Design of a computer vision system for a differential spraying operation in precision agriculture using Hebbian learning

IET Computer Vision

Volumn 1, Issue 3-4, 2007, Pages 93-99

  Pajares, G ^a   Tellaeche, A ^b   Burgosartizzu, X P ^c   Ribeiro, A ^c  

^a UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

^b UNIVERSIDAD NACIONAL DE EDUCACIÓN A DISTANCIA   (Spain)

^c Consejo Superior de Investigaciones Científicas   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ACQUISITION; DECISION MAKING; HERBICIDES; IMAGE SEGMENTATION; LEARNING SYSTEMS;

DIFFERENTIAL SPRAYING; HEBBIAN LEARNING; PRECISION AGRICULTURE;

COMPUTER VISION;

EID: 37249032464     PISSN: 17519632     EISSN: 17519640     Source Type: Journal    
DOI: 10.1049/iet-cvi:20070028     Document Type: Article

References (27)

1. Stafford, J.V.: ' The role of technology in the emergence and current status of precision agriculture ', Srinivasan, A., Handbook of precision agriculture, (Food Products Press, New York, 2006), p. 19-56
2. Zhang, A., Wang, M., and Wang, N.: ' Precision agricuture-a worldwide overview ', Comput. Electron. Agric., 2002, 36, p. 113-132 10.1016/S0168- 1699(02)00096-0 0168-1699
3. Gerhards, R., and 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, p. 185-193
4. Pérez, A.J., López, F., Benlloch, J.V., and Christensen, S.: ' Colour and shape analysis techniques for weed detection in cereal fields ', Comput. Electron. Agric., 2000, 25, p. 197-212 0168-1699
5. Yang, C.C., Prasher, S.O., Landry, J.A., and Ramaswamy, H.S.: ' Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications ', Precision Agric., 2003, 4, p. 5-18
6. Thorp, K.R., and Tian, L.F.: ' A Review on remote sensing of weeds in agriculture ', Precision Agric., 2004, 5, p. 477-508
7. Ribeiro, A., Fernández-Quintanilla, C., Barroso, J., and García-Alegre, M.C.: ' Development of an image analysis system for estimation of weed ', Proc. 5th European Conf. on Precision Agriculture (5ECPA), Uppsala, Sweden, June, 2005, p. 169-174
8. Barroso, J., Fernández-Quintanilla, C., Ruiz, C., Hernaiz, P., and Rew, L.J.: ' Spatial stability of Avena sterilis ssp. Ludoviciana populations under annual applications of low rates of imazamethbenz ', Weed Res., 2004, 44, p. 178-186
9. Radics, L., Glemnitz, M., Hoffmann, J., and Czimber, G.: ' Composition of weed floras in different agricultural management systems within the European climatic gradient ', Proc. 6th European Weed Research Society (EWRS). Workshop on Physical and Cultural Weed Control, 2004, p. 58-64
10. Aitkenhead, M.J., Dalgetty, I.A., Mullins, C.E., McDonald, A.J.S., and Strachan, N.J.C.: ' Weed and crop discrimination using image analysis and artificial intelligence methods ', Comput. Electron. Agric., 2003, 39, p. 157-171 10.1016/S0168-1699(03)00076-0 0168-1699
11. Granitto, P.M., Verdes, P.F., and Ceccatto, H.A.: ' Large-scale investigation of weed seed identification by machine vision ', Comput. Electron. Agric., 2005, 47, p. 15-24 10.1016/j.compag.2004.10.003 0168-1699
12. Onyango, C.M., and Marchant, J.A.: ' Segmentation of row crop plants from weeds using colour and morphology ', Comput. Electron. Agric., 2003, 39, p. 141-155 10.1016/S0168-1699(03)00023-1 0168-1699
13. Bacher, B.: ' Weed density estimation from digital images in spring barley ', 2001, PhD, Dpt. Agricultural Sciences, Section of AgroTechnology, The Royal Veterinary and Agricultural University, Denmark
14. Tian, L.F., and Slaughter, D.C.: ' Environmentally adaptive segmentation algorithm for outdoor image segmentation ', Comput. Electron. Agric., 1998, 21, p. 153-168 10.1016/S0168-1699(98)00037-4 0168-1699
15. Rosin, P.L., and Ioannidis, E.: ' Evaluation of global image thresholding for change detection ', Pattern Recognit. Lett., 2003, 24, p. 2345-2356 10.1016/S0167-8655(03)00060-6 0167-8655
16. Kapur, J., Sahoo, P., and Wong, A.: ' A new method for gray-level picture thresholding using the entropy of the histogram ', Comput. Vis. Graph. Image Process., 1985, 29, (3), p. 273-285 10.1016/0734-189X(85)90125-2 0734-189X
17. Søgaard, H.T., and Olsen, H.J.: ' Determination of crop rows by image analysis without segmentation ', Comput. Electron. Agric., 2003, 38, p. 141-158 0168-1699
18. Astrand, B., and Baerveldt, A.J.: ' An agricultural mobile robot with vision-based perception for mechanical weed control ', Autonom. Robots, 2002, 13, p. 21-35
19. Billingsley, J., and Schoenfisch, M.: ' The successful development of a vision guidance system for agriculture ', Comput. Electron. Agric., 1997, 16, p. 147-163 10.1016/S0168-1699(96)00034-8 0168-1699
20. Gonzalez, R.C., Woods, R.E., and Eddins, S.L.: ' Digital image processing using matlab ', (Prentice-Hall, New York, 2004)
21. Tian, L., Reid, J.F., and Hummel, J.W.: ' Development of a precision sprayer for site-specific weed management ', Trans. Amer. Society Agric. Eng. (ASAE), 1999, 42, (4), p. 893-900
22. Haykin, S.: ' Neural networks: a comprehensive foundation ', (Macmillan College, New York, 1994)
23. Hebb, D.O.: ' The organization of behaviour: a neuropsychological theory ', (Wiley, New York, 1949)
24. Kohonen, T.: ' An introduction to neural computing ', Neural Netw., 1988, 1, p. 3-16 0893-6080
25. Duda, R.O., Hart, P.E., and Stork, D.G.: ' Pattern classification ', (John Wiley and Sons, New York, 2001)
26. Tellaeche, A., BurgosArtizzu, P.E., Pajares, G., Ribeiro, A., and Fernández-Quintanilla, C.: ' A new vision-based approach for differential spraying in precision agriculture ', Comput. Electron. Agric., in press (available online, doi:10.1016/j.compag.2007.07.008) 0168-1699
27. Sneath, P., and Sokal, R.: ' Numerical taxonomy: the principle and practice of numerical classification ', (W.H. Freeman, 1973)