climatology, 45, 1003–1015.

    Article  Google Scholar 

  • Bausch, W. C., & Khosla, R. (2010). QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize. Precision Agriculture, 11, 274–290.

    Article  Google Scholar 

  • Beeri, O., & Peled, A. (2009). Geographical model for precise agriculture monitoring with real-time remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 47–54.

    Article  Google Scholar 

  • Beeri, O., Phillips, R., Carson, P., & Liebig, M. (2005). Alternate satellite models for estimation of sugar beet residue nitrogen credit. Agriculture, Ecosystems & Environment, 107, 21–35.

    Article  Google Scholar 

  • Berni, J. A. J., Zarco-Tejada, P. J., Suarez, L., & Fereres, E. (2009a). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47, 722–738.

    Article  Google Scholar 

  • 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.

  • Blackmore, S. (2000). The interpretation of trends from multiple yield maps. Computers and Electronics in Agriculture, 26, 37–51.

    Article  Google Scholar 

  • Blackmore, S., Godwin, R. J., & Fountas, S. (2003). The analysis of spatial and temporal trends in yield map data over six years. Biosystems Engineering, 84, 455–466.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chandler, J., Fryer, J. G., & Jack, A. (2005). Metric capabilities of low-cost digital cameras for close range surface measurement. The Photogrammetric Record, 20, 12–26.

    Article  Google Scholar 

  • Clevers, J. G. P. W. (1988). The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sensing of Environment, 35, 53–70.

    Article  Google Scholar 

  • Colewell, R. N. (1956). Determining the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia, 26, 223–286.

    Google Scholar 

  • Cook, S. E., & Bramley, R. G. V. (1998). Precision agriculture: Opportunities, benefits and pitfalls of site specific crop management in Australia. Australian Journal of Experimental Agriculture, 38, 753–763.

    Article  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 

  • Diker, K., Heermann, D. F., & Bordahl, M. K. (2004). Frequency analysis of yield for delineating yield response zones. Precision Agriculture, 5, 435–444.

    Article  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).

  • Du, Q., Chang, N. B., Yang, C. H., & Srilakshmi, K. R. (2008). Combination of multispectral remote sensing, variable rate technology and environmental modeling for citrus pest management. Journal of Environmental Management, 86, 14–26.

    PubMed  Article  Google Scholar 

  • 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.

  • Enclona, E. A., Thenkabail, P. S., Celis, D., & Diekmann, J. (2004). Within-field wheat yield prediction from IKONOS data: A new matrix approach. International Journal of Remote Sensing, 25, 377–388.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Flowers, M., Weisz, R., & White, J. G. (2005). Yield-based management zones and grid sampling strategies: Describing soil test and nutrient variability. Agronomy Journal, 97, 968–982.

    Article  Google Scholar 

  • Godwin, R. J., Richards, T. E., Wood, G. A., Welsh, J. P., & Knight, S. M. (2003). An economic analysis of the potential for precision farming in UK cereal production. Biosystems Engineering, 84, 533–545.

    Article  Google Scholar 

  • Gomez, C., Rossel, R. A. V., & McBratney, A. B. (2008). Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma, 146, 403–411.

    Article  CAS  Google Scholar 

  • Gomez-Candon, D., Lopez-Granados, F., Caballero-Novella, J. J., Gomez-Casero, M. T., Jurado-Exposito, M., & Garcia-Torres, L. (2011). Geo-referencing remote images for precision agriculture using artificial terrestrial targets. Precision Agriculture, 12, 876–891.

    Article  Google 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.

    Article  Google 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.

  • 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.

    Article  Google 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.

    Article  Google Scholar 

  • Hardin, P. J., & Hardin, T. J. (2010). Small-scale remotely piloted vehicles in environmental research. Geography Compass, 4, 1297–1311.

    Article  Google Scholar 

  • Hardin, P., & Jackson, M. (2005). An unmanned aerial vehicle for rangeland photography. Rangeland Ecology & Management, 58, 439–442.

