Accuracy and feasibility of optoelectronic sensors for weed mapping in wide row crops

Sensors

Volumn 11, Issue 3, 2011, Pages 2304-2318

  Andújar, Dionisio ^a   Ribeiro, Ángela ^b   Fernández Quintanilla, César ^a   Dorado, José ^a  

^a INSTITUTO DE CIENCIAS AGRARIAS   (Spain)

^b CENTRE FOR AUTOMATION AND ROBOTICS   (Spain)

Author keywords

Ground based mapping system; Sampling resolution; Site specific weed management; Weed detection

Indexed keywords

ARTICLE; CROP; ELECTRONICS; FEASIBILITY STUDY; GEOGRAPHIC INFORMATION SYSTEM; GROWTH, DEVELOPMENT AND AGING; INSTRUMENTATION; OPTICAL INSTRUMENTATION; SPAIN; WEED;

CROPS, AGRICULTURAL; ELECTRONICS; FEASIBILITY STUDIES; GEOGRAPHIC INFORMATION SYSTEMS; OPTICAL DEVICES; PLANT WEEDS; SPAIN;

EID: 79953296503     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s110302304     Document Type: Article

References (37)

1. Rew, L.J.; Cousens, R.D. Spatial distribution of weeds in arable crops: Are current sampling and analytical methods appropriate? Weed Res. 2001, 41, 1-18.
2. Barroso, J.; Fernández-Quintanilla, C.; Maxwell, B.D.; Rew, L.J. Simulating the effects of weed spatial patterns and resolution of mapping and spraying on economics of site-specific management. Weed Res. 2004, 44, 460-468.
3. Van Wychen, L.R.; Luschei, E.C.; Bussan, A.J.; Maxwell, B.D. Accuracy and cost effectiveness of GPS-assisted wild oat mapping in spring cereal crops. Weed Sci. 2002, 50, 120-129.
4. Ruiz, D.; Escribano, C.; Fernández-Quintanilla, C. Assessing the opportunity for site-specific management of Avena sterilis in winter barley fields in Spain. Weed Res. 2006, 46, 379-387.
5. Thorp, K.R.; Tian, L.F. A review of remote sensing of weeds in agriculture. Precis. Agric. 2004, 5, 477-508.
6. López-Granados, F.; Jurado-Expósito, M.; Peña-Barragán, J.M.; García-Torres, L. Using remote sensing for identification of late-season grass weed patches in wheat. Weed Sci. 2006, 54, 346-353.
7. Peña-Barragán, J.M.; López-Granados, F.; Jurado-Expósito, M.; García-Torres, L. Mapping Ridolfia segetum patches in sunflower crop using remote sensing. Weed Res. 2007, 47, 164-172.
8. 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.
9. Slaughter, D.C.; Giles, D.K.; Downey, D. Autonomous robotic weed control systems: A review. Comput. Electron. Agric. 2008, 61, 63-78.
10. Tellaeche, A.; Burgos-Artizzu, X.P.; Pajares, G.; Ribeiro, A. A vision-based method for weeds identification through the bayesian decision theory. Patt. Recog. 2008, 41, 521-530.
11. Burgos-Artizzu, X.P.; Ribeiro, A.; Tellaeche, A.; Pajares, G. Improving weed pressure assessment using digital images from an experience-based reasoning approach. Comput. Electron. Agric. 2009, 65, 176-185.
12. Siddiqi, M.H.; Sulaiman, S.B.T.; Faye, I.; Ahmad, I. A real time specific weed discrimination system using multi-level wavelet decomposition. Int. J. Agr. Biol. 2009, 11, 559-565.
13. Woebbecke, D.M.; Meyer, G.E.; Bargen, K.V.; Mortensen, D.A. Color indices for weed identification under various soil, residue and lighting conditions. Trans. ASAE 1995, 38, 259-269.
14. Downey, D.; Giles, D.K.; Slaughter, D.C. Weeds accurately mapped using DGPS and ground-based vision identification. Calif. Agr. 2004, 58, 218-221.
15. Burgos-Artizzu, X.P.; Ribeiro, A.; Pajares, G. Precise real-time crop/weed discrimination in widely spaced crop fields. In Proceedings of ICRA2009 Workshop on Agricultural Robotics. IEEE International Conference on Robotics and Automation, Kobe, Japan, May 2009; pp. 8-13.
16. Felton, W.L.; McCloy, K.R. Spot spraying. Agr. Eng. 1992, 11, 26-29.
