Merge fuzzy visual servoing and GPS-based planning to obtain a proper navigation behavior for a small crop-inspection robot

Sensors (Switzerland)

Volumn 16, Issue 3, 2016, Pages

  Bengochea Guevara, José M ^a   Conesa Muñoz, Jesus ^a   Andújar, Dionisio ^a   Ribeiro, Angela ^a  

^a CSIC   (Spain)

Author keywords

Crop inspection; Fuzzy control; Generation of autonomous behaviour; GPS; Precision agriculture; Visual servoing

Indexed keywords

AGRICULTURE; CAMERAS; CRASHWORTHINESS; CROPS; FUZZY CONTROL; GLOBAL POSITIONING SYSTEM; IMAGE ACQUISITION; IMAGE PROCESSING; INSPECTION; ROBOT PROGRAMMING; SOIL MECHANICS; VISUAL SERVOING;

AUTONOMOUS MOBILE ROBOT; FUZZY VISUAL SERVOING; GENERATION OF AUTONOMOUS BEHAVIOUR; IMAGE PROCESSING - METHODS; LIGHTING CONDITIONS; NAVIGATION BEHAVIOR; PRECISION AGRICULTURE; VISION-GUIDED NAVIGATION;

ROBOTS;

AGRICULTURE; CROP; DEVICES; GEOGRAPHIC INFORMATION SYSTEM; GROWTH, DEVELOPMENT AND AGING; HUMAN; IMAGE PROCESSING; MAIZE; PROCEDURES; ROBOTICS;

AGRICULTURE; CROPS, AGRICULTURAL; GEOGRAPHIC INFORMATION SYSTEMS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; ROBOTICS; ZEA MAYS;

EID: 84959199510     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s16030276     Document Type: Article

References (65)

