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Pre-harvest weed mapping of Cirsium arvense in wheat and barley with off-the-shelf UAVs

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Abstract

The paper describes a procedure to detect green weeds in pre-harvest cereals using images from off-the-shelf UAVs with RGB cameras. All images used to develop and test the detection procedure were from fields infested with Cirsium arvense and in consequence, the procedure is called Thistle Tool. Thistle tool, however, may also detect other green weeds and may also be useful in post-harvest stubble. C. arvense maps may be used for pre-harvest glyphosate spraying in countries allowing this practice or used post-harvest or in the following year because C. arvense patches are relatively stable from one year to the next. The detection procedure exclusively used colour analysis and discriminated green and senescent vegetation without the ability to discriminate between green plant species. Thistle Tool divides images into patches of 1 m2 irrespective of the flight altitude, and calculates a classifier called TopMaxExG used for visual threshold editing. Patches are classified into two categories: with or without green vegetation. When C. arvense was the main contributor to green vegetation in pre-harvest cereals, 92–97% patches were classified correctly under varying environmental conditions with different consumer-grade RGB cameras. With small consumer UAVs, such as Phantom 3 or 4, it is possible to map 10 ha in 20 min at 40 m flight altitude, which corresponds to the duration of one battery. This study has demonstrated that UAV imagery is practically manageable for C. arvense mapping. Barriers to reach the end user and pros-and-cons of using more advance weed detection algorithms are discussed.

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References

  • Andreasen, C., & Stryhn, H. (2008). Increasing weed flora in Danish arable fields and its importance for biodiversity. Weed Research, 48, 1–9.

    Article  Google Scholar 

  • Andreasen, C., & Stryhn, H. (2012). Increasing weed flora in Danish beet, pea and winter barley fields. Crop Protection, 36, 11–17.

    Article  Google Scholar 

  • Ballesteros, R., Ortega, J. F., Hernández, D., & Moreno, M. A. (2014). Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: Application to maize and onion crops of a semi-arid region in Spain. Precision Agriculture, 15, 593–614. https://doi.org/10.1007/s11119-014-9357-6.

    Article  Google Scholar 

  • Castillejo-González, I. L., Pena-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 Quick Bird imagery for site-specific management. European Journal of Agronomy, 59, 57–66.

    Article  Google Scholar 

  • Darwent, A. L., Kirkland, K. J., Baig, M. N., & Lefkovitch, L. (1994). Preharvest applications of glyphosate for Canada thistle (Cirsium arvense) control. Weed Technology, 8, 477–482.

    Article  CAS  Google Scholar 

  • 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. https://doi.org/10.1007/s11119-013-9304-y.

    Article  Google Scholar 

  • Egilsson, J. G. (2014). Detecting weed on images of cereal fields acquired by drones. Master thesis. Department of Computer Science, Faculty of Science, University of Copenhagen, Denmark.

  • Egilsson, J. G., Pedersen, K. S., Olsen, S. I., Nielsen, J., Ntakos, G., & Rasmussen, J., (2015). Pre-harvest assessment of perennial weeds in cereals based on images from unmanned aerial systems (UAS). In 17th European Weed Research Society Symposium “Weed management in changing environments”. Retrieved January 19, 2018, from http://www.ewrs.org/2015meeting.asp.

  • Frasconi, C., Martelloni, L., Fontanelli, M., Raffaelli, M., Marzialetti, P., & Peruzzi, A. (2017). Thermal weed control in photinia x fraseri “red robin” container nurseries. Applied Engineering in Agriculture, 33, 345–356. https://doi.org/10.13031/aea.11529.

    Article  Google Scholar 

  • Garcia-Ruiz, F., Sankaran, S., Maja, J. M., Lee, W. S., Rasmussen, J., & Ehsani, R. (2013). Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Computers and Electronics in Agriculture, 91, 106–115. https://doi.org/10.1016/j.compag.2012.12.002.

    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. https://doi.org/10.1111/j.1365-3180.2006.00504.x.