    Article  Google Scholar 

  • Hardin, P. J., Jackson, M. W., Anderson, V. J., & Johnson, R. (2007). Detecting squarrose knapweed (Centaurea virgata Lam. Ssp. Squarrosa Gugl.) using a remotely piloted vehicle: A Utah case study. GIScience & Remote Sensing, 44, 203–219.

    Article  Google Scholar 

  • Hardin, P. J., & Jensen, R. R. (2011). Small-scale unmanned aerial vehicles in environmental remote sensing: Challenges and opportunities. GIScience & Remote Sensing, 48, 99–111.

    Article  Google Scholar 

  • Hinkleya, E. A., & Zajkowski, T. (2011). USDA forest service-NASA: Unmanned aerial systems demonstrations-pushing the leading edge in fire mapping. Geocarto International, 26, 103–111.

    Article  Google 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.

  • Hunt, E. R., Cavigelli, M., Daughtry, C. S. T., McMurtrey, J. E., & Walthall, C. L. (2005). Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, 6, 359–378.

    Article  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.

  • 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.

  • Hunt, E. R., 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, 290–305.

    Article  Google Scholar 

  • Inoue, Y., Morinaga, S., & Tomita, A. (2000). A blimp-based remote sensing system for low-altitude monitoring of plant variables: A preliminary experiment for agricultural and ecological applications. International Journal of Remote Sensing, 21, 379–385.

    Article  Google Scholar 

  • 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 

  • Jones, G. P., Pearlstine, L. G., & Percival, H. F. (2006). An assessment of small unmanned aerial vehicles for wildlife research. Wildlife Society Bulletin, 34, 750–758.

    Article  Google Scholar 

  • Kendoul, F., Lara, D., Fantoni-Coichot, I., & Lozano, R. (2007). Real-time nonlinear embedded control for an autonomous quadrotor helicopter. Journal of Guidance Control and Dynamics, 30, 1049–1061.

    Article  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. (2011). Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands. GIScience & Remote Sensing, 48, 4–23.

    Article  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 

  • Lamb, D. W., & Brown, R. B. (2001). Remote-sensing and mapping of weeds in crops. Journal of Agricultural Engineering Research, 78, 117–125.

    Article  Google Scholar 

  • Lamb, D. W., Frazier, P., & Adams, P. (2008). Improving pathways to adoption: Putting the right P’s in precision agriculture. Computers and Electronics in Agriculture, 61, 4–9.

    Article  Google Scholar 

  • Lambert, D., & Lowenberg-Deboer, J. (2000). Precision agriculture profitability review (p. 154). Purdue, USA: Site Specific Management Center.

  • 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 

  • Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Labbe, S., & Barett, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8, 3557–3585.

    Article  Google Scholar 

  • Lelong, C. C. D., Pinet, P. C., & Poilvé, H. (1998). Hyperspectral imaging and stress mapping in agriculture: A case study on wheat in Beauce (France). Remote Sensing of Environment, 66, 179–191.

    Article  Google Scholar 

  • Lewis, G. (2007). 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 (pp. 117–133). Berlin: Springer.

    Chapter  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 

  • Lopez-Lozano, R., Baret, F., de Cortazar-Atauri, I. G., Bertrand, N., & Casterad, M. A. (2009). Optimal geometric configuration and algorithms for LAI indirect estimates under row canopies: The case of vineyards. Agricultural and Forest Meteorology, 149, 1307–1316.

    Article  Google Scholar 

  • Lorenzen, B., & Jensen, A. (1989). Changes in leaf spectral properties induced in barley by cereal powdery mildew. Remote Sensing of Environment, 27, 201–209.

    Article  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.

    Article  Google Scholar 

  • Malthus, T. J., & Maderia, A. C. (1993). High resolution spectroradiometry: Spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing of Environment, 45, 107–116.

