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Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds

Precision Agriculture volume 17, pages 183–199 (2016)Cite this article

Abstract

Site-specific weed management is defined as the application of customised control treatments only where weeds are located within the crop-field by using adequate herbicide according to weed emergence. The aim of the study was to generate georeferenced weed seedling infestation maps in two sunflower fields by analysing overlapping aerial images of the visible and near-infrared spectrum (using visible or multi-spectral cameras) collected by an unmanned aerial vehicle (UAV) flying at 30 and 60 m altitudes. The main tasks focused on the configuration and evaluation of the UAV and its sensors for image acquisition and ortho-mosaicking, as well as the development of an automatic and robust image analysis procedure for weed seedling mapping used to design a site-specific weed management program. The control strategy was based on seven weed thresholds with 2.5 steps of increasing ratio from 0 % (herbicide must be applied just when there is presence or absence of weed) to 15 % (herbicide applied when weed coverage >15 %). As a first step of the imagery analysis, sunflower rows were correctly matched to the ortho-mosaicked imagery, which allowed accurate image analysis using object-based image analysis [object-based-image-analysis (OBIA) methods]. The OBIA algorithm developed for weed seedling mapping with ortho-mosaicked imagery successfully classified the sunflower-rows with 100 % accuracy in both fields for all flight altitudes and camera types, indicating the computational and analytical robustness of OBIA. Regarding weed discrimination, high accuracies were observed using the multi-spectral camera at any flight altitude, with the highest (approximately 100 %) being those recorded for the 15 % weed threshold, although satisfactory results from 2.5 to 5 % thresholds were also observed, with accuracies higher than 85 % for both field 1 and field 2. The lowest accuracies (ranging from 50 to 60 %) were achieved with the visible camera at all flight altitudes and 0 % weed threshold. Herbicide savings were relevant in both fields, although they were higher in field 2 due to less weed infestation. These herbicide savings varied according to the different scenarios studied. For example, in field 2 and at 30 m flight altitude and using the multi-spectral camera, a range of 23–3 % of the field (i.e., 77 and 97 % of area) could be treated for 0–15 % weed thresholds. The OBIA procedure computed multiple data which permitted calculation of herbicide requirements for timely and site-specific post-emergence weed seedling management.

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References

  • Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 2–16.

    Article  Google Scholar 

  • Carranza, P., Saavedra, M., & Gacría-Torres, L. (1995). Ridolfia segetum Moris. competition with sunflower (Helianthus annuus L.). Weed Research, 35, 369–375.

    Article  Google Scholar 

  • Castro-Tendero, A. J., & García-Torres, L. (1995). SEMAGI—an expert system for weed control decision making in sunflowers. Crop Protection, 14, 543–548.

    Article  Google Scholar 

  • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.

    Article  Google Scholar 

  • Czapar, G. F., Curry, M. P., & Wax, L. M. (1997). Grower acceptance of economic thresholds for weed management in Illinois. Weed Technology, 11, 828–831.

    Google Scholar 

  • De Castro, A. I., Jurado-Expósito, M., Peña-Barragán, J. M., & López-Granados, F. (2012). Airborne multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops. Precision Agriculture, 13, 302–321.

    Article  Google Scholar 

  • De Castro, A. I., López-Granados, F., Peña-Barragán, J. M., & Jurado-Expósito, M. (2013). Broad-scale cruciferous weed patches classification in winter wheat using QuickBird imagery for in-season site-specific control. Precision Agriculture, 14, 392–417.

    Article  Google Scholar 

  • FAO (2015). http://faostat3.fao.org/faostat-gateway/go/to/home/E. Accessed 16 June 2014.

  • García-Torres, L., López-Granados, F., & Castejón-Muñoz, M. (1994). Preemergence herbicides for the control of broomrape (Orobanche cernua Loefl.) in sunflower (Helianthus annuus L.). Weed Research, 34, 395–402.

    Article  Google Scholar 

  • Gibson, K. D., Dirks, R., Medlin, C. R., & Johnston, L. (2004). Detection of weed species in soybean using multispectral digital images. Weed Technology, 18, 742–749.

    Article  Google Scholar 

  • Gómez-Candón, D., De Castro, A. I., & López-Granados, F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes. Precision Agriculture, 15, 44–56.

    Article  Google Scholar 

  • Gutiérrez-Peña, P. A., López-Granados, F., Peña-Barragán, J. M., Jurado-Expósito, M., Gómez-Casero, M. T., & Hervás-Martínez, C. (2008). Mapping sunflower yield as affected by Ridolfia segetum patches and elevation by applying evolutionary product unit neural networks to remote sensed data. Computers and Electronics in Agriculture, 60, 122–132.

    Article  Google Scholar 

  • Haarbrink, R. B., & Eisenbeiss, H. (2008). Accurate DSM production from unmanned helicopter systems. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII(Part B1), 1259–1264.

    Google Scholar 

  • Horizon (2020) http://ec.europa.eu/programmes/horizon2020/. Accessed 16 June 2015.

  • Hunt, E. R, Jr., 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 

  • 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.

    Article  Google Scholar 

  • Jurado-Expósito, M., López-Granados, F., González-Andújar, J. L., & García-Torres, L. (2005). Characterizing population rate of Convolvulus arvensis in wheat-sunflower no-tillage systems. Crop Science, 45, 2106–2112.

