Real-time image processing for crop/weed discrimination in maize fields

Computers and Electronics in Agriculture

Volumn 75, Issue 2, 2011, Pages 337-346

  Burgos Artizzu, Xavier P ^a   Ribeiro, Angela ^a   Guijarro, Maria ^b   Pajares, Gonzalo ^b  

^a CENTRE FOR AUTOMATION AND ROBOTICS   (Spain)

^b UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

Author keywords

Computer vision; Precision Agriculture; Real time image processing; Weed detection

Indexed keywords

CAMERA MOVEMENT; COMPUTER VISION SYSTEM; CROP ROW DETECTION; CROP ROWS; FAST IMAGE PROCESSING; GROWTH CONDITIONS; PRECISION AGRICULTURE; REAL-TIME IMAGE PROCESSING; SOIL HUMIDITY; TREATMENT SYSTEMS; WEED DETECTION;

CROPS; IMAGING SYSTEMS; WEED CONTROL;

COMPUTER VISION;

AGRICULTURAL TECHNOLOGY; COMPUTER; FIELD SURVEY; GROWTH RATE; HUMIDITY; IMAGE PROCESSING; MAIZE; PRECISION AGRICULTURE; REAL TIME;

ZEA MAYS;

EID: 79551684139     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2010.12.011     Document Type: Article

References (52)

