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Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize

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Abstract

The capability of images acquired from unmanned aerial vehicles (UAVs), with multispectral cameras, to detect weed patches, should be tested in operational situations of site-specific weed management. In this regard, different post-emergence herbicide application strategies were evaluated on a total of four silage maize fields in Central Italy. The treatments compared were uniform blanket application, patch spraying according to the application map and an untreated control (the latter treatment only in the second year). Images were acquired a few weeks after maize emergence and were processed into application (i.e. prescription) maps. The accuracy of prescription maps was evaluated on the basis of ground-truth data. Maize and weed biomass data collected at end of the growing season were used to assess differences among the herbicide application strategies. Results showed no differences between uniform and patch spraying treatments for silage maize biomass in the two fields of the first year. In the second year, maize biomass differences were observed between the untreated control and the other two treatments. In terms of weed biomass there were no differences among treatments, for three out of four fields. The use of UAV image data captured early post-emergence in maize lead to a decrease in the use of herbicide without negative consequences in terms of crop yield and, at the same time, increased the silage biomass production as compared to non-treated area. The saving of herbicide calculated in terms of untreated area ranged between 14 and 39.2 % for patch spraying as compared to a uniform blanket application, and saved between 16 and 45 € ha−1.

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References

  • 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(6), 593–614. doi:10.1007/s11119-014-9357-6.

    Article  Google Scholar 

  • Blanco-Moreno, J. M., Chamorro, L., & Sans, F. X. (2006). Spatial and temporal patterns of Lolium rigidum-Avena sterilis mixed populations in a cereal field. Weed Research, 46(3), 207–218. doi:10.1111/j.1365-3180.2006.00493.x.

    Article  Google Scholar 

  • Brus, D. J., & de Gruijter, J. J. (1997). Random sampling or geostatistical modelling? Choosing between design-based and model-based sampling strategies for soil (with discussion). Geoderma, 80(1–2), 1–44. doi:10.1016/S0016-7061(97)00072-4.

    Article  Google Scholar 

  • Cardina, J., Johnson, G. A., & Sparrow, D. H. (1997). Nature and consequence of weed spatial distribution. Weed science. Retrieved from http://agris.fao.org/agris-search/search.do?recordID=US201302901049

  • Christensen, S., Sǿgaard, H. T., Kudsk, P., Nørremark, M., Lund, I., Nadimi, E. S., et al. (2009). Site-specific weed control technologies. Weed Research, 49(3), 233–241. doi:10.1111/j.1365-3180.2009.00696.x.

    Article  Google Scholar 

  • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. doi:10.1016/0034-4257(91)90048-B.

    Article  Google Scholar 

  • Costantini, E.A.C., L’Abate, G., Barbetti, R., Fantappié, M., Lorenzetti, R. and Magini, S. (2012). Carta dei suoli d’Italia, scala 1:1.000.000 (Soil map of Italy, scale 1:1.000.000). Consiglio per ricerca e la sperimentazione in agricoltura, S.EL.CA. Florence/Firenze, Italy (ISBN: 978-88-97002-02-4). http://www.soilmaps.it/. Accessed 29 May 2016.

  • 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(4), 392–413. doi:10.1007/s11119-013-9304-y.

    Article  Google Scholar 

  • Demarez, V., Duthoit, S., Baret, F., Weiss, M., & Dedieu, G. (2008). Estimation of leaf area and clumping indexes of crops with hemispherical photographs. Agricultural and Forest Meteorology, 148(4), 644–655. doi:10.1016/j.agrformet.2007.11.015.

    Article  Google Scholar 

  • Gerhards, R. (2013). Site-specific weed control. In H. J. Heege (Ed.), Precision in crop farming: Site specific concepts and sensing methods (pp. 273–294). Kiel: Springer.

    Chapter  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(6), 385–392. doi:10.1046/j.1365-3180.2003.00349.x.

    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(3), 185–193. doi:10.1111/j.1365-3180.2006.00504.x.

    Article  Google Scholar 

  • Gerhards, R., Wyse-Pester, D. Y., Mortensen, D., & Johnson, G. A. (1997). Characterizing spatial stability of weed populations using interpolated maps. Weed Science, 45(1). http://experts.umn.edu/en/publications/characterizing-spatial-stability-of-weed-populations-using-interpolated-maps(85455155-1538-459c-a2c2-1961b609e288).html

  • Goel, P., Prasher, S., Landry, J., Patel, R., Bonnell, R., Viau, A., et al. (2003). Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and Electronics in Agriculture, 38(2), 99–124. doi:10.1016/S0168-1699(02)00138-2.

    Article  Google Scholar 

  • Gray, C. J., Shaw, D. R., & Bruce, L. M. (2009). Utility of hyperspectral reflectance for differentiating soybean (Glycine max) and six weed species. Weed Technology, 23, 108–119.

    Article  Google Scholar 

  • Gutiérrez, 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(2), 122–132. doi:10.1016/j.compag.2007.07.011.

    Article  Google Scholar 

  • Hamouz, P., Hamouzová, K., Holec, J., & Tyšer, L. (2013). Impact of site-specific weed management on herbicide savings and winter wheat yield. Plant, Soil and Environment, 59(3), 101–107. Retrieved from http://www.cabdirect.org/abstracts/20133173258.html

  • Hamouz, P., Hamouzova, K., Tyser, L., & Holec, J. (2014). Effect of Site-Specific Weed Management in Winter Crops on Yield and Weed Populations. Plant, Soil and Environment. Retrieved from http://www.i-scholar.in/index.php/PSECAAS/article/view/60766

  • Hess, M., Barralis, G., Bleiholder, H., Buhr, L., Eggers, T., Hack, H., et al. (1997). Use of the extended BBCH scale—general for the descriptions of the growth stages of mono- and dicotyledonous weed species. Weed Research, 6, 433–441.

