Skip to main content
SpringerLink
Log in

Weed mapping in cotton using ground-based sensors and GIS

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Site-specific weed management presupposes the careful monitoring and mapping of weed infestation areas. Cut-edge sensor technologies coupled with geographical information systems (GIS) provide the means for reliable decision-making concerning weed management even in sub-field level. In present research, two different spectral sensing systems were engaged in order to digitally map weed patches as grown in four different cotton fields in Central Greece. The systems used were a set of two Crop Circle multispectral sensors ACS-430 and a digital camera Nikon D300S. The spaces between cotton rows were scanned and photographed with the two systems accordingly. Raw recorded data were stored and analyzed in GIS environment producing spatially interpolated maps of red-edge normalized difference vegetation index (NDVI) and weed cover percentage values. Both mapping approaches were satisfactorily related to weed distribution as occurred in the fields; however, the photographic method tended to underestimate weed populations. Correlation of red-edge NDVI and weed cover values, at the points where photographs were taken, as revealed by Pearson’s correlation coefficient was high (r > 0.83) and statistically significant at the 0.01 level. A first-degree linear equation adequately modeled (R2 > 0.7) the between value pair relations, strengthening the validity of the two methodologies in spatially monitoring weed patches. The methodologies and the technologies used in the study can be used for yearly mapping weed flora in cotton cultivation and potentially constitute a means of rationalizing herbicide application in terms of doses and spatio-temporal decision-making.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  • Berge, T. W., Goldberg, S., Kaspersen, K., & Netland, J. (2012). Towards machine vision based site-specific weed management in cereals. Computers and Electronics in Agriculture, 81, 79–86.

    Article  Google Scholar 

  • Cardina, J., Johnson, G. A., & Sparrow, D. H. (1997). The nature and consequence of weed spatial distribution. Weed Science, 45(3), 364–373.

    CAS  Google Scholar 

  • Castillejo-González, I. L., Peña-Barragán, J. M., Jurado-Expósito, M., Mesas-Carrascosa, F. J., & López-Granados, F. (2014). Evaluation of pixel- and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management. European Journal of Agronomy, 59, 57–66.

    Article  Google Scholar 

  • Clevers, J. G. P. W., & Gitelson, A. A. (2013). Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. International Journal of Applied Earth Observation and Geoinformation, 23, 344–351.

    Article  Google Scholar 

  • Deytieux, V., Nemecek, T., Knuchel, R. F., Gaillard, G., & Munier-Jolain, N. M. (2012). Is integrated weed management efficient for reducing environmental impacts of cropping systems? A case study based on life cycle assessment. European Journal of Agronomy, 36(1), 55–65.

    Article  Google Scholar 

  • Dicke, D., Gerhards, R., Büchse, A., & Hurle, K. (2007). Modeling spatial and temporal dynamics of Chenopodium album L. under the influence of site-specific weed control. Crop Protection, 26(3), 206–211.

    Article  Google Scholar 

  • Doppler, T., Lück, A., Camenzuli, L., Krauss, M., & Stamm, C. (2014). Critical source areas for herbicides can change location depending on rain events. Agriculture, Ecosystems & Environment, 192, 85–94.

    Article  CAS  Google Scholar 

  • Elvidge, C. D., & Chen, Z. (1995). Comparison of broad-band and narrow-band red and near-infrared vegetation indices. Remote Sensing of Environment, 54(1), 38–48.

    Article  Google Scholar 

  • Everitt, J. H., Fletcher, R. S., Elder, H. S., & Yang, C. (2008). Mapping giant salvinia with satellite imagery and image analysis. Environmental Monitoring and Assessment, 139, 35–40.

    Article  CAS  Google Scholar 

  • Gerhards, R., & Christensen, S. (2003). Real-time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed Research, 43, 385–392.

    Article  Google Scholar 

  • Gerhards, R., & Oebel, H. (2006). Practical experiences with a system for site-specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying. Weed Research, 46, 185–193.

    Article  Google Scholar 

  • Gitelson, A., Keydan, G., & Merzlyak, M. (2006). Three-band model for non-invasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 33, 114–120.

    Article  Google Scholar 

  • Hammermeister, A. M. (2016). Organic weed management in perennial fruits. Scientia Horticulturae, 208, 28–42.

    Article  Google Scholar 

  • Heap, I. (2014). Global perspective of herbicide-resistant weeds. Pest Management Science, 70(9), 1306–1315.

    Article  CAS  Google Scholar 

  • Konstantinou, I. K., Hela, D. G., & Albanis, T. A. (2006). The status of pesticide pollution in surface waters (rivers and lakes) of Greece. Part I. Review on occurrence and levels. Environmental Pollution, 141(3), 555–570.

    Article  CAS  Google Scholar 

  • Montull, J. M., Soenderskov, M., Rydahl, P., Boejer, O. M., & Taberner, A. (2014). Four years validation of decision support optimising herbicide dose in cereals under Spanish conditions. Crop Protection, 64, 110–114.

    Article  Google Scholar 

  • Newbold, C. (1975). Herbicides in aquatic systems. Biological Conservation, 7(2), 97–118.

    Article  Google Scholar 

  • Oerke, E.-C. (2006). Crop losses to pests. Journal of Agricultural Science, 144, 31–43.