    Article  Google Scholar 

  • Lambers, K., Eisenbeiss, H., Sauerbier, M., Kupferschmidt, D., Gaisecker, Th, Sotoodeh, S., et al. (2007). Combining photogrammetry and laser scanning for the recording andmodelling of the late intermediate period site of Pinchango Alto, Palpa, Peru. Journal of Archaeological Science, 34, 1702–1712.

    Article  Google Scholar 

  • Li, Ch-ch, Zhang, G.-S., Lei, T.-J., & Gong, A.-D. (2011). Quick image-processing method of UAV without control points data in earthquake disaster area. Transactions Nonferrous Metals Society of China, 21, s523–s528.

    Article  Google Scholar 

  • Longchamps, L., Panneton, B., Simard, M. J., & Leroux, G. D. (2014). An imagery-based weed cover threshold established using expert knowledge. Weed Science, 62, 177–185.

    CAS  Article  Google Scholar 

  • López-Granados, F. (2011). Weed detection for site-specific weed management: Mapping and real-time approaches. Weed Research, 51, 1–11.

    Article  Google Scholar 

  • MAGRAMA (2015). Ministerio Agricultura, Alimentación y Medioambiente. http://www.magrama.gob.es/es/estadistica/temas/default.aspx. Accessed 16 June 2015 (in Spanish).

  • Meier, U. (2001). Growth stages of mono- and dicotyledonous plants. BB Monograph. Federal Biological Research Centre for Agriculture and Forestry. http://www.jki.bund.de/fileadmin/dam_uploads/_veroeff/bbch/BBCH-Skala_englisch.pdf. Accessed 16 June 2015.

  • Molinero-Ruiz, L., García-Carneros, A. B., Collado-Romero, M., Rarancius, S., Domínguez, J., & Melero-Vara, J. (2014). Pathogenic and molecular diversity in highly virulent populations of the parasitic weed Orobanche cumana (sunflower broomrape) from Europe. Weed Research, 54, 87–98.

    Article  Google Scholar 

  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE, Man, and Cybernetics Society, 9, 62–66.

    Article  Google Scholar 

  • Peña, J. M., Torres-Sánchez, J., de Castro, A. I., Kelly, M., & López-Granados, F. (2013). Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS One, 8, e77151.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pérez-Ruiz, M., Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C., Peruzzi, A., Vieri, M., et al. (2015). Highlights and preliminary results for autonomous crop protection. Computers and Electronics in Agriculture, 110, 150–161.

    Article  Google Scholar 

  • Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings of the Earth Resources Technology Satellite Symposium NASA SP-351, (vol 1, pp. 309–317). Washington, DC.

  • Swanton, C. J., Weaver, S., Cowan, P., Van Acker, R., Deen, W., & Shreshta, A. (1999). Weed thresholds: Theory and applicability. Journal of Crop Production, 2, 9–29.

    Article  Google Scholar 

  • Thomlinson, J. R., Bolstad, P. V., & Cohen, W. B. (1999). Coordinating methodologies for scaling landcover classification from site-specific to global: Steps toward validating global maps products. Remote Sensing of Environment, 70, 16–28.

    Article  Google Scholar 

  • Torres-Sánchez, J., López-Granados, F., de Castro, A. I., & Peña-Barragán, J. M. (2013). Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS One, 8, e58210.

    Article  PubMed  PubMed Central  Google Scholar 

  • Torres-Sánchez, J., López-Granados, F., & Peña-Barragán, J. M. (2015). An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous. Computers and Electronics in Agriculture, 114, 43–52.

    Article  Google Scholar 

  • Torres-Sánchez, J., Peña-Barragán, J. M., de Castro, A. I., & López-Granados, F. (2014). Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture, 103, 104–113.

    Article  Google Scholar 

  • Woebbecke, D. M., Meyer, G. E., Von Bargen, K., & Mortensen, D. A. (1995). Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, 38(259–269), 28.

    Google Scholar 

  • Zhang, C., & Kovacs, J. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13, 693–712.

    CAS  Article  Google Scholar 

Download references

Acknowledgments

This research was partially financed by the RECUPERA 2020 Project (Agreement CSIC-Spanish MINECO and EU-FEDER funds). Research of Mr. Torres-Sánchez, Dr. de Castro and Dr. Peña was financed by the FPI, the JAE-predoc (CSIC-FEDER) and Ramón y Cajal Programs, respectively.

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Authors and Affiliations

  1. Institute for Sustainable Agriculture, CSIC, P.O. Box 4084, 14080, Córdoba, Spain

    Francisca López-Granados, Jorge Torres-Sánchez, Angélica Serrano-Pérez, Ana I. de Castro & José-Manuel Peña

  2. Department of Graphic Engineering and Geomatics, University of Cordoba, Campus de Rabanales, 14071, Córdoba, Spain

    Fco.-Javier Mesas-Carrascosa

Authors
  1. Francisca López-Granados
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  2. Jorge Torres-Sánchez
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  3. Angélica Serrano-Pérez
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  4. Ana I. de Castro
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  5. Fco.-Javier Mesas-Carrascosa
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  6. José-Manuel Peña
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Correspondence to Francisca López-Granados.

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López-Granados, F., Torres-Sánchez, J., Serrano-Pérez, A. et al. Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precision Agric 17, 183–199 (2016). https://doi.org/10.1007/s11119-015-9415-8

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  • DOI: https://doi.org/10.1007/s11119-015-9415-8

Keywords

  • Site-specific weed management (SSWM)
  • Mosaicked imagery
  • object-based-image-analysis (OBIA)
  • Remote sensing
  • Unmanned aerial vehicle (UAV)
  • Weed threshold