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Special Sensors for Autonomous Navigation Systems in Crops Investigation System

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Virtual and Augmented Reality for Automobile Industry: Innovation Vision and Applications

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 412))

Abstract

Mobile robots functioning in farmlands have been an important focus for scientists. The fast improvement in communication, sensing and computer technology has given considerable advances to Robot navigation guidance techniques in agriculture. Automatic autonomous robots minimize work expenses, avoid dangerous activities from being carried out by people and give farmers timely and accurate data to help management choices. Appropriate methods for sensing, mapping, localization, trajectory planning, and preventing obstacles are designed through research into robot sensor technologies in agricultural contexts. The Navigation Algorithms must use visual information to determine an acceptable course, execute a selection and navigate appropriately without collisions in its environment. A summary of sensor technology for autonomous prototype vehicles is presented and discussed in this chapter. Navigating sensors, computer methods, and navigation management approaches are the main aspects. Crucial procedures include selecting, coordinating, and combining the appropriate Sensors to provide essential robotics navigational knowledge. For function extraction, processing of data and fusing computationally efficiently are utilized. The steering controllers give the correct steering motion to operate automated vehicles for autonomous navigation. Mobile robots are still an open topic in outside contexts such as in agriculture. To address the challenges posed by the climatic conditions, dynamic surroundings, unforeseen obstructions, terrain variations, and vegetation, it is necessary to provide effective and powerful protective and actuators technologies for mobility farming robotics. In this chapter, we will discuss about special sensor keep monitoring through GPS system requirement of crops and to improve and fine growth of quality seeds.

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References

  1. Ahamed, T., Kulmutiwat, S., Thanpattranon, P., Tuntiwut, S., Ryozo, N., Takigawa, T.: Monitoring of plant growth using laser range finder. In: 2011 ASABE Annual International Meeting, Louisville, Kentucky, Chapter Number 1111373 (2011)

    Google Scholar 

  2. Ampatzidis, Y., Vougioukas, S., Bochtis, D., Tsatsarelis, C.: A yield mapping system for hand-harvested fruits based on RFID and GPS location technologies: field testing. Precis. Agric. 10, 63–72 (2009)

    Article  Google Scholar 

  3. Andersen, J.C., Ravn, O., Andersen, N.A.: Autonomous rule-based robot navigation in orchards, In: 7th IFAC Symposium on Intelligent Autonomous Vehicles, Lecce, Italy, pp. 43–48 (2010)

    Google Scholar 

  4. Åstrand, B., Baerveldt, A.: A vision-based row-following system for agricultural field machinery. Mechatronics 15, 251–269 (2005)

    Article  Google Scholar 

  5. AuatCheein, F., Steiner, G., Perez Paina, G., Carelli, R.: Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection. Comput. Electron. Agric. 78, 195–207 (2011)

    Article  Google Scholar 

  6. Barawid, O., Jr., Mizushima, A., Ishii, K., Noguchi, N.: Development of an autonomous navigation system using a two-dimensional laser scanner in an orchard application. Biosys. Eng. 96, 139–149 (2007)

    Article  Google Scholar 

  7. Ayala, M., Soria, C., Carelli, R.: Visual servo control of a mobile robot in agriculture environments. Mech. Based Des. Struct. Mach. 36, 392–410 (2008)

    Article  Google Scholar 

  8. Benson, E., Reid, J., Zhang, Q.: Machine vision-based guidance system for agricultural grain harvesters using cut-edge detection. Biosys. Eng. 86, 389–398 (2003)

    Article  Google Scholar 

  9. Benson, E., Stombaugh, T., Noguchi, N., Will, J., Reid, J.: An evaluation of a geomagnetic direction sensor for vehicle guidance in precision agriculture applications. In: 1998 ASAE Annual International Meeting, Orlando, FL, Chapter Number 983203 (1998)

    Google Scholar 

  10. Billingsley, J., Schoenfisch, M.: The successful development of a vision guidance system for agriculture. Comput. Electron. Agric. 16, 147–163 (1997)

