A technique for high-accuracy ground-based continuous weed mapping at field scale

Transactions of the ASABE

Volumn 56, Issue 6, 2013, Pages 1523-1533

  Longchamps, L ^a   Panneton, B ^b   Simard, M J ^b,c   Leroux, G D ^a  

^a Department of Environmental and Radiological Health Sciences   (United States)

^b AGRICULTURE AND AGRI FOOD CANADA   (Canada)

^c UNIVERSITÉ LAVAL   (Canada)

Author keywords

Image segmentation; Imagery; Proximal sensing; Weed mapping; Weed spatial distribution

Indexed keywords

AUTOMATIC IMAGE SEGMENTATION; DETECTION ACCURACY; ENVIRONMENTAL ASPECTS; GROUND-BASED IMAGERIES; IMAGERY; PROXIMAL SENSING; SCIENTIFIC LITERATURE; WEED MAPPINGS;

CROPS; IMAGE SEGMENTATION; PRINCIPAL COMPONENT ANALYSIS; SATELLITE IMAGERY; SLOW LIGHT; VEGETATION; WEED CONTROL;

SPATIAL DISTRIBUTION;

ACCURACY ASSESSMENT; AIRBORNE SENSING; DATA ACQUISITION; IMAGE RESOLUTION; MAIZE; PRINCIPAL COMPONENT ANALYSIS; SATELLITE IMAGERY; SPATIAL DISTRIBUTION; WEED CONTROL;

EID: 84891385157     PISSN: 21510032     EISSN: None     Source Type: Journal    
DOI: 10.13031/trans.56.10110     Document Type: Article

References (34)