1. Gebbers, R.; Adamchuk, V.I. Precision agriculture and food security. Science 2010, 327, 828–831.
2. Srinivasan, A. Handbook of Precision Agriculture: Principles and Applications; Food Products Press: New York, NY, USA, 2006.
3. Stafford, J.V. Implementing precision agriculture in the 21st century. J. Agric. Eng. Res. 2000, 76, 267–275.
4. Reid, J.F. The impact of mechanization on agriculture. Bridge Agric. Inf. Technol. 2011, 41, 22–29.
5. Senay, G.B.; Ward, A.D.; Lyon, J.G.; Fausey, N.R.; Nokes, S.E. Manipulation of high spatial resolution aircraft remote sensing data for use in site-specific farming. Trans. ASAE 1998, 41, 489–495.
6. 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.
7. Marshall, E.J.P. Field-scale estimates of grass weed populations in arable land. Weed Res. 1988, 28, 191–198.
8. Slaughter, D.C.; Giles, D.K.; Downey, D. Autonomous robotic weed control systems: A review. Comput. Electron. Agric. 2008, 61, 63–78.
9. Li, M.; Imou, K.; Wakabayashi, K.; Yokoyama, S. Review of research on agricultural vehicle autonomous guidance.Int. J. Agric. Biol. Eng. 2009, 2, 1–16.
10. Stoll, A.; Kutzbach, H.D. Guidance of a forage harvester with GPS. Precis. Agric. 2000, 2, 281–291.
11. Blackmore, B.S.; Griepentrog, H.W.; Nielsen, H.; Nørremark, M.; Resting-Jeppesen, J. Development of a deterministic autonomous tractor. In Proceedings of the 2004 CIGR Olympics of Agricultural Engineering, Beijing, China, 11–14 October 2004.
12. Gomez-Gil, J.; Alonso-Garcia, S.; Gómez-Gil, F.J.; Stombaugh, T. A simple method to improve autonomous GPS positioning for tractors. Sensors 2011, 11, 5630–5644.
13. Alonso-Garcia, S.; Gomez-Gil, J.; Arribas, J.I. Evaluation of the use of low-cost GPS receivers in the autonomous guidance of agricultural tractors. Spanish J. Agric. Res. 2011, 9, 377–388.
14. Eaton, R.; Katupitiya, J.; Siew, K.W.; Howarth, B. Autonomous farming: Modelling and control of agricultural machinery in a unified framework. Int. J. Intel. Syst. Technol. Appl. 2010, 8, 444–457.
15. Kise, M.; Noguchi, N.; Ishii, K.; Terao, H. The development of the autonomous tractor with steering controller applied by optimal control. In Proceeding of the 2002 Automation Technology for Off-Road Equipment, Chicago, IL, USA, 26–27 July 2002; pp. 367–373.
16. Nagasaka, Y.; Umeda, N.; Kanetai, Y.; Taniwaki, K.; Sasaki, Y. Autonomous guidance for rice transplanting using global positioning and gyroscopes. Comput. Electron. Agric. 2004, 43, 223–234.
17. Noguchi, N.; Kise, M.; Ishii, K.; Terao, H. Field automation using robot tractor. In Proceeding of the 2002 Automation Technology for Off-Road Equipment, Chicago, IL, USA, 26–27 July 2002; pp. 239–245.
18. Bak, T.; Jakobsen, H. Agricultural robotic platform with four wheel steering for weed detection. Biosyst. Eng. 2004, 87, 125–136.
19. Marchant, J.A.; Hague, T.; Tillett, N.D. Row-following accuracy of an autonomous vision-guided agricultural vehicle.Comput. Electron. Agric. 1997, 16, 165–175.
20. Billingsley, J.; Schoenfisch, M. The successful development of a vision guidance system for agriculture. Comput. Electron. Agric. 1997, 16, 147–163.
21. Slaughter, D.C.; Chen, P.; Curley, R.G. Vision guided precision cultivation. Precis. Agric. 1999, 1, 199–217.
22. Tillett, N.D.; Hague, T. Computer-vision-based hoe guidance for cereals—An initial trial. J. Agric. Eng. Res. 1999, 74, 225–236.
23. Åstrand, B.; Baerveldt, A.-J. A vision based row-following system for agricultural field machinery. Mechatronics 2005,15, 251–269.
24. Benson, E.R.; Reid, J.F.; Zhang, Q. Machine vision-based guidance system for agricultural grain harvesters using cut-edge detection. Biosyst. Eng. 2003, 86, 389–398.
25. Gottschalk, R.; Burgos-Artizzu, X.P.; Ribeiro, A.; Pajares, G. Real-time image processing for the guidance of a small agricultural field inspection vehicle. Int. J. Intel. Syst. Technol. Appl. 2010, 8, 434–443.
26. Kise, M.; Zhang, Q.; Rovira Más, F. A stereovision-based crop row detection method for tractor-automated guidance.Biosyst. Eng. 2005, 90, 357–367.
27. Sainz-Costa, N.; Ribeiro, A.; Burgos-Artizzu, X.P.; Guijarro, M.; Pajares, G. Mapping wide row crops with video sequences acquired from a tractor moving at treatment speed. Sensors 2011, 11, 7095–7109.
28. Søgaard, H.T.; Olsen, H.J. Determination of crop rows by image analysis without segmentation. Comput. Electron. Agric. 2003, 38, 141–158.
29. Hague, T.; Tillett, N.D.; Wheeler, H. Automated crop and weed monitoring in widely spaced cereals. Precis. Agric.2006, 7, 21–32.
30. Hough, P.V. Method and Means for Recognizing Complex Patterns. US Patent US3069654 A, December 1962.
31. Marchant, J.A. Tracking of row structure in three crops using image analysis. Comput. Electron. Agric. 1996, 15, 161–179.
32. Hague, T.; Marchant, J.A.; Tillett, D. A system for plant scale husbandry. Precis. Agric. 1997, 2, 635–642.
33. Leemans, V.; Destain, M.-F. Application of the Hough transform for seed row localisation using machine vision.Biosyst. Eng. 2006, 94, 325–336.
34. 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.
