Multi-temporal site-specific weed control of Cirsium arvense (L.) scop. and Rumex crispus L. in maize and sugar beet using unmanned aerial vehicle based mapping

Agriculture (Switzerland)

Volumn 8, Issue 5, 2018, Pages

  Mink, Robin ^a   Dutta, Avishek ^b   Peteinatos, Gerassimos G ^a   Sökefeld, Markus ^a   Engels, Johannes Joachim ^b   Hahn, Michael ^b   Gerhards, Roland ^a  

^a UNIVERSITY OF HOHENHEIM   (Germany)

^b University of Applied Sciences Stuttgart   (Germany)

Author keywords

Digital elevation model; Excessive green red vegetation index; Patch spraying; Site specific weed control; UAV weed detection; Weed mapping

Indexed keywords

EID: 85048299496     PISSN: None     EISSN: 20770472     Source Type: Journal    
DOI: 10.3390/agriculture8050065     Document Type: Article

References (44)

1. Rasmussen, J.; Nielsen, J.; Garcia-Ruiz, F.; Christensen, S.; Streibig, J.C. Potential uses of small unmanned aircraft systems (UAS) in weed research. Weed Res. 2013, 53, 242–248. [CrossRef]
2. Torres-Sánchez, J.; López-Granados, F.; De Castro, A.; Peña-Barragán, J. Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management. PLoS ONE 2013, 8, e58210. [CrossRef] [PubMed]
3. Borra-Serrano, I.; Peña, J.; Torres-Sánchez, J.; Mesas-Carrascosa, F.; López-Granados, F. Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping. Sensors 2015, 15, 19688–19708. [CrossRef] [PubMed]
4. Pérez-Ortiz, M.; Peña, J.M.; Gutiérrez, P.A.; Torres-Sánchez, J.; Hervás-Martínez, C.; López-Granados, F. Selecting patterns and features for between-and within-crop-row weed mapping using UAV-imagery. Expert Syst. Appl. 2016, 47, 85–94. [CrossRef]
5. Hung, C.; Xu, Z.; Sukkarieh, S. Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV. Remote Sens. 2014, 6, 12037–12054. [CrossRef]
6. López-Granados, F.; Torres-Sánchez, J.; Serrano-Pérez, A.; de Castro, A.I.; Mesas-Carrascosa, F.J.; Peña, J.M. Early season weed mapping in sunflower using UAV technology: Variability of herbicide treatment maps against weed thresholds. Precis. Agric. 2016, 17, 183–199. [CrossRef]
7. López-Granados, F.; Torres-Sánchez, J.; De Castro, A.I.; Serrano-Pérez, A.; Mesas-Carrascosa, F.J.; Peña, J.M. Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery. Agron. Sustain. Dev. 2016, 36, 1–12. [CrossRef]
8. De Castro, A.; Torres-Sánchez, J.; Peña, J.; Jiménez-Brenes, F.; Csillik, O.; López-Granados, F. An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery. Remote Sens. 2018, 10, 285. [CrossRef]
9. Fernandez-Quintanilla, C.; Peña-Barragán, J.M.; Andújar, D.; Dorado, J.; Ribeiro, A.; López-Granados, F. Is the current state-of-the-art of weed monitoring suitable for site-specific weed management in arable crops? Weed Res. 2018. [CrossRef]
10. Salamí, E.; Barrado, C.; Pastor, E. UAV Flight Experiments Applied to the Remote Sensing of Vegetated Areas. Remote Sens. 2014, 6, 11051–11081. [CrossRef]
11. Turner, D.; Lucieer, A.; Watson, C. An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds. Remote Sens. 2012, 4, 1392–1410. [CrossRef]
12. Tilly, N.; Aasen, H.; Bareth, G. Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass. Remote Sens. 2015, 7, 11449–11480. [CrossRef]
13. Geipel, J.; Link, J.; Claupein, W. Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System. Remote Sens. 2014, 6, 10335–10355. [CrossRef]
14. Andújar, D.; Escolà, A.; Dorado, J.; Fernández-Quintanilla, C. Weed discrimination using ultrasonic sensors. Weed Res. 2011, 51, 543–547. [CrossRef]
15. Christensen, S.; Rasmussen, J.; Pedersen, S.M.; Dorado, J.; Fernandez-Quintanilla, C. Prospects for Site Specific Weed Management. In Proceedings of the International Conference on Robotics and Associated High-Technologies and Equipment for Agriculture and Forestry, Madrid, Spain, 21–23 May 2014; pp. 541–549.
16. Longchamps, L.; Panneton, B.; Simard, M.-J.; Leroux, G.D. An Imagery-Based Weed Cover Threshold Established Using Expert Knowledge. Weed Sci. 2014, 62, 177–185. [CrossRef]
17. Keller, M.; Gutjahr, C.; Möhring, J.; Weis, M.; Sökefeld, M.; Gerhards, R. Estimating economic thresholds for site-specific weed control using manual weed counts and sensor technology: An example based on three winter wheat trials. Pest Manag. Sci. 2014, 70, 200–211. [CrossRef] [PubMed]
18. Hamouz, P.; Hamouzová, K.; Holec, J.; Tyšer, L. Impact of site-specific weed management on herbicide savings and winter wheat yield. Plant Soil Environ. 2018, 59, 101–107. [CrossRef]
19. Marshall, E.J.P. Field-scale estimates of grass weed populations in arable land. Weed Res. 1988, 28, 191–198. [CrossRef]
20. Johnson, G.A.; Mortensen, D.A.; Martin, A.R. A simulation of herbicide use based on weed spatial distribution. Weed Res. 1995, 35, 197–205. [CrossRef]