    Article  Google Scholar 

  • Parsons, D. J., Benjamin, L. R., Clarke, J., Ginsburg, D., Mayes, A., Milne, A. E., & Wilkinson, D. J. (2009). Weed manager—a model-based decision support system for weed management in arable crops. Computers and Electronics in Agriculture, 65(2), 155–167.

    Article  Google Scholar 

  • Pérez-Ortiz, M., Peña, J. M., Gutiérrez, P. A., Torres-Sánchez, J., Hervás-Martínez, C., & López-Granados, F. (2015). A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Applied Soft Computing, 37, 533–544.

    Article  Google Scholar 

  • Santín-Montanyá, M. I., Martín-Lammerding, D., Zambrana, E., & Tenorio, J. L. (2016). Management of weed emergence and weed seed bank in response to different tillage, cropping systems and selected soil properties. Soil and Tillage Research, 161, 38–46.

    Article  Google Scholar 

  • Schuster, I., Nordmeyer, H., & Rath, T. (2007). Comparison of vision-based and manual weed mapping in sugar beet. Biosystems Engineering, 98(1), 17–25.

    Article  Google Scholar 

  • Sims, D. A., & Gamon, J. A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, 337–354.

    Article  Google Scholar 

  • Sønderskov, M., Fritzsche, R., de Mol, F., Gerowitt, B., Goltermann, S., Kierzek, R., Krawczyk, R., Bøjer, O. M., & Rydahl, P. (2015). DSSHerbicide: weed control in winter wheat with a decision support system in three South Baltic regions—field experimental results. Crop Protection, 76, 15–23.

    Article  Google Scholar 

  • Stenberg, B., Viscarra Rossel, R.A., Mouazen, A.M., Wetterlind, J., (2010). Chapter five—visible and near infrared spectroscopy in soil science. Advances in Agronomy, 107, 163–215.

    Article  CAS  Google Scholar 

  • Sui, R., Thomasson, J. A., Hanks, J., & Wooten, J. (2008). Ground-based sensing system for weed mapping in cotton. Computers and Electronics in Agriculture, 60(1), 31–38.

    Article  Google Scholar 

  • Tang, J. L., Chen, X.-Q., Miao, R.-H., & Wang, D. (2016). Weed detection using image processing under different illumination for site-specific areas spraying. Computers and Electronics in Agriculture, 122, 103–111.

    Article  Google Scholar 

  • Tellaeche, A., Burgos-Artizzu, X. P., Pajares, G., & Ribeiro, A. (2008). A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognition, 41(2), 521–530.

    Article  Google Scholar 

  • Van der Meulen, A., & Chauhan, B. S. (2017). A review of weed management in wheat using crop competition. Crop Protection, 95, 38–44.

    Article  Google Scholar 

  • Vasileiadis, V. P., Otto, S., van Dijk, W., Urek, G., Leskovšek, R., Verschwele, A., Furlan, L., & Sattin, M. (2015). On-farm evaluation of integrated weed management tools for maize production in three different agro-environments in Europe: agronomic efficacy, herbicide use reduction, and economic sustainability. European Journal of Agronomy, 63, 71–78.

    Article  Google Scholar 

  • Wackernagel, H. (1998). Ordinary kriging in multivariate geostatistics: an introduction with applications (pp. 83–92). Heidelberg: Springer Berlin online ISBN: 978-3-662-03550-4.

    Google Scholar 

  • Yang, C., & Everitt, J. H. (2010). Mapping three invasive weeds using airborne hyperspectral imagery. Ecological Informatics, 5(5), 429–439.

    Article  Google Scholar 

  • Yang, C.-C., Prasher, S. O., Landry, J.-A., & Ramaswamy, H. S. (2003). Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications. Precision Agriculture, 4(1), 5–18.

    Article  Google Scholar 

  • Ziska, L. H. (2016). The role of climate change and increasing atmospheric carbon dioxide on weed management: herbicide efficacy. Agriculture, Ecosystems & Environment, 231, 304–309.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the contribution of Dr. Dimitrios Taskos, regarding technical issues.

Funding

This work was funded partly by the EU Project LIFE08 ENV/GR/0000570: HydroSense.

Author information

Authors and Affiliations

  1. Department of Phytopathology, Laboratory of Non-Parasitic Diseases, Benaki Phytopathological Institute, 8 St. Delta Str., Kifissia, 145 61, Athens, Greece

    Antonis V. Papadopoulos

  2. Weed Science Laboratory, Benaki Phytopathological Institute, 8 St. Delta Str., Kifissia, 145 61, Athens, Greece

    Vaya Kati, Demosthenis Chachalis & Vasileios Kotoulas

  3. Soil Ecology and Biotechnology Laboratory, Goulandris Natural History Museum, 13 Levidou street, Kifissia, 145 62, Athens, Greece

    Stamatis Stamatiadis

Authors
  1. Antonis V. Papadopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vaya Kati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Demosthenis Chachalis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vasileios Kotoulas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stamatis Stamatiadis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antonis V. Papadopoulos.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Papadopoulos, A.V., Kati, V., Chachalis, D. et al. Weed mapping in cotton using ground-based sensors and GIS. Environ Monit Assess 190, 622 (2018). https://doi.org/10.1007/s10661-018-6991-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-018-6991-x

Keywords

Search

Navigation