35. Tellaeche, A.; BurgosArtizzu, X.P.; Pajares, G.; Ribeiro, A.; Fernández-Quintanilla, C. A new vision-based approach to differential spraying in precision agriculture. Comput. Electron. Agric. 2008, 60, 144–155.
36. Tellaeche, A.; Pajares, G.; Burgos-Artizzu, X.P.; Ribeiro, A. A computer vision approach for weeds identification through Support Vector Machines. Appl. Soft Comput. 2011, 11, 908–915.
37. Kise, M.; Zhang, Q. Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance. Biosyst. Eng. 2008, 101, 191–198.
38. Vioix, J.-B.; Douzals, J.-P.; Truchetet, F.; Assémat, L.; Guillemin, J.-P. Spatial and spectral methods for weed detection and localization. EURASIP J. Appl. Sign. Process. 2002, 2002, 679–685.
39. Bossu, J.; Gée, C.; Guillemin, J.P.; Truchetet, F. Development of methods based on double Hough transform and Gabor filtering to discriminate crop and weeds in agronomic images. In Proceedings of the SPIE 18th Annual Symposium Electronic Imaging Science and Technology, San Jose, CA, USA, 15–19 January 2006.
40. Bossu, J.; Gée, C.; Jones, G.; Truchetet, F. Wavelet transform to discriminate between crop and weed in perspective agronomic images. Comput. Electron. Agric. 2009, 65, 133–143.
41. Romeo, J.; Pajares, G.; Montalvo, M.; Guerrero, J.M.; Guijarro, M.; Ribeiro, A. Crop row detection in maize fields inspired on the human visual perception. Sci. World J. 2012, 2012.
42. Conesa-Muñoz, J.; Gonzalez-de-Soto, M.; Gonzalez-de-Santos, P.; Ribeiro, A. Distributed multi-level supervision to effectively monitor the operations of a fleet of autonomous vehicles in agricultural tasks. Sensors 2015, 15, 5402–5428.
43. Bochtis, D.D.; Sørensen, C.G. The vehicle routing problem in field logistics part I. Biosyst. Eng. 2009, 104, 447–457.
44. Conesa-Munoz, J.; Bengochea-Guevara, J.M.; Andujar, D.; Ribeiro, A. Efficient Distribution of a Fleet of Heterogeneous Vehicles in Agriculture: A Practical Approach to Multi-path Planning. In Proceedings of the 2015 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Vila Real, Portugal, 8–10 April 2015; pp. 56–61.
45. Meyer, G.E.; Neto, J.C. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 2008, 63, 282–293.
46. Woebbecke, D.M.; Meyer, G.E.; Von Bargen, K.; Mortensen, D.A. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 1995, 38, 259–269.
47. 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.
48. Perez, A.J.; Lopez, F.; Benlloch, J.V.; Christensen, S. Colour and shape analysis techniques for weed detection in cereal fields. Comput. Electron. Agric. 2000, 25, 197–212.
49. Aitkenhead, M.J.; Dalgetty, I.A.; Mullins, C.E.; McDonald, A.J.S.; Strachan, N.J.C. Weed and crop discrimination using image analysis and artificial intelligence methods. Comput. Electron. Agric. 2003, 39, 157–171.
50. 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.
51. Ribeiro, A.; Fernández-Quintanilla, C.; Barroso, J.; García-Alegre, M.C.; Stafford, J.V. Development of an image analysis system for estimation of weed pressure. In Proceedings of 5th European Conference on Precision Agriculture, Uppsala, Sweden, 9–12 June 2005; pp. 169–174.
52. Van Evert, F.K.; Van Der Heijden, G.W.; Lotz, L.A.; Polder, G.; Lamaker, A.; De Jong, A.; Kuyper, M.C.; Groendijk, E.J.; Neeteson, J.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.
53. 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.
54. Bengochea-Guevara, J.M.; Burgos Artizzu, X.P.; Ribeiro, A. Real-time image processing for crop/weed discrimination in wide-row crops. In Proceedings of RHEA, Madrid, Spain, 21–23 May 2014; pp. 477–488.
55. Sheikholeslam, S.; Desoer, C. Design of decentralized adaptive controllers for a class of interconnected nonlinear dynamical systems. In Proceedings of the 31st IEEE Conference on Decision and Control, Tucson, AZ, USA, 16–18 December 1992; pp. 284–288.
56. Rossetter, E.J.; Gerdes, J.C. Performance guarantees for hazard based lateral vehicle control. In Proceedings of the ASME 2002 International Mechanical Engineering Congress and Exposition, New Orleans, LA, USA, 17–22 November 2002; pp. 731–738.
57. Pomerleau, D.A. Alvinn: An Autonomous Land Vehicle in a Neural Network; Technical Report CMU-CS-89–107; Carnegie Mellon University: Pittsburgh, PA, USA, 1989.
58. Sugeno, M. On stability of fuzzy systems expressed by fuzzy rules with singleton consequents. IEEE Trans. Fuzzy Syst.1999, 7, 201–224.
59. Zadeh, L.A. Fuzzy sets. Inf. Control 1965, 8, 338–353.
60. Fraichard, T.; Garnier, P. Fuzzy control to drive car-like vehicles. Rob. Autom. Syst. 2001, 34, 1–22.
61. Naranjo, J.E.; Sotelo, M.; Gonzalez, C.; Garcia, R.; Sotelo, M.A. Using fuzzy logic in automated vehicle control. IEEE Intell. Syst. 2007, 22, 36–45.
62. Pradhan, S.K.; Parhi, D.R.; Panda, A.K. Fuzzy logic techniques for navigation of several mobile robots. Appl. Soft Comput. 2009, 9, 290–304.
63. Kodagoda, K.R.S.; Wijesoma, W.S.; Teoh, E.K. Fuzzy speed and steering control of an AGV. IEEE Trans. Control Syst. Technol. 2002, 10, 112–120.
64. Antonelli, G.; Chiaverini, S.; Fusco, G. A fuzzy-logic-based approach for mobile robot path tracking. IEEE Trans. Fuzzy Syst. 2007, 15, 211–221.
65. Digital.CSIC. Avalaible online: http://digital.csic.es/handle/10261/110162 (accessed on 11 December 2015).