    Article  Google Scholar 

  • McBratney, A., Whelan, B., & Ancev, T. (2005). Future directions of precision agriculture. Precision Agriculture, 6, 7–23.

    Article  Google 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. 141–160

  • McNairn, H., & Brisco, B. (2004). The application of C-band polarimetric SAR for agriculture: A review. Canadian Journal of Remote Sensing, 30, 525–542.

    Article  Google Scholar 

  • Monmonier, M. (2002). Aerial photography at the Agricultural Adjustment Administration: Acreage controls, conservation. Photogrammetric Engineering & Remote Sensing, 68, 1257–1261.

    Google Scholar 

  • Moran, M. S., Inoue, Y., & Barnes, E. M. (1997). Opportunities and limitation for image-based remote sensing in precision crop Management. Remote Sensing of Environment, 61, 319–346.

    Article  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.

    Article  Google Scholar 

  • Pena-Barragan, J. M., Lopez-Granados, F., Garcia-Torres, L., Jurado-Exposito, M., de la Orden, M. S., & Garcia-Ferrer, A. (2008). Discriminating cropping systems and agro-environmental measures by remote sensing. Agronomy for Sustainable Development, 28, 355–362.

    Article  Google Scholar 

  • Price, P. (2004). Spreading the PA message. Ground Cover, Issue 51 Grains Research and Development Corporation: Canberra, Australia Capital Territory, Australia.

  • Primicerio, J., Gennaro, S. F. D., Fiorillo, E., Genesio, L., Lugato, E., Matese, A., et al. (2012). A flexible unmanned aerial vehicle for precision agriculture. Precision Agriculture (Online first),. doi:10.1007/s11119-012-9257-6.

    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, USA

  • 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 

  • Quilter, M. C., & Anderson, V. J. (2001). A proposed method for determining shrub utilization using (LA/LS) imagery. Journal of Range Management, 54, 378–381.

    Article  Google Scholar 

  • Rango, A., & Laliberte, A. S. (2010). Impact of flight regulations on effective use of unmanned aerial vehicles for natural resources applications. Journal of Applied Remote Sensing, 4, 043539.

    Article  Google Scholar 

  • Rango, A., Laliberte, A. S., Herrick, J. E., Winters, C., Havstad, K., Steele, C., et al. (2009). Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. Journal of Applied Remote Sensing, 3, 033542.

    Article  Google Scholar 

  • Rao, N. R., Garg, P. K., & Ghosh, S. K. (2007). Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data. Precision Agriculture, 8, 173–185.

    Article  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.

    CAS  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.

  • 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. 1193–1200

  • Schmale, D. G., Dingus, B. R., & Reinholtz, C. (2008). Development and application of an autonomous aerial vehicle for precise aerobiological sampling above agricultural fields. Journal of Field Robotics, 25, 133–147.

    Article  Google Scholar 

  • Scotford, I. M., & Miller, P. C. H. (2005). Applications of spectral reflectance techniques in Northern European cereal production: A review. Biosystems Engineering, 90, 235–250.

    Article  Google 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.

  • 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, 157–169.

    Article  Google Scholar 

  • Shou, L., Jia, L. L., Cui, Z. L., Chen, X. P., & Zhang, F. S. (2007). Using high-resolution satellite imaging to evaluate nitrogen status of winter wheat. Journal of Plant Nutrition, 30, 1669–1680.

    Article  CAS  Google Scholar 

  • Silva, C. B., Vale, S. M. L. R., Pinto, F. A. C., Muller, C. A. S., & Moura, A. D. (2007). The economic feasibility of precision agriculture in Mato Grosso do Sul State, Brazil: A case study. Precision Agriculture, 8, 255–265.

    Article  Google Scholar 

  • Song, X., Wang, J., Huang, W., Liu, L., Yan, G., & Pu, R. (2009). The delineation of agricultural management zones with high resolution remotely sensed data. Precision Agriculture, 10, 471–487.

