Analysis of natural images processing for the extraction of agricultural elements

Image and Vision Computing

Volumn 28, Issue 1, 2010, Pages 138-149

  Burgos Artizzu, Xavier P ^a   Ribeiro, Angela ^a   Tellaeche, Alberto ^b   Pajares, Gonzalo ^c   Fernández Quintanilla, Cesar ^d  

^a Consejo Superior de Investigaciones Científicas   (Spain)

^b UNIVERSIDAD NACIONAL DE EDUCACIÓN A DISTANCIA   (Spain)

^c UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

^d INSTITUTO DE CIENCIAS AGRARIAS   (Spain)

Author keywords

Computer vision; Genetic algorithms; Parameter setting; Precision agriculture; Weed detection

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER VISION; CROPS; GENETIC ALGORITHMS; REFINING; SOILS; VEGETATION; WEED CONTROL;

BEST VALUE; COMPLEX ENVIRONMENTS; CORRELATION COEFFICIENT; CROP FIELDS; INPUT PARAMETER; NATURAL IMAGES; PARAMETER SETTING; PRECISION AGRICULTURE; REALTIME PROCESSING; REGION OF INTEREST; VISUAL DETECTION; WEED DETECTION; WHOLE SYSTEMS; WINTER CEREALS;

COMPUTATIONAL METHODS;

EID: 70449559428     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2009.05.009     Document Type: Article

References (46)

