Precision Agriculture

, Volume 15, Issue 1, pp 44–56 | Cite as

Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat

  • D. Gómez-Candón
  • A. I. De Castro
  • F. López-Granados
Article

Abstract

High spatial resolution images taken by unmanned aerial vehicles (UAVs) have been shown to have the potential for monitoring agronomic and environmental variables. However, it is necessary to capture a large number of overlapped images that must be mosaicked together to produce a single and accurate ortho-image (also called an ortho-mosaicked image) representing the entire area of work. Thus, ground control points (GCPs) must be acquired to ensure the accuracy of the mosaicking process. UAV ortho-mosaics are becoming an important tool for early site-specific weed management (ESSWM), as the discrimination of small plants (crop and weeds) at early growth stages is subject to serious limitations using other types of remote platforms with coarse spatial resolutions, such as satellite or conventional aerial platforms. Small changes in flight altitude are crucial for low-altitude image acquisition because these variations can cause important differences in the spatial resolution of the ortho-images. Furthermore, a decrease of flying altitude reduces the area covered by each single overlapped image, which implies an increase of both the sequence of images and the complexity of the image mosaicking procedure to obtain an ortho-image covering the whole study area. This study was carried out in two wheat fields naturally infested by broad-leaved and grass weeds at a very early phenological stage. The geometric accuracy differences and crop line alignment among ortho-mosaics created from UAV image series were investigated while taking into account three different flight altitudes (30, 60 and 100 m) and a number of GCPs (from 11 to 45). The results did not show relevant differences in geo-referencing accuracy on the interval of altitudes studied. Similarly, the increase of the number of GCPs did not imply a relevant increase of geo-referencing accuracy. Therefore, the most important parameter to consider when choosing the flying altitude is the ortho-image spatial resolution required rather than the geo-referencing accuracy. Regarding the crop mis-alignment, the results showed that the overall errors were less than twice the spatial resolution, which did not break the crop line continuity at the studied spatial resolutions (pixels from 7.4 to 24.7 mm for 30, 60 and 100 m flying altitudes respectively) on the studied crop (early wheat). The results lead to the conclusion that a UAV flying at a range of 30 to 100 m altitude and using a moderate number of GCPs is able to generate ultra-high spatial resolution ortho-imagesortho-images with the geo-referencing accuracy required to map small weeds in wheat at a very early phenological stage. This is an ambitious agronomic objective that is being studied in a wide research program whose global aim is to create broad-leaved and grass weed maps in wheat crops for an effective ESSWM.

Keywords

Crop line alignment Image mosaicking Overlapping Weed seedling discrimination Early site-specific weed management 

Notes

Acknowledgments

This research was partly financed by the AGL2011-30442-CO2-01 Project (Spanish Ministry of Economy and Competition, FEDER Funds) and the RHEA Project (ref.: NMP-CP-IP 245986-2, EU-7th Frame Program). The research by Ms. de Castro was co-financed by CSIC and FEDER funds (JAE-Pre Program). The authors thank Mr. Caballero-Novella for his very helpful software support and field-work assistance and Dr. Peña-Barragán and Mr J. Torres-Sánchez for their helpful assistance during the field work. Authors thank Mr. Íñigo de Arteaga y Martín and Mr. Iván de Arteaga del Alcázar for allowing developing our field work in La Monclova farm.