    Article  Google Scholar 

  • Hsu, C.-W., Chang, C.-C., and Lin, C.-J. (2007). A practical guide to support vector classification. National Taiwan University. Retrieved from: http://ntu.csie.org/~cjlin/papers/guide/guide.pdf. Accessed 29 June 2016

  • IUSS Working Group WRB, 2015. World Reference Base for Soil Resources 2014, update 2015 International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106. Rome: FAO

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

    Article  Google Scholar 

  • López-Granados, F., Torres-Sánchez, J., Serrano-Pérez, A., de Castro, A. I., Mesas-Carrascosa, F.-J., et al. (2015). Early season weed mapping in sunflower using UAV technology: Variability of herbicide treatment maps against weed thresholds. Precision Agriculture. doi:10.1007/s11119-015-9415-8.

    Google Scholar 

  • Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(8), 1778–1790. doi:10.1109/TGRS.2004.831865.

    Article  Google Scholar 

  • Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1–2), 17–23. doi:10.1093/biomet/37.1-2.17.

    Article  CAS  PubMed  Google Scholar 

  • Nordmeyer, H. (2009). Spatial and temporal dynamics of Apera spica-venti seedling populations. Crop Protection, 28(10), 831–837. doi:10.1016/j.cropro.2009.06.006.

    Article  Google Scholar 

  • Oerke, E.-C. (2006). Crop losses to pests. The Journal of Agricultural Science, 144(01), 31. doi:10.1017/S0021859605005708.

    Article  Google Scholar 

  • Pelosi, F., Castaldi, F., & Casa, R. (2015). Operational unmanned aerial vehicle assisted post-emergence herbicide patch spraying in maize: A field study. In Precision Agriculture 2015 - Papers Presented at the 10th European Conference on Precision Agriculture, ECPA 2015 (pp. 159–166). Wageningen Academic Publishers. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84947283203&partnerID=tZOtx3y1

  • 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. doi:10.1371/journal.pone.0077151.

    Article  PubMed  PubMed Central  Google Scholar 

  • Perry, N. H., Lutman, P. J. W., Miller, P. C. H., & Wheeler, H. C. (2001). A map-based system for patch spraying weeds - weed mapping. In The BCPC Conference: Weeds, 2001, Volume 1 and Volume 2. Proceedings of an international conference held at the Brighton Hilton Metropole Hotel, Brighton, UK, 12-15 November 2001. (pp. 841–846). British Crop Protection Council. Retrieved from http://www.cabdirect.org/abstracts/20023048052.html

  • Pollice, A., & Bilancia, M. (2002). Kriging with mixed effects models (pp. 405–429). LXII: Statistica.

    Google Scholar 

  • Pringle, M. J., Bishop, T. F. A., Lark, R. M., & Whelan, B. M. (2010). The Analysis of Spatial Experiments. In M. A. Oliver (Ed.), Geostatistical applications for precision agriculture (pp. 243–269). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Rango, A., Laliberte, A., 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(03), 159–168. doi:10.1017/S1466046606060224.

    Article  Google Scholar 

  • Thorp, K. R., & Tian, L. F. (2004). A review on remote sensing of weeds in agriculture. Precision Agriculture, 5(5), 477–508. doi:10.1007/s11119-004-5321-1.

    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. Computers and Electronics in Agriculture, 103, 104–113. doi:10.1016/j.compag.2014.02.009.

    Article  Google Scholar 

  • Wallinga, J., Groeneveld, R. M. W., & Lotz, L. A. P. (1998). Measures that describe weed spatial patterns at different levels of resolution and their applications for patch spraying of weeds. Weed Research, 38(5), 351–359. doi:10.1046/j.1365-3180.1998.00106.x.

    Article  Google Scholar 

  • Wiles, L. J. (2009). Beyond patch spraying: site-specific weed management with several herbicides. Precision Agriculture, 10(3), 277–290. doi:10.1007/s11119-008-9097-6.

    Article  Google Scholar 

  • Williams, M. M, I. I., Gerhards, R., & Mortensen, D. A. (2000). Two-year weed seedling population responses to a post-emergent method of site-specific weed management. Precision Agriculture, 2(3), 247–263. doi:10.1023/A:1011886722418.

    Article  Google Scholar 

  • Younan, N. H., King, R. L., & Bennett, H. H, Jr. (2004). Classification of hyperspectral data: A comparative study. Precision Agriculture, 5(1), 41–53.

    Article  Google Scholar 

  • Zanin, G., Berti, A., & Toniolo, L. (1993). Estimation of economic thresholds for weed control in winter wheat. Weed Research, 33(6), 459–467. doi:10.1111/j.1365-3180.1993.tb01962.x.

    Article  Google Scholar 

  • Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693–712. doi:10.1007/s11119-012-9274-5.

    Article  CAS  Google Scholar 

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Acknowledgments

Funding for the research presented in this paper was provided by the Italian Ministry of Agricultural and Forest Policy (Mipaf) within the APREINF OIGA project. The authors would like to acknowledge the essential contribution of the farmer Vittorio Lopez, who provided the boom sprayer and the fields in which the research took place and who took care of all the agronomic management.

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Correspondence to F. Castaldi.

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Castaldi, F., Pelosi, F., Pascucci, S. et al. Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precision Agric 18, 76–94 (2017). https://doi.org/10.1007/s11119-016-9468-3

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