1. Kropff M., Wallinga J., and Lotz L. Modelling for precision weed management. Precision Agriculture: Spatial and Temporal Variability of Environmental Quality (1997), Wiley, Chester 182-204
2. Earl R., Wheeler P., Blackmore B.S., and Godwin R. Precision farming: the management of variability. Landwards 51 4 (1996) 18-23
3. Cressie N. Statistics for Spatial Data (1993), Wiley, New York
4. Thorp K., and Tian L. A review on remote sensing of weeds in agriculture. Precision Agriculture 5 (2004) 477-508
5. Menges R.M., Nixon P.R., and Richardson A.J. Light reflectance and remote sensing of weeds in agronomic and horticultural crops. Weed Science 33 (1985) 569-581
6. A.J. Richardson, R.M. Menges, P.R. Nixon, Distinguishing weed from crop plants using video remote sensing, in: Photogrammetric Engineering and Remote Sensing, vol. 51, 1985, pp. 1785-1790.
7. López Granados F., Jurado-Expósito M., Atenciano Núez S., García-Ferrer A., Sánchez dela Orden M., and L G.-T. Spatial variability of agricultural soils in southern Spain. Plant and Soil 246 (2002) 97-105
8. Meyer G., and Neto J. Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture 63 (2008) 282-293
9. Woebbecke D., Meyer G., VonBargen K., and Mortensen D. Color indices for weed identification under various soil, residue and lighting conditions. Transactions of the American Society of Agricultural Engineers 38 1 (1995) 271-281
10. Andreasen C., Rudemo M., and Sevestre S. Assessment of weed density at an early stage by use of image processing. Weed Research 37 (1997) 5-18
11. Pérez A., López F., Benlloch J., and Christensen S. Colour and shape analysis techniques for weed detection in celeral fields. Computers and Electronics in Agriculture 25 (2000) 197-212
12. Aitkenhead M., Dalgetty I., Mullins C., McDonald A., and Strachan N. Weed and crop discrimination using image analysis and artificial intelligence methods. Computers and Electronics in Agriculture 39 (2003) 157-171
13. Yang C., Prasher S., Landry J., and R H.S. Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications. Precision Agriculture 4 (2003) 5-18
14. Á. Ribeiro, C. Fernández-Quintanilla, J. Barroso, M.C. García-Alegre, Development of an image analysis system for estimation of weed, in: Proceedings of the Fifth European Conference on Precision Agriculture (5ECPA), 2005, pp. 169-174.
15. Van Evert F.K., Van Der Heijden G.W., Lotz L.A.P., Polder G., Lamaker A., De Jong A., Kuyper M.C., Groendijk E.J., Neeteson J.J., and Van Der Zalm T. A mobile field robot with vision-based detection of volunteer potato plants in a corn crop. Weed Technology 20 (2006) 853-861
16. W. Lee, D. Slaughter, D. Giles, Development of a machine vision system for weed control using precision chemical application., in: Proceedings of International Conference on Agricultural Machinery Engineering'96, Seoul, Korea, 1996, pp. 802-811.
17. Lee W., Slaughter D., and Giles D. Robotic weed control system for tomatoes using machine vision and precision chemical application. Precision Agriculture 1 (1999) 95-113
18. Hemming J., and Rath T. Computer-vision based weed identification under field conditions using controlled lighting. Journal of Agricultural Engineering Research 78 3 (2001) 233-243
19. Blasco J., Aleixos N., Roger J., Rabatel G., and Moltó E. Robotic weed control using machine vision. Biosystems Engineering 83 2 (2002) 149-157
20. Martí J., Freixenet J., Batlle J., and Casals A. A new approach to outdoor scene description based on learning and top-down segmentation. Image and Vision Computing 19 (2001) 1041-1055
21. Bosch A., Muñoz X., and Freixenet J. Segmentation and description of natural outdoor scenes. Image and Vision Computing 25 (2007) 727-740
22. Gerhards R., and Christensen S. Real-time weed detection, decision making and patch spraying in maize sugarbeet winter wheat and winter barley. Weed Research 43 (2003) 385-392
23. Onyango C., and Marchant J. Segmentation of row crop plants from weeds using colour and morphology. Computers and Electronics in Agriculture 39 (2003) 141-155
24. Piron A., Leemans V., Kleynen O., Lebeau F., and Destain M.-F. Selection of the most efficient wavelength bands for discriminating weeds from crop. Computers and Electronics in Agriculture 62 (2008) 141-148
25. 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 Research 46 (2006) 185-193
26. Granitto P., Verdes P., and Ceccatto H. Large-scale investigation of weed seed identification by machine vision. Computers and Electronics in Agriculture 47 (2005) 15-24
27. Astrand B., and Baerveldt A. An agricultural mobile robot with vision-based perception for mechanical weed control. Autonomous Robots 13 (2002) 21-35
28. Burks T., Shearer S., Heath J., and Donouhue K. Evaluation of neural network classifiers for weed species discrimination. Biosystems Engineering 91 3 (2005) 293-304
29. C. Yang, S. Prasher, J. Landry, Weed recognition in corn fields using back-propagation neural network models, in: Canadian Biosystems Engineering, vol. 44, 2002, pp. 7.15-7.22.
30. Tian L., Reid J., and Hummel J. Development of a precision sprayer for site-specific weed management. Transactions of the American Society of Agricultural Engineers 42 (1999) 893-900
31. Tang L., Tian L., Steward B., and Reid J. Texture-based weed classification using Gabor wavelets and neural network for real-time selective herbicide applications. Transactions of the American Society of Agricultural Engineers 99 (1999) 7035
32. Billingsley J., and Schoenfisch M. The successful development of a vision guidance system for agriculture. Computers and Electronics in Agriculture 16 (1997) 147-163
33. Hague T., and Tillet N. A bandpass filter-based approach to crop row location and tracking. Mechatronics 11 (2001) 1-12
34. R. Gottschalk, X.P. Burgos-Artizzu, Á. Ribeiro, G. Pajares, Á. Sánchez-Miralles, Real-time image processing for the guidance of a small agricultural field inspection vehicle, in: Proceedings of the 15th International Conference on Mechatronics and Machine Vision in Practice, (M2VIP'08) ISBN:978-0-473-13532-4, Auckland, New Zealand, 2008.
35. Geé C., Bossu J., Jones G., and Truchetet F. Crop weed discrimination in perspective agronomic images. Computers and Electronics in Agriculture 60 (2008) 49-59
36. Tellaeche A., Burgos-Artizzu X.P., Pajares G., and Ribeiro A. A new vision-based approach to differential spraying in precision agriculture. Computers and Electronics in Agriculture 60 (2008) 144-155
37. Olsen H. Determination of row position in small-grain crops by analysis of video images. Computers and Electronics in Agriculture 12 (1995) 147-162
38. Sogaard H., and Olsen H. Determination of crop rows by image analysis without segmentation. Computers and Electronics in Agriculture 38 (2003) 141-158
39. Stafford J.V. Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research 76 (2000) 267-275
40. Holland J. Adaptation in Natural and Artificial Systems (1975), The University of Michigan Press, Ann Arbor, MI
41. Davis L. Genetic Algorithms and Simulated Annealing (1987), Morgan Kaufman, San Mateo, CA
42. Goldberg D. Genetic Algorithms in Search: Optimization and Machine Learning (1989), Addison-Wesley Professional Publishers, Reading, MA
43. Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 (2000) 182-197
44. Marti R., and Laguna M. Scatter search: design and advance strategies. Revista Iberoamericana de Inteligencia Artificial 19 (2003) 123-130
45. X.P. Burgos-Artizzu, A. Ribeiro, Un sistema de razonamiento basado en casos en el análisis de imágenes naturales, in: 5ta. Conferencia Iberoamericana en Sistemas, Cibernética e Informática (CISCI 2006), vol. 1, Orlando, USA, 2006, pp. 140-145.
46. Burgos-Artizzu X.P., Ribeiro A., and de Santos M. Controlador borroso multivariable para el ajuste de tratamientos en agricultura de precisin. Revista Iberoamericana de Automática e Informática Industrial 4 2 (2007) 64-71