References

  1. Blackmore, S. (1996). An information system for precision farming. British Crop Protection Council Pests and Diseases, Brighton, UK, 3,1207–1214.Google Scholar
  2. De Castro, A. I., Jurado-Expósito, M., Peña-Barragán, J. M., & López-Granados, F. (2012). Airborne multi-spectral imagery for zapping cruciferous weeds in cereal and legume crops. Precision Agriculture, 13, 302–321.CrossRefGoogle Scholar
  3. De Castro, A. I., López-Granados, F., & Jurado-Expósito, M. (2013). Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control. Precision Agriculture, 14, 392–413.CrossRefGoogle Scholar
  4. ERDAS Inc. (1999). RMS error (pp. 362–365). In: ERDAS field guide (5th edn, 672 pp). Atlanta, GA: ERDAS Inc.Google Scholar
  5. Gómez-Candón, D., López-Granados, F., Caballero-Novella, J. J., García-Ferrer, A., Peña-Barragán, J. M., Jurado-Expósito, M., et al. (2012). Sectioning remote imagery for characterization of Avena sterilis infestations part A: Weed abundance. Precision Agriculture, 13, 322–336.CrossRefGoogle Scholar
  6. Gómez-Candón, D., López-Granados, F., Caballero-Novella, J. J., Gómez-Casero, M. T., Jurado-Expósito, M., & García-Torres, L. (2011). Geo-referencing remote images for precision agriculture using artificial terrestrial targets. Precision Agriculture, 12, 876–891.CrossRefGoogle Scholar
  7. 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.CrossRefGoogle Scholar
  8. Jurado-Expósito, M., López-Granados, F., García-Torres, L., García-Ferrer, A., Sánchez de la Orden, M., & Atenciano, S. (2003). Multi-species weed spatial variability and site-specific management maps in cultivated sunflower. Weed Science, 51, 319–328.CrossRefGoogle Scholar
  9. Jurado-Expósito, M., López-Granados, F., González-Andújar, J. L., & García-Torres, L. (2004). Spatial and temporal analysis of Convolvulus arvensis L. populations over four growing seasons. European Journal of Agronomy, 21, 287–296.CrossRefGoogle Scholar
  10. Kropff, M. J., Wallinga, J., & Lotz, L. A. P. (1997). Crop-weed interactions and weed population dynamics: Current knowledge and new research targets. In Proceedings of the 10th Symposium of the European Weed Research Society (EWRS) (pp. 41–48).Google Scholar
  11. Laliberte, A. S., Herrick, J. E., Rango, A., & Winters, C. (2010). Acquisition, orthorectification, and object-based classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring. Photogrammetric Engineering and Remote Sensing, 76(6), 661–672.CrossRefGoogle Scholar
  12. Leica Geosystems. (2006). Leica Photogrammetry Suite Project Manager. Leica Geosystems Geospatial Imaging, LLC, Norcross, GA, (434 pp.) Retrieved July 25, 2013, from http://pdf.ebooks6.com/Leica-Photogrammetry-Suite-pdf.pdf.
  13. Lin, Y., & Medioni, G. (2007). Map-enhanced UAV image sequence registration and synchronization of multiple image sequences. In IEEE Conference on Computer Vision and Pattern Recognition (Vol 1, pp. 3272–3278).Google Scholar
  14. López-Granados, F. (2011). Weed detection for site-specific weed management: Mapping and real-time approaches. Weed Research, 51, 1–11.CrossRefGoogle Scholar
  15. López-Granados, F., Jurado-Expósito, M., Peña-Barragán, J. M., & García-Torres, L. (2006). Using remote sensing for identification of late-season grass weed patches in wheat. Weed Science, 54, 346–353.Google Scholar
  16. Lunetta, R. S., Congalton, R. G., Fenstermaker, L. K., Jensen, J. R., Mcgwire, K. C., & Tinney, L. R. (1991). Remote sensing and geographic information system data integration: Error sources and research issues. Photogrammetric Engineering and Remote Sensing, 57, 677–687.Google Scholar
  17. Mostafa, M. M. R., Hutton, J., & Lithopoulos, E. (2001). Airborne direct georeferencing of frame imagery: An error budget. In Proceedings of the 3rd International Symposium on Mobile Mapping Technology. Cairo, Egypt.Google Scholar
  18. Peña-Barragan, J. M., Kelly, M., de-Castro, A. I. & Lopez-Granados, F. (2012). Object-based approach for crow row characterization in UAV images for site-specific weed management. In: R. Queiroz, G. A. Da Cost, M. de Almeida, L. M. García & H. J. Kux (Eds.), In 4th International Conference on Geographic Object-Based Image Analysis (GEOBIA 2012) (pp. 426–430).Google Scholar
  19. Rango, A., Laliberte, A. S., Steele, C., Herrick, J. E., Bestelmeyer, B., Schmugge, T., et al. (2006). Using unmanned aerial vehicles for rangelands: Current applications and future potentials. Environmental Practice, 8, 159–168.CrossRefGoogle Scholar
  20. Schmale, D. G., Dingus, B. R., & Reinholtz, C. (2008). Development and application of an autonomous unmanned aerial vehicle for precise aerobiological sampling above agricultural fields. Journal of Field Robotics, 25, 133–147.CrossRefGoogle Scholar
  21. Slama, C. C., Theurer, C., & Henriksen, S. W. (1980). Manual of photogrammetry (4th ed.). Bethesda, MD: The American Society for Photogrammetry and Remote Sensing.Google Scholar
  22. Thorp, K. R., & Tian, L. F. (2004). A review on remote sensing of weeds in agriculture. Precision Agriculture, 5, 477–508.CrossRefGoogle Scholar
  23. Torres-Sánchez, J., López-Granados, F., de Castro-Megías, A. I., & Peña-Barragán, J. M. (2013). Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS One,. doi: 10.1371/journal.pone.0058210,e58210.Google Scholar
  24. Vericat, D., Brasington, J., Wheaton, J., & Cowie, M. (2009). 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.CrossRefGoogle Scholar
  25. Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13, 693–712.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • D. Gómez-Candón
    • 1
  • A. I. De Castro
    • 1
  • F. López-Granados
    • 1
  1. 1.Institute for Sustainable AgricultureCSICCórdobaSpain

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