1. Aitkenhead M., Dalgetty I., Mullins C., McDonald A., Strachan N. Weed and crop discrimination using image analysis and artificial intelligence methods. Comput. Electron. Agr. 2003, 39:157-171.
2. Andreasen C., Rudemo M., Sevestre S. Assessment of weed density at an early stage by use of image processing. Weed Res. 1997, 37:5-18.
3. Astrand B., Baerveldt A. An agricultural mobile robot with vision-based perception for mechanical weed control. Auton. Robot. 2002, 13:21-35.
4. Bakker T., Wouters H., Van Asselt K., Bontsema J., Tang L., Muller J., Van Straten G. A vision based row detection system for sugar beet. Comput. Electron. Agr. 2008, 60:87-95.
5. Billingsley J., Schoenfisch M. The successful development of a vision guidance system for agriculture. Comput. Electron. Agric. 1997, 16:147-163.
6. Blasco J., Aleixos N., Roger J., Rabatel G., Moltó E. Robotic weed control using machine vision. Biosyst. Eng. 2002, 83(2):149-157.
7. Bosch A., Muñoz X., Freixenet J. Segmentation and description of natural outdoor scenes. Image Vision Comput. 2007, 25:727-740.
8. Burgos-Artizzu, X., 2009. Percepción y Actuación para el tratamiento selectivo de malas hierbas en tiempo real. Universidad Complutense de Madrid, Tesis Doctoral.
9. Burgos-Artizzu X.P., Ribeiro A., de Santos M. Controlador borroso multivariable para el ajuste de tratamientos en agricultura de precisión. Revista Iberoamericana de Automática e Informática Industrial 2007, 4(April (2)):64-71.
10. Burgos-Artizzu X.P., Ribeiro A., Tellaeche A., Pajares G., Fernández-Quintanilla C. Analysis of natural images processing for the extraction of agricultural elements. Image Vision Comput. 2010, 28:138-149.
11. Burks T., Shearer S., Heath J., Donouhue K. Evaluation of neural network classifiers for weed species discrimination. Biosyst. Eng. 2005, 91(3):293-304.
12. Donald W.W. Estimated corn yields using either weed cover or rated control after pre-emergence herbicides. Weed Sci. 2006, 54(2):373-379.
13. Earl R., Wheeler P., Blackmore B., Godwin R. Precision farming: the management of variability. Landwards 1996, 51(4):18-23.
14. Gée C., Bossu J., Jones G., Truchetet F. Crop/weed discrimination in perspective agronomic images. Comput. Electron. Agr. 2008, 60:49-59.
15. Gerhards R., Christensen S. Real-time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed Res. 2003, 43:385-392.
16. 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.
17. Gonzalez R.C., Woods R.E. Digital Image Processing 2003, Prentice-Hall. 2nd ed.
18. Gottschalk R., Burgos-Artizzu X.P., Ribeiro A., Pajares G., Sánchez-Miralles A. Real-time image processing for the guidance of a small agricultural field inspection vehicle. 15th International Conference on Mechatronics and Machine Vision in Practice (M2VIP'08) December 2008.
19. Granitto P., Verdes P., Ceccatto H. Large-scale investigation of weed seed identification by machine vision. Comput. Electron. Agr. 2005, 47:15-24.
20. Hague T., Tillet N. A bandpass filter-based approach to crop row locartion and tracking. Mechatronics 2001, 11:1-12.
21. Hague T., Tillet N., Wheeler H. Automated crop and weed monitoring in widely spaced cereals. Precis. Agr. 2006, 7:21-32.
22. Hemming J., Rath T. Computer-vision based weed identification under field contions using controlled lighting. J. Agr. Eng. Res. 2001, 78(3):233-243.
23. Keihcer R., Seufert H. Automatic guidance for agricultural vehicles in Europe. Comput. Electron. Agr. 2000, 25:169-194.
24. Kropff M., Wallinga J., Lotz L. Modelling for precision weed management. Precision Agriculture: Spatial and Temporal Variability of Environmental Quality 1997, Wiley, Chester, pp. 182-204.
25. Lee W., Slaughter D., Giles D. Development of a machine vision system for weed control using precision chemical application. Proceedings of International Conference on Agricultural Machinery Engineering '96 1996, 802-811.
26. Lee W., Slaughter D., Giles D. Robotic weed control system for tomatoes using machine vision and precision chemical application. Precis. Agr. 1999, 1:95-113.
27. Leemans V., Destain M.-F. Line cluster detection using a variant of the hough transform for culture row localisation. Image Vision Comput. 2006, 24:541-550.
28. Martí J., Freixenet J., Batlle J., Casals A. A new approach to outdoor scene description based on learning and topdown segmentation. Image Vision Comput. 2001, 19:1041-1055.
29. Meyer G., Neto J. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agr. 2008, 63:282-293.
30. Olsen H. Determination of row position in small-grain crops by analisis of video images. Comput. Electron. Agr. 1995, 12:147-162.
31. Onyango C., Marchant J. Segmentation of row crop plants from weeds using colour and morphology. Comput. Electron. Agr. 2003, 39:141-155.
32. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 1979, 9:62-66.
33. Pérez A., López F., Benlloch J., Christensen S. Colour and shape analysis techniques for weed detection in cereal fields. Comput. Electron. Agr. 2000, 25:197-212.
34. Reid J., Zhang Q., Noguchi N., Dickson M. Agricultural automatic guidance research in north America. Comput. Electron. Agr. 2000, 25:155-167.
35. Ribeiro A., Fernández-Quintanilla C., Barroso J., García-Alegre M.C. Development of an image analysis system for estimation of weed. Proceedings 5th European Conf. On Precision Agriculture (5ECPA) 2005, 169-174.
36. Slaughter D., D.K.G., Downey D. Autonomous robotic weed control systems: a review. Comput. Electron. Agr. 2008, 61:63-78.
37. Sogaard H., Olsen H. Determination of crop rows by image analysis without segmentation. Comput. Electron. Agr. 2003, 38:141-158.
38. Srinivasan A. Handbook of Precision Agriculture. Principles and Applications 2006, The Haworth Press, New York.
39. Stafford J.V. Implementing precision agriculture in the 21st century. J. Agr. Eng. Res. 2000, 76:267-275.
40. Tang L., Tian L., Steward B., Reid J. Texture-based weed classification using gabor wavelets and neural network for real-time selective herbicide applications. Trans. ASAE 1999, 99:7035.
41. Tellaeche A., Burgos-Artizzu X.P., Pajares G., Ribeiro A. A new vision-based approach to differential spraying in precision agriculture. Comput. Electron. Agr. 2008, 60:144-155.
42. Tellaeche A., Burgos-Artizzu X.P., Pajares G., Ribeiro A. A vision-based method for weeds identification through the bayesian decision theory. Pattern Recognit. 2008, 41:521-530.
43. Thorp K., Tian L. A review on remote sensing of weeds in agriculture. Precis. Agr. 2004, 5:477-508.
44. Tian L., Reid J., Hummel J. Development of a precision sprayer for site-specific weed management. Trans. ASAE 1999, 42:893-900.
45. Tillet N., Hague T. Computer-vision based hoe guidance for cereals an initial trail. J. Agr. Eng. Res. 1999, 74:225-326.
46. Torii T. Research in autonomous agriculture vehicles in Japan. Comput. Electron. Agr. 2000, 25:133-153.
47. Van Evert F.K., Van Der Heijden G., Lotz L., Polder G., Lamaker A., De Jong A., Kuyper M., Groendijk E., Neeteson J., Van Der Zalm T. A mobile field robot with vision-based detection of volunteer potato plants in a corn crop. Weed Technol. 2006, 20:853-861.
48. Vioix J., Douzals J., Truchetet F., Assémat L., Guillemin J. Spatial and spectral methods for weed detection and localization. EURASIP JASP 2002, 7:679-685.
49. Woebbecke D., Meyer G., VonBargen K., Mortensen D. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 1995, 38(1):271-281.
50. Yang C., Prasher S., Landry J. Weed recognition in corn fields using back-propagation neural network models. Canadian Byosistems Engineering, vol. 44 2002, pp. 7.15-7.22.
51. Yang C., Prasher S., Landry J., H.S.R. Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications. Precis. Agr. 2003, 4:5-18.
52. Zheng L., Zhang J., Wang Q. Mean-shift-based color segmentation of images containing green vegetation. Comput. Electron. Agr. 2009, 65:93-98.