Abstract
Aerial images are useful tools for farmers who practise precision agriculture. The difficulty in taking geo-referenced high-resolution aerial images in a narrow time window considering weather restrictions and the high cost of commercial services are the main drawbacks of these techniques. In this paper, a useful tool to obtain aerial images by using low cost unmanned aerial vehicles (UAV) is presented. The proposed system allows farmers to easily define and execute an aerial image coverage mission by using geographic information system tools in order to obtain mosaics made of high-resolution images. The system computes a complete path for the UAV by taking into account the on-board camera features once the image requirements and area to be covered are defined. This work introduces a full four-step procedure: mission definition, automatic path planning, mission execution and mosaic generation.
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Aggarwal, M., Hua, H., Ahuja, N. (2001). On cosine-fourth and vignetting effects in real lenses. In B. Werner (Ed.), Proceedings of the eighth international conference on computer vision (Vol. 1, pp. 472–479). Vancouver: IEEE Computer Society.
Berni, J., Zarco-Tejada, P., Suarez, L., & Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. Geoscience and Remote Sensing, IEEE Transactions, 47(3), 722–738.
Choset, H. (2001). Coverage for robotics—A survey of recent results. Annals of Mathematics and Artificial Intelligence, 31(1–4), 113–126.
D’Angelo, P. (2012). Radiometric alignment and vignetting calibration. In K. D. Baker et al. (Eds.), Proceedings 5th international conference on computer vision systems. Bielefeld: Applied Computer Science Group.
German, D., D’Angelo, P., Gross, M., & Postle, B. (2007). New methods to project panoramas for practical and aesthetic purposes. In D. W. Cunningham, G. Meyer, L. Neumann, A. Dunning, R. Paricio (Eds.), Proceedings of computational aesthetics, Baff, Canada (pp. 15–22). Aire-la-Ville: Eurographics Association.
Herwitz, S., Johnson, L., Dunagan, S., Higgins, R., Sullivan, D., Zheng, J., et al. (2004). Imaging from an unmanned aerial vehicle: Agricultural surveillance and decision support. Computers and Electronics in Agriculture, 44(1), 49–61.
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(1), 290–305.
Jiao, Y.S.,Wang, X.M., Chen, H., & Li, Y. (2010). Research on the coverage path planning of uavs for polygon areas. In Jing Bing Zhang et al. (Eds.), Proceedings of the 5th IEEE conference on industrial electronics and applications (pp. 1467–1472). Piscataway, New Jersey, USA: IEEE Service Center.
Johnson, L.F., Herwitz, S., Dunagan, S., Lobitz, B., Sullivan, D., & Slye, R. (2003). Collection of ultra high spatial and spectral resolution image data over California vineyards with a small uav. In International Center for Remote Sensing of Environment (Ed.), Proceedings of the international symposium on remote sensing of environment (pp. 221–230). Honolulu, HI: International Center for Remote Sensing of Environment.
Ke, Y., & Sukthankar, R. (2004). Pca-sift: A more distinctive representation for local image descriptors. In proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition (Vol. 2, pp. II–506). Washington: IEEE.
LaValle, S. M. (2006). Planning algorithms. Cambridge: Cambridge University Press.
Lelong, C. C. D., Burger, P., Jubelin, G., Roux, B., Labbe, S., & Baret, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8(5), 3557–3585.
Lowe, D. (1999). Object recognition from local scale-invariant features. In B. Werner (Ed.), In The proceedings of the seventh IEEE international conference on computer vision (Vol. 2, pp. 1150–1157). IEEE. doi:10.1109/ICCV.1999.790288.
Maza, I., & Ollero, A. (2007). Multiple uav cooperative searching operation using polygon area decomposition and efficient coverage algorithms. In R. Alami, R. Chatila, H. Asama (Eds.), Distributed autonomous robotic systems (Vol. 6, pp. 221–230). Tokyo: Springer.
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, XXXVII, 1193–1200.
Philpot, W. (2001). Digital image processing. Ithaca: Cornell University.
Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., & Ng, A. (2009). Ros: an open-source robot operating system. In Proceedings of the IEEE international conference on robotics and automation (workshop on open source software). Kobe.
Senore, F. (2004). Accessed September 2, 2012 from http://wiki.panotools.org/Fluvio_Senore.
Valente, J., Barrientos, A., Cerro, J. D., & Sanz, D. (2011a). A waypoint-based mission planner for farmland coverage with an aerial robot—A precision farming tool. In J. V. Stafford (Ed.), Proceedings of the 8th european conference on precision agriculture (pp. 427–436). Wageningen: Wageningen Academic Publishers.
Valente, J., Sanz, D., Barrientos, A., Cerro, Jd., Ribeiro, A., & Rossi, C. (2011b). An air-ground wireless sensor network for crop monitoring. Sensors, 11(6), 6088–6108.
Xiang, H., & Tian, L. (2011). Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (uav) platform. Biosystems Engineering, 108(2), 104–113.
Acknowledgments
This work have been supported by the Robotics and Cybernetics Research Group at Technique University of Madrid (Spain), and funded under the projects “ROTOS: Multi-Robot system for outdoor infrastructures protection”, sponsored by The Spanish Ministry of Education and Science (DPI2010-17998), and ‘Robot Fleets for Highly Effective Agriculture and Forestry Management’, (RHEA) sponsored by the European Commission’s Seventh Framework Programme (NMP-CP-IP 245986-2 RHEA). The authors would like to thank all the project partners: Agencia Estatal Consejo Superior de Investigaciones Cientificas-CSIC (Centro de Automàtica y Robotica, Instituto de Ciencias Agrarias, Instituto de Agricultura Sostenible), CogVisGmbH, Forschungszentrum Telekommunikation Wien Ltd., CyberboticsLtd, Università di Pisa, Universidad Complutense de Madrid, Tropical, Soluciones Agricolas de Precision S.L., Universidad Politécnica de Madrid-UPM (ETS Ingenieros Agronomos, ETS Ingenieros Industriales), AirRobotGmbH& Co. KG, Universita degli Studi di Firenze, Centre National du MachinismeAgricole, du Gènie Rural, des Eaux et des Forets -CEMAGREF, CNH Belgium NV, CNH France SA, Bluebotics S.A. y CM Srl.
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Valente, J., Sanz, D., Del Cerro, J. et al. Near-optimal coverage trajectories for image mosaicing using a mini quad-rotor over irregular-shaped fields. Precision Agric 14, 115–132 (2013). https://doi.org/10.1007/s11119-012-9287-0
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DOI: https://doi.org/10.1007/s11119-012-9287-0