    Article  Google Scholar 

  • Graglia, E., Melander, B., & Jensen, R. K. (2006). Mechanical and cultural strategies to control Cirsium arvense in organic arable cropping systems. Weed Research, 46, 304–312. https://doi.org/10.1111/j.1365-3180.2006.00514.x.

    Article  Google Scholar 

  • Hamouz, P., Hamouzová, K., Holec, J., & Tyšer, L. (2014). Effect of site-specific weed management in winter crops on yield and weed populations. Plant Soil and Environment, 60, 518–524.

    Article  CAS  Google Scholar 

  • Hamouz, P., Novakova, K., Soukup, J., & Holec, J. (2008). Detection of Cirsium arvense L. in winter wheat using a multispectral imaging system. Journal of Plant Diseases and Protection, Special Issue, 21, 167–170.

    Google Scholar 

  • Hamuda, E., Glavin, M., & Jones, E. (2016). A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture, 125, 184–199.

    Article  Google Scholar 

  • Hoffmann, H., Jensen, R., Thomsen, A., Nieto, H., Rasmussen, J., & Friborg, T. (2016). Crop water stress maps for an entire growing season from visible and thermal UAV imagery. Biogeosciences, 13, 6545–6563. https://doi.org/10.5194/bg-13-6545-2016.

    Article  Google Scholar 

  • Hunt, E. R., Jr., Doraiswamy, P. C., McMurtrey, J. E., Daughtry, C. S. T., Perry, E. M., & Akhmedov, B. (2013). A visible band index for remote sensing leaf chlorophyll content at the canopy scale. International Journal of Applied Earth Observation and Geoinformation, 21, 103–112. https://doi.org/10.1016/j.jag.2012.07.020.

    Article  Google Scholar 

  • Khot, L. R., Sankaran, S., Carter, A., Johnson, D. A., & Cummings, T. F. (2016). UAS imaging-based decision tools for arid winter wheat and irrigated potato production management. International Journal of Remote Sensing, 37, 125–137. https://doi.org/10.1080/01431161.2015.1117685.

    Article  Google Scholar 

  • Laliberte, A. S., & Rango, A. (2009). Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery. IEEE Transactions on Geoscience and Remote Sensing, 47, 761–770. https://doi.org/10.1109/TGRS.2008.2009355.

    Article  Google Scholar 

  • López-Granados, F. (2011). Weed detection for site-specific weed management: mapping and real time approaches. Weed Research, 51, 1–11. https://doi.org/10.1111/j.1365-3180.2010.00829.x.

    Article  Google Scholar 

  • McBratney, A. B., Santos, M. L. M., & Minasny, B. (2003). On digital soil mapping. Geoderma, 117, 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4.

    Article  Google Scholar 

  • O’Sullivan, P. A., Weiss, G. M., & Kossatz, V. C. (1985). Indices of competition for estimating rapeseed yield loss due to Canada thistle. Canadian Journal of Plant Science, 65, 145–149. https://doi.org/10.4141/cjps85-020.

    Article  Google Scholar 

  • Olsen, S. I., Nielsen, J., & Rasmussen, J. (2017). Thistle detection. In P. Sharma & F. M. Bianchi (Eds.), Scandinavian Conference on Image Analysis 2017, Tromsø, Norway, Part II, Lecture Notes in Computer Science. Basel, Switzerland: Springer. https://doi.org/10.1007/978-3-319-59129-2_35.

    Chapter  Google Scholar 

  • Pajares, G. (2015). Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogrammetric Engineering & Remote Sensing, 81, 281–329. https://doi.org/10.14358/PERS.81.4.281.

    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(10), e77151. https://doi.org/10.1371/journal.pone.0077151.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Peña, J. M., Torres-Sánchez, J., Serrano-Pérez, A., de Castro, A. I., & López-Granados, F. (2015). Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors, 15, 5609–5626. https://doi.org/10.3390/s150305609.

    Article  PubMed  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. https://doi.org/10.1016/j.asoc.2015.08.027.

    Article  Google Scholar 

  • Rasmussen, J., Mathiasen, H., & Bibby, B. M. (2010). Timing of post-emergence weed harrowing. Weed Research, 50, 436–446. https://doi.org/10.1111/j.1365-3180.2010.00799.x.

