Evaluating the potential of Unmanned Aerial Systems for mapping weeds at field scales: a case study with Alopecurus myosuroides

Weed Research

Volumn 58, Issue 1, 2018, Pages 35-45

  Lambert, J P T ^a   Hicks, H L ^a   Childs, D Z ^a   Freckleton, R P ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

black grass; distribution; drones; modelling; precision agriculture; site specific weed management; wheat

Indexed keywords

AERIAL SURVEY; FIELD METHOD; GRASS; IMAGERY; MAPPING METHOD; PRECISION AGRICULTURE; SPATIAL DISTRIBUTION; TECHNOLOGY ADOPTION; UNMANNED VEHICLE; WEED; WEED CONTROL; WHEAT;

ALOPECURUS MYOSUROIDES; TRITICUM AESTIVUM;

EID: 85034080737     PISSN: 00431737     EISSN: 13653180     Source Type: Journal    
DOI: 10.1111/wre.12275     Document Type: Article

References (59)

1. Audsley E (1993) Operational research analysis of patch spraying. Crop Protection 12, 111–119.
2. Austin R (2010) Unmanned Aircraft Systems: UAVS Design, Development and Deployment. John Wiley & Sons, Hoboken, NJ, USA.
3. Ballesteros R, Ortega JF, Hernández D & Moreno MA (2014) Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: description of image acquisition and processing. Precision Agriculture 15, 579–592.
4. Barnett DT, Stohlgren TJ, Jarnevich CS et al. (2007) The art and science of weed mapping. Environmental Monitoring and Assessment 132, 235–252.
5. Blair AM, Cussans JW & Lutman PJW (1999) A biological framework for developing a weed management support system for weed control in winter wheat: weed competition and time of weed control. In: Proceedings of the 1999 Brighton Conference – Weeds. 15–18 November 1999. Hilton Brighton Metropole Hotel, UK.
6. Breiman L (2001) Random forests. Machine Learning 45, 5–32.
7. Brenchley WE (1913) The weeds of arable land. III. Annals of Botany 27, 141–166.
8. Bryson M, Reid A, Hung C, Ramos F & Sukkarieh S (2014) Cost-effective mapping using unmanned aerial vehicles in ecology monitoring applications. Experimental Robotics 35, 509–523.
9. Catapult Satellite Applications (2016) Unmanned Aerial Vehicles (UAVs) for UK agriculture: creating an invisible precision farming technology, 2016. Available at: https://sa.catapult.org.uk/wp-content/uploads/2016/07/White-paper-UAVs-and-agriculture_Final2.pdf (last accessed 28 February 2017).
10. Chauhan BS, Singh VP, Kumar A & Johnson DE (2011) Relations of rice seeding rates to crop and weed growth in aerobic rice. Field Crops Research 121, 105–115.
11. Collett D (2002) Modelling Binary Data. 2. CRC Press, New York, NY, USA.
12. Craufurd PQ & Wheeler TR (2009) Climate change and the flowering time of annual crops. Journal of Experimental Botany 60, 2529–2539.
13. Cuneo P, Jacobson CR & Leishman MR (2009) Landscape-scale detection and mapping of invasive African Olive (Olea europaea L. ssp. cuspidata Wall ex G. Don Ciferri) in SW Sydney, Australia using satellite remote sensing. Applied Vegetation Science 12, 145–154.
14. De Castro AI, Ehsani R, Ploetz RC, Crane JH & Buchanon S (2015) Detection of laurel wilt disease in avocado using low altitude aerial imaging. PLoS One 10, e0124642.
15. Dvořák P, Müllerová J, Bartaloš T & Brůna J (2015) Unmanned aerial vehicles for alien plant species detection and monitoring. ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 40, 83.
16. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874.
17. Freckleton RP, Sutherland WJ, Watkinson AR & Queenborough SA (2011) Density-structured models for plant population dynamics. American Naturalist 177, 1–17.
18. Fried G, Norton LR & Reboud X (2008) Environmental and management factors determining weed species composition and diversity in France. Agriculture Ecosystems and Environment 128, 68–76.
19. Gabriel D, Thies C & Tscharntke T (2005) Local diversity of arable weeds increases with landscape complexity. Perspectives in Plant Ecology, Evolution and Systematics 7, 85–93.
20. Heard MS, Hawes C, Champion GT et al. (2003) Weeds in fields with contrasting conventional and genetically modified herbicide-tolerant crops. I. Effects on abundance and diversity. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 358, 1819–1832.
21. Henckel L, Borger L, Meiss H, Gaba S & Bretagnolle V (2015) Organic fields sustain weed metacommunity dynamics in farmland landscapes. Proceedings of the Royal Society of London. Series B, Biological Sciences 282, 20150002.
22. Huang CY & Asner GP (2009) Applications of remote sensing to alien invasive plant studies. Sensors 9, 4869–4889.
23. Hung C, Xu Z & Sukkarieh S (2014) Feature learning based approach for weed classification using high resolution aerial images from a digital camera mounted on a UAV. Remote Sensing 6, 12037–12054.
24. Joseph LN, Field SA, Wilcox C & Possingham HP (2006) Presence-absence versus abundance data for monitoring threatened species. Conservation Biology 6, 1679–1687.
25. Kerr JT & Ostrovsky M (2003) From space to species: ecological applications for remote sensing. Trends in Ecology and Evolution 8, 299–305.
26. Lamb DW & Brown RB (2001) Remote-sensing and mapping of weeds in crops. Journal of Agricultural Engineering Research 78, 117–125.