21. Gerhards, R.; Sökefeld, M.; Timmermann, C.; Kühbauch, W.; Williams, M.M., II. Site-Specific weed control in maize, sugar beet, winter wheat, and winter barley. Precis. Agric. 2002, 3, 25–35. [CrossRef]
22. Gerhards, R.; Oebel, H. Practical experiences with a system for site-specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying. Weed Res. 2006, 46, 185–193. [CrossRef]
23. Christensen, S.; SØgaard, H.T.; Kudsk, P.; NØrremark, M.; Lund, I.; Nadimi, E.S.; JØrgensen, R. Site-specific weed control technologies. Weed Res. 2009, 49, 233–241. [CrossRef]
24. Gerhards, R.; Sökefeld, M.; Knuf, D.; Kühbauch, W. Kartierung und geostatistische Analyse der Unkrautverteilung in Zuckerrübenschlägen als Grundlage für eine teilschlagspezifische Bekämpfung. J. Agron. Crop Sci. 1996, 176, 259–266. [CrossRef]
25. Lamastus, F.E.; Shaw, D.R. Comparison of different sampling scales to estimate weed populations in three soybean fields. Precis. Agric. 2005, 6, 271–280. [CrossRef]
26. Gutjahr, C.; Sökefeld, M.; Gerhards, R. Evaluation of two patch spraying systems in winter wheat and maize. Weed Res. 2012, 52, 510–519. [CrossRef]
27. Core Team R. R: A Language and Environment for Statistical Computing; The R Foundation for Statistical Computing: Vienna, Austria, 2017.
28. Hijmans, R.J.; van Etten, J.; Cheng, J.; Mattiuzzi, M.; Sumner, M.; Greenberg, J.A.; Lamigueiro, O.P.; Bevan, A.; Racine, E.B.; Shortridge, A., et al. Raster: Geographic Analysis and Modeling with Raster Data. Available online: https://cran.r-project.org/web/packages/raster/index.html (accessed on 23 March 2018).
29. Bivand, R.; Keitt, T.; Rowlingson, B.; Pebesma, E.; Sumner, M.; Hijmans, R.; Rouault, E. Rgdal: Bindings for the “Geospatial” Data Abstraction Library. Available online: https://cran.r-project.org/web/packages/rgdal/index.html (accessed on 23 March 2018).
30. Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation; Technical Report; NASA: Washington, DC, USA, 1973; p. 112.
31. Liebisch, F.; Kirchgessner, N.; Schneider, D.; Walter, A.; Hund, A. Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 2015, 11, 9. [CrossRef] [PubMed]
32. Roth, L.; Streit, B. Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: An applied photogrammetric approach. Precis. Agric. 2018, 19, 93–114. [CrossRef]
33. Meyer, G.E.; Hindman, T.W.; Laksmi, K. Machine Vision Detection Parameters for Plant Species Identification. In Proceedings of the Precision Agriculture and Biological Quality, Boston, MA, USA, 3–4 November 1999; Volume 3543, pp. 327–335. [CrossRef]
34. Woebbecke, D.M.; Meyer, G.E.; Von Bargen, K.; Mortensen, D.A. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 1995, 38, 259–269. [CrossRef]
35. Meyer, G.E.; Neto, J.C.; Jones, D.D.; Hindman, T.W. Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Comput. Electron. Agric. 2004, 42, 161–180. [CrossRef]
36. Hamuda, E.; Glavin, M.; Jones, E. A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 2016, 125, 184–199. [CrossRef]
37. Williams, M.M., II; Gerhards, R.; Mortensen, D.A. Two-year weed seedling population responses to a post-emergent method of site-specific weed management. Precis. Agric. 2000, 2, 247–263. [CrossRef]
38. Tian, L.; Reid, J.F.; Hummel, J.W. Development of a precision sprayer for site-specific weed management. Trans. ASAE 1999, 42, 893–900. [CrossRef]
39. Longchamps, L.; Panneton, B.; Simard, M.-J.; Leroux, G.D. Could weed sensing in corn interrows result in efficient weed control? Weed Technol. 2012, 26, 649–656. [CrossRef]
40. Mesas-Carrascosa, F.-J.; Torres-Sánchez, J.; Clavero-Rumbao, I.; García-Ferrer, A.; Peña, J.M.; Borra-Serrano, I.; López-Granados, F. Assessing optimal flight parameters for generating accurate multispectral orthomosaicks by UAV to support site-specific crop management. Remote Sens. 2015, 7, 12793–12814. [CrossRef]
41. Mesas-Carrascosa, F.J.; Clavero Rumbao, I.; Torres-Sánchez, J.; García-Ferrer, A.; Peña, J.M.; López Granados, F. Accurate ortho-mosaicked six-band multispectral UAV images as affected by mission planning for precision agriculture proposes. Int. J. Remote Sens. 2017, 38, 2161–2176. [CrossRef]
42. Nkurunziza, L.; Streibig, J.C. Carbohydrate dynamics in roots and rhizomes of Cirsium arvense and Tussilago farfara. Weed Res. 2011, 51, 461–468. [CrossRef]
43. Welton, F.A.; Morris, V.H.; Hartzler, A.J. Organic Food Reserves in Relation to the Eradication of Canada Thistles; Ohio Agricultural Experiment Station: Wooster, OH, USA, 1929.
44. Håkansson, S. Weeds and Weed Management on Arable Land: An Ecological Approach; Hakansson, S., Ed.; CABI: Wallingford, UK, 2003; ISBN 0-85199-651-5.