    Article  Google Scholar 

  • Rasmussen, J., Nielsen, J., Garcia-Ruiz, F., Christensen, S., & Streibig, J. C. (2013). Potential uses of small unmanned aircraft systems (UAS) in weed research. Weed Research, 53, 242–248. https://doi.org/10.1111/wre.12026.

    Article  Google Scholar 

  • Rasmussen, J., Nielsen, J., Streibig, J. C., Olsen, S. I., Pedersen, K. S., & Jensen, J. E., (2016). Droner til monitering af flerårigt ukrudt i korn (Drones used for mapping of perennial weeds in cereals). Bekæmpelsesmiddelforskning nr. 165 (p. 68). Retrieved January 19, 2018, from http://mst.dk/service/publikationer/publikationsarkiv/2017/jan/droner-tidsler-pletsproejtning/.

  • Rasmussen, J., Nørremark, M., & Bibby, B. M. (2007). Assessment of leaf cover and crop soil cover in weed harrowing research using digital images. Weed Research, 47, 299–310. https://doi.org/10.1111/j.1365-3180.2007.00565.x.

    Article  Google Scholar 

  • Rasmussen, J., Ntakos, G., Nielsen, J., Svensgaard, J., Poulsen, R. N., & Christensen, S. (2016b). Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? European Journal of Agronomy, 74, 75–92. https://doi.org/10.1016/j.eja.2015.11.026.

    Article  Google Scholar 

  • Sankaran, S., Khot, L. R., & Carter, A. H. (2015a). Field-based crop phenotyping: Multispectral aerial imaging for evaluation of winter wheat emergence and spring stand. Computers and Electronics in Agriculture, 118, 372–379. https://doi.org/10.1016/j.compag.2015.09.001.

    Article  Google Scholar 

  • Sankaran, S., Khot, L. R., Espinoza, C. Z., et al. (2015b). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy, 70, 112–123. https://doi.org/10.1016/j.eja.2015.07.004.

    Article  Google Scholar 

  • Sørensen, R. A., Rasmussen, J., Nielsen, J., & Jørgensen, R. N., (2017). Thistle Detection using Convolutional Neural Networks. In EFITA WCCA 2017 Conference, Montpellier Supagro, Montpellier, France, July 2–6, 2017. Retrieved January 5, 2018, from http://easychair.org/smart-program/EFITA2017/2017-07-03.html#talk:45840.

  • Tiley, G. E. D. (2010). Biological flora of the British Isles: Cirsium arvense (L.) scop. Journal of Ecology, 98, 938–983. https://doi.org/10.1111/j.1365-2745.2010.01678.x.

    Article  Google Scholar 

  • Torres-Sánchez, J., López-Granados, F., De Castro, A. I., & PenñA-Barragán, J. M. (2013). Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS ONE, 8(3), e58210. https://doi.org/10.1371/journal.pone.0058210.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Torres-Sánchez, J., López-Granados, F., & Peña, J. M. (2015). An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture, 114, 43–52. https://doi.org/10.1016/j.compag.2015.03.019.

    Article  Google Scholar 

  • Torres-Sánchez, J., Peña, 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. Computer and Electronics in Agriculture, 103, 104–113. https://doi.org/10.1016/j.compag.2014.02.009.

    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 American Society of Agricultural Engineers (ASAE), 38, 259–269.

    Article  Google Scholar 

  • Wulfsohn, D., & Lagos, I. Z., (2014). The use of a multirotor and high-resolution imaging for precision horticulture in Chile: An industry perspective. Paper No. 1688. In Proceedings of the 12th International Conference on Precision Agriculture. Retrieved November 19, 2018, from https://ispag.org/proceedings/?action=year_abstracts.

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Acknowledgements

This work was funded by The Danish Environmental Protection Agency (J.nr. MST-667-00138 and J.nr. MST-667-00141).

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Rasmussen, J., Nielsen, J., Streibig, J.C. et al. Pre-harvest weed mapping of Cirsium arvense in wheat and barley with off-the-shelf UAVs. Precision Agric 20, 983–999 (2019). https://doi.org/10.1007/s11119-018-09625-7

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