27. Lancahsire PD, Bleiholder H, Boom TVD et al. (1991) A uniform decimal code for growth stages of crops and weeds. Annals of Applied Biology 119, 561–601.
28. Lawrence RL, Wood SD & Sheley RL (2006) Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sensing of Environment 100, 356–362.
29. Lintell Smith G, Freckleton RP, Firbank LG & Watkinson AR (1999) The population dynamics of Anisantha sterilis in winter wheat: comparative demography and the role of management. Journal of Applied Ecology 36, 455–471.
30. López-Granados F (2011) Weed detection for site-specific weed management: mapping and real-time approaches. Weed Research 51, 1–11.
31. Marshall EJP (1988) Field-scale estimates of grass weed populations in arable land. Weed Research 28, 191–198.
32. Marshall EJP (2009) The impact of landscape structure and sown grass margin strips on weed assemblages in arable crops and their boundaries. Weed Research 49, 107–115.
33. Moshou D, Bravo C, West J, Wahlen T, McCartney A & Ramon H (2004) Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture 44, 173–188.
34. Nonami K (2007) Prospect and recent research & development for civil use autonomous unmanned aircraft as UAV and MAV. Journal of System Design and Dynamics 1, 120–128.
35. Nordmeyer H (2006) Patchy weed distribution and site-specific weed control in winter cereals. Precision Agriculture 7, 219–231.
36. Pena JM, Torres-Sanchez J, de Castro AI, Kelly M & Lopez-Granados F (2013) Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS One 8, e77151.
37. Pettorelli N (2013) The Normalized Difference Vegetation Index. Oxford University Press, Oxford, UK.
38. Pettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ & Stenseth NC (2005) Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution 20, 503–510.
39. Preston CD, Pearman D & Dines TD (2002) New Atlas of the British & Irish Flora. Oxford University Press, Oxford, UK.
40. Primicerio J, Di Gennaro SF, Fiorillo E et al. (2012) A flexible unmanned aerial vehicle for precision agriculture. Precision Agriculture 13, 517–523.
41. Queenborough SA, Burnet KM, Sutherland WJ, Watkinson AR & Freckleton RP (2011) From meso- to macroscale population dynamics: a new density-structured approach. Methods in Ecology and Evolution 2, 289–302.
42. Quinlan JR (1992) Learning with continuous classes. Machine Learning, 92, 343–348.
43. Rango A, Laliberte A, Herrick JE et al. (2009) Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. Journal of Applied Remote Sensing 3, 608–611.
44. Rasmussen J, Nielsen J, Garcia-Ruiz F, Christensen S & Streibig JC (2013) Potential uses of small unmanned aircraft systems (UAS) in weed research. Weed Research 53, 242–248.
45. Shorter N & Kasparis T (2009) Automatic vegetation identification and building detection from a single nadir aerial image. Remote Sensing, 1, 731–757.
46. Storkey J, Stratonovitch P, Chapman DS, Vidotto F & Semenov MA (2014) A process-based approach to predicting the effect of climate change on the distribution of an invasive allergenic plant in europe. PLoS One, 9, e88156.
47. Taylor CM & Hastings A (1998) Finding optimal control strategies for invasive species: a density-structured model for Spartina alterniflora. Journal of Applied Ecology, 41, 1049–1057.
48. Thornton PK, Fawcett RH, Dent JB & Perkins TJ (1990) Spatial weed distribution and economic thresholds for weed control. Crop Protection 9, 337–342.
49. Thorp KR & Tian LF (2004) A review on remote sensing of weeds in agriculture. Precision Agriculture 5, 477–508.
50. Timmermann C, Gerhards R & Kühbauch W (2003) The economic impact of site-specific weed control. Precision Agriculture 4, 249–260.
51. Torres-Sánchez J, López-Granados F, De Castro AI & Peña-Barragán JM (2013) Configuration and specifications of an Unmanned Aerial Vehicle (UAV) for early site specific weed management. PLoS One 8, e58210.
52. Torres-Sánchez J, Peña JM, de Castro AI & 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.
53. Tredennick AT, Hooten MB & Adler PB (2017) Do we need demographic data to forecast plant population dynamics? Methods in Ecology and Evolution 8, 541–551.
54. Turner D, Lucieer A & Watson C (2011) Development of an Unmanned Aerial Vehicle (UAV) for hyper-resolution vineyard mapping based on visible, multispectral and thermal imagery. In: 34th International Symposium on Remote Sensing of Environment, 4. Sydney, Australia.
55. Twining S & Clarke J (2009) Future of UK winter oilseed rapez production. Crop Protection Association Agricultural Industries Confederation: ADAS, vol. 14. AIC, Peterborough, UK.
56. Westbury DB, Woodcock BA, Harris SJ, Brown VK & Potts SG (2008) The effects of seed mix and management on the abundance of desirable and pernicious unsown species in arable buffer strip communities. Weed Research 48, 113–123.
57. Wilson BJ & Brain P (1991) Long-term stability of distribution of Alopecurus myosuroides Huds within cereal fields. Weed Research 31, 367–373.
58. Xie YC, Sha ZY & Yu M (2008) Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology 1, 9–23.
59. Zhang C & Kovacs JM (2012) The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture 13, 694–712.