17. Blasco, J.; Aleixos, N.; Roger, J.M.; Rabatel, G.; Molto, E. Robotic weed control using machine vision. Biosyst. Eng. 2002, 83, 149-157.
18. Shearer, S.A.; Jones, P.T. Selective application of post-emergence herbicides using photoelectrics. Trans. ASAE 1991, 34, 1661-1666.
19. Biller, R.H. Reduced input of herbicides by use of optoelectronic sensors. J. Agr. Eng. Res. 1998, 71, 357-362.
20. Sui, R.; Thomasson, J.A.; Hanks, J.; Wooten, J. Ground-based sensing system for weed mapping in cotton. Comput. Electron. Agric. 2008, 60, 31-38.
21. Paice, M.E.R.; Day, W.; Rew, L.J.; Howard, A. A stochastic simulation model for evaluating the concept of patch spraying. Weed Res. 1998, 38, 373-388.
22. Barroso, J.; Ruiz, D.; Fernández-Quintanilla, C.; Leguizamon, E.S.; Hernaiz, P.; Ribeiro, A.; Diaz, B.; Rew, L.J. Comparison of sampling methodologies for site specific management of Avena sterilis. Weed Res. 2005, 45, 165-174.
23. Berge, T.W.; Cederkvist, H. R; Aastveit, A.H.; Fykse, H. Simulating the effects of mapping and spraying resolution and threshold level on accuracy of patch spraying decisions and herbicide use based on mapped weed data. Acta Agr. Scand. Sect. B-Soil Pl. 2008, 58, 216-229.
24. Donald, W.W. Estimated corn yields using either weed cover or rated control after pre-emergence herbicides. Weed Sci. 2006, 54, 373-379.
25. Growth stages of mono-and dicotyledonous plants. In BBCH Monograph, 2nd ed.; Federal Biological Research Centre for Agriculture and Forestry: Berlin, Germany, 2001; Available online: http://syntechresearch.hu/sites/default/files/publikaciok/bbch.pdf (accessed on 20 December 2010).
26. Cousens, R.D.; Brown, R.W.; McBratney, A.B.; Whelan, B.; Moerkerk, M. Sampling strategy is important for producing weed maps: A case study using kriging. Weed Sci. 2002, 50, 542-546.
27. SPSS® 17.0 User's Guide; SPSS (Statistical Product and Service Solutions) Inc.: Chicago, IL, USA, 2008.
28. Cardina, J.; Johnson, G.A.; Sparrow, D.H. The nature and consequence of weed spatial distribution. Weed Sci. 1997, 45, 364-373.
29. Heisel, T.; Andreasen, C.; Ersboll, A.K. Annual weed distributions can be mapped with kriging. Weed Res. 1996, 36, 325-337.
30. Zanin, G.; Berti, A.; Riello, L. Incorporation of weed spatial variability into the weed control decision-making process. Weed Res. 1998, 38, 107-118.
31. Rossi, R.E.; Mulla, D.J.; Journel, A.G.; Franz, E.H. Geostatistical tools for modelling and interpreting ecological spatial dependence. Ecol. Monogr. 1992, 62, 277-314.
32. Isaaks, E.H.; Srivastava, R.M. An Introduction to Applied Geostatistics; Oxford University Press: New York, NY, USA, 1989.
33. Yang, C.C.; Prasher, S.O.; Landry, J.A.; Ramaswamy, H.S. Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications. Precis. Agric. 2003, 4, 5-18.
34. Backes, M.; Scumacher, D.; Plümer, L. The Sampling Problem in Weed Control: Are Currently Applied Sampling Strategies Adequate for Site-Specific Weed Control; Stafford, J.V., Ed.; Precision Agriculture'05. Wageningen Academic Publishers: Wageningen, The Netherlands, 2005; pp. 155-161.
35. Gliever, C.; Slaughter, D.C. Crop versus Weed Recognition with Artificial Neural Networks; American Society of Agricultural and Biological Engineers: Saint Joseph, MI, USA, 2001.
36. Gerhards, R.; Christensen, S. Real time weed detection, decision making and patch spraying in maize, sugarbeets, winter wheat and winter barley. Weed Res. 2003, 43, 385-392.
37. Berge, T.W.; Fykse, H.; Aastveit, A.H. Patch spraying of weeds in spring cereals: Simulated influences of threshold level and spraying resolution on spraying errors and potential herbicide reduction. Acta Agr. Scand. Sect. B-Soil Pl. 2007, 57, 212-221.