    Article  Google Scholar 

  • Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 76, 267–275.

    Article  Google Scholar 

  • 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.

  • 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.

  • Sullivan, D. G., Shaw, J. N., & Rickman, D. (2005). IKONOS imagery to estimate surface soil property variability in two Alabama physiographies. Soil Science Society of America Journal, 69, 1789–1798.

    Article  CAS  Google Scholar 

  • Swain, K. C., Jayasuriya, H. P. W., & Salokhe, V. M. (2007). Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption. Journal of Applied Remote Sensing, 1, 013547.

    Article  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 

  • Torbett, J. C., Roberts, R. K., Larson, J. A., & English, B. C. (2008). Perceived improvements in nitrogen fertilizer efficiency from cotton precision farming. Computers and Electronics in Agriculture, 64, 140–148.

    Article  Google Scholar 

  • Vericat, D., Brasington, J., Wheaton, J., & Cowie, M. (2008). Accuracy assessment of aerial photographs acquired using lighter-than-air blimps: Low-cost tools for mapping river corridors. River Research and Applications, 25, 985–1000.

    Article  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.

  • Wu, C., Niu, Z., Tang, Q., & Huang, W. (2008). Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology, 148, 1230–1241.

    Article  Google Scholar 

  • Wu, J. D., Wang, D., & Bauer, M. E. (2007a). Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies. Field Crops Research, 102, 33–42.

    Article  Google Scholar 

  • Wu, J. D., Wang, D., & Rosen, C. J. (2007b). Comparison of petiole nitrate concentrations, SPAD chlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies. Field Crops Research, 101, 96–103.

    Article  Google Scholar 

  • Wundram, D., & Loffler, J. (2007). Kite aerial photography in high mountain ecosystem research. Grazer Schriften der Geographie und Raumforschung, 43, 15–22.

    Google Scholar 

  • Xiang, H., & Tian, L. (2011). Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform. Biosystems Engineering, 108, 104–113.

    Article  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 

  • Yang, C. H., Everitt, J. H., & Bradford, J. M. (2006). Comparison of QuickBird satellite imagery and airborne imagery for mapping grain sorghum yield patterns. Precision Agriculture, 7, 33–44.

    Article  Google Scholar 

  • Zarco-Tejada, P. J., Gonzalez-Dugo, V., & 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 of Environment, 117, 322–337.

    Article  Google Scholar 

  • Zhang, J. H., Wang, K., Bailey, J. S., & Wang, R. C. (2006). Predicting nitrogen status of rice using multispectral data at canopy scale. Pedosphere, 16, 108–117.

    Article  CAS  Google Scholar 

  • Zhao, D. H., Huang, L. M., Li, J. L., & Qi, J. G. (2007). 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, 62, 25–33.

    Article  Google Scholar 

  • Zhou, G. (2009). Near real-time ortho rectification and mosaic of small UAV flow for time-critical event response. IEEE Transactions on Geoscience and Remote Sensing, 47, 739–747.

    Article  Google Scholar 

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Acknowledgments

This research was supported by a Grant (project #920161) provided to John M. Kovacs from the Northern Ontario Heritage Fund Corporation of Canada.

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Authors and Affiliations

  1. Department of Geography and Geology, Algoma University, 1520 Queen street East, Sault Ste. Marie, ON, P6A 2G4, Canada

    Chunhua Zhang

  2. Department of Geography, Nipissing University, North Bay, ON, P1B 8L7, Canada

    John M. Kovacs

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  1. Chunhua Zhang
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  2. John M. Kovacs
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Correspondence to Chunhua Zhang.

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Zhang, C., Kovacs, J.M. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agric 13, 693–712 (2012). https://doi.org/10.1007/s11119-012-9274-5

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Keywords

  • Low altitude remote sensing
  • Unmanned aerial systems (UAS)
  • UAS platforms
  • UAS cameras
  • UAS aviation regulations
  • UAS limitations
  • Farmer participation