1. Alchanatis, V., L. Ridel, A. Hetzroni, and L. Yaroslavsky. 2005. Weed detection in multi-spectral images of cotton fields. Computers and Electronics in Agric. 47(3): 243-260.
2. Andújar, D., A. Ribeiro, C. Fernández-Quintanilla, and J. Dorado. 2011. Accuracy and feasibility of optoelectronic sensors for weed mapping in row crops. Sensors 11(3): 2304-2318.
3. Backes, M., and J. Jacobi. 2006. Classification of weed patches in QuickBird images: Verification by ground truth data. EARSeL eProc. 5(2): 173-179.
4. Bhatti, A. U., D. J. Mulla, and B. E. Frazier. 1991. Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and thematic mapper images. Remote Sensing Environ. 37(3): 181-191.
5. Brown, R. B., and S. D. Noble. 2005. Site-specific weed management: Sensing requirementsWhat do we need to see Weed Sci. 53(2): 252-258.
6. Burgos-Artizzu, X. P., A. Ribeiro, A. Tellaeche, and G. Pajares. 2009. Improving weed pressure assessment using digital images from an experience-based reasoning approach. Computers and Electronics in Agric. 65(2): 176-185.
7. Burks, T. F., S. A. Shearer, R. S. Gates, and K. D. Donohue. 2000. Backpropagation neural network design and evaluation for classifying weed species using color image texture. Trans. ASAE 43(4): 1029-1037.
8. Cardina, J., G. A. Johnson, and D. H. Sparrow. 1997. The nature and consequence of weed spatial distribution. Weed Sci. 45(3): 364-373.
9. Chen, F., D. E. Kissel, L. T. West, and W Adkins. 2000. Field-scale mapping of surface soil organic carbon using remotely sensed imagery. SSSA J. 64(2): 746-753.
10. Colbach, N., F. Forcella, and G. A. Johnson. 2000. Spatial and temporal stability of weed populations over five years. Weed Sci. 48(3): 366-377.
11. Faechner, T., K. Norrena, A. G. Thomas, and C. V. Deutsch. 2002. A risk-qualified approach to calculate locally varying herbicide application rates. Weed Res. 42(6): 476-485.
12. Fleming, K. L., D. F. Heermann, and D. G. Westfall. 2004. Evaluating soil color with farmer input and apparent soil electrical conductivity for management zone delineation. Agron. J. 96(6): 1581-1587.
13. Gerhards, R., and H. Oebel. 2006. Practical experiences with a system for site-specific weed control in arable crops using realtime image analysis and GPS-controlled patch spraying. Weed Res. 46(3): 185-193.
14. Goudy, H. J., K. A. Bennett, R. B. Brown, and F. J. Tardif. 2001. Evaluation of site-specific weed management using a directinjection sprayer. Weed Sci. 49(3): 359-366.
15. Johnson, G. A., D. A. Mortensen, and C. A. Goyway. 1996. Spatial and temporal analysis of weed seedling populations using geostatistics. Weed Sci. 44(3): 704-710.
16. Lamb, D. W., and R. B. Brown. 2001. Remote sensing and mapping of weeds in crops. J. Agric. Eng. 78(2): 117-125.
17. Marshall, G., R. C. Kirkwood, and D. J. Martin. 1987. Studies on the mode of action of asulam, aminotriazole, and glyphosate in Equisetum arvense L.(field horsetail): II. The metabolism of [14C] asulam, [14C] aminotriazole, and [14C] glyphosate. Pesticide Sci. 18(1): 65-77.
18. Meyer, G. E., T. Metha, M. F. Kocher, D. A. Mortensen, and A. Samal. 1998. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Trans. ASAE 41(4): 1189-1197.
19. Mortensen, D. A., J. A. Dieleman, and G. A. Johnson. 1998. Weed spatial variation and weed management. In Integrated Weed and Soil Management, 293-309. J. L. Hatfield, D. D. Buhler, and B. A. Stewart, eds. Chelsea, Mich.: Ann Arbor Press.
20. Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man, and Cybernetics 9(1): 62-66.
21. Panneton, B., and M. Brouillard. 2009. Colour representation methods for segmentation of vegetation in photographs. Biosystems Eng. 102(4): 365-378.
22. Parent, S., and C. Messier. 1996. A simple and efficient method to estimate microsite light availability under a forest canopy. Canadian J. Forest Res. 26(1): 151-154.
23. Philipp, I., and T. Rath. 2002. Improving plant discrimination in image processing by use of different colour space transformations. Computers and Electronics in Agric. 35(1): 1-15.
24. Rew, L. J., and R. D. Cousens. 2001. Spatial distribution of weeds in arable crops: Are current sampling and analytical methods appropriate Weed Res. 41(1): 1-18.
25. Ruiz, D., C. Escribano, and C. Fernández-Quintanilla. 2006. Assessing the opportunity for site-specific management of Avena sterilis in winter barley fields in Spain. Weed Res. 46(5): 379-387.
26. Saporta, G. 1990. Probabilités, Analyse des Données et Statistique. Paris, France: Editions Technip.
27. Scheaffer, R. L., W. Mendenhall, and R. L. Ott. 2006. Elementary Survey Sampling. 6th ed. Belmont, Cal.: Duxbury Press.
28. Siddiqi, M. H., S. B. T. Sulaiman, I. Faye, and I. Ahmad. 2009. A real-time specific weed discrimination system using multi-level wavelet decomposition. Intl. J. Agric. and Biol. 11(5): 559-565.
29. Sui, R., J. A. Thomasson, J. Hanks, and J. Wooten. 2008. Groundbased sensing system for weed mapping in cotton. Computers and Electronics in Agric. 60(1): 31-38.
30. Tian, L., and D. C. Slaughter. 1998. Environmentally adaptive segmentation algorithms for outdoor machine vision systems. Computers and Electronics in Agric. 21(3): 153-168.
31. Timmermann, C., R. Gerhards, and W. Kühbauch. 2003. The economic impact of site-specific weed control. Precision Agric. 4(3): 249-260.
32. Van Wychen, L. R., E. C. Luschei, A. J. Bussan, and B. D. Maxwell. 2002. Accuracy and cost-effectiveness of GPS-assisted wild oat mapping in spring cereal crops. Weed Sci. 50(1): 120-129.
33. Wallinga, J., R. M. W. Groeneveld, and L. A. P. Lotz. 1998. Measures that describe weed spatial patterns at different levels of resolution and their applications for patch spraying of weeds. Weed Res. 38(5): 351-359.
34. Woebbecke, D. M., G. E. Meyer, K. V. Bargen, and D. A. Mortensen. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 38(1): 259-269.