    Article  Google Scholar 

  11. Cho, S., Lee, J.: Autonomous speedsprayer using differential global positioning system, genetic algorithm and fuzzy control. J. Agric. Eng. Res. 76, 111–119 (2000)

    Article  Google Scholar 

  12. Christiansen, M.: Localization in orchards using extended Kalman filter for sensor-fusion. Master thesis, University of Southern Denmark (2011)

    Google Scholar 

  13. Gao, F., Xun, Y., Wu, J., Bao, G., Tan, Y.: Navigation line detection based on robotic vision in natural vegetation-embraced environment. In: 2010 3rd International Congress on Image and Signal Processing (CISP), pp. 2596–2600 (2010)

    Google Scholar 

  14. Gée, C., Bossu, J., Jones, G., Truchetet, F.: Crop/weed discrimination in perspective agronomic images. Comput. Electron. Agric. 60, 49–59 (2008)

    Article  Google Scholar 

  15. 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. Intell. Syst. Technol. Appl. 8, 434–443 (2010)

    Google Scholar 

  16. Grift, T., Zhang, Q., Kondo, N., Ting, K.: A review of automation and robotics for the bioindustry. J. Biomechatronics Eng. 1, 37–54 (2008)

    Google Scholar 

  17. Guivant, J.E., Masson, F.R., Nebot, E.M.: Simultaneous localization and map building using natural features and absolute information. Robot. Auton. Syst. 40, 79–90 (2002)

    Article  Google Scholar 

  18. Hagras, H., Callaghan, V., Colley, M.: Learning and adaptation of an intelligent mobile robot navigator operating in unstructured environment based on a novel online fuzzy-genetic system. Fuzzy Sets Syst. 141, 107–160 (2004)

    Article  Google Scholar 

  19. Hamner, B., Singh, S., Bergerman, M.: Improving orchard efficiency with autonomous utility vehicles. In: 2010 ASABE Annual International Meeting, Pittsburgh, PA, Chapter Number 1009415 (2010)

    Google Scholar 

  20. Debain, C., Malartre, F., Delmas, P., Chapuis, R., Humbert, T., Berducat, M.: Smart range finder and camera fusion-application to real-time dense digital elevation map estimation. In: Robotics 2010, International Workshop of Mobile Robotics for Environment/Agriculture, Clermont-Ferrand, France (2010)

    Google Scholar 

  21. Ding, Y., Chen, D., Wang, S.: The mature wheat cut and uncut edge detection method based on wavelet image rotation and projection. Afr. J. Agric. Res. 6, 2609–2616 (2011)

    Google Scholar 

  22. Eaton, R., Katupitiya, J., Siew, K.W., Howarth, B.: Autonomous farming: modelling and control of agricultural machinery in a unified framework. Int. J. Intell. Syst. Technol. Appl. 8, 444–457 (2010)

    Google Scholar 

  23. Ericson, S., Astrand, B.: Row-detection on an agricultural field using omnidirectional camera. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), p. 49824987 (2010)

    Google Scholar 

  24. Jiang, H., Xiao, Y., Zhang, Y., Wang, X., Tai, H.: Curve path detection of unstructured roads for the outdoor robot navigation. Math. Comput. Model. (2011)

    Google Scholar 

  25. Joshi, M.M., Zaveri, M.A.: Reactive Navigation of autonomous mobile robot using neuro-fuzzy system. Int. J. Robot. Autom. (IJRA) 2, 128–145 (2011)

    Google Scholar 

  26. Han, S., Zhang, Q., Noh, H.: Kalman filtering of DGPS positions for a parallel tracking application. Trans. Am. Soc. Agric. Eng. 45, 553–560 (2002)

    Google Scholar 

  27. Han, S., Zhang, Q., Ni, B., Reid, J.: A guidance directrix approach to vision-based vehicle guidance systems. Comput. Electron. Agric. 43, 179–195 (2004)

    Article  Google Scholar 

  28. Hansen, S., Bayramoglu, E., Andersen, J.C., Ravn, O., Andersen, N., Poulsen, N.K.: Orchard navigation using derivative free Kalman filtering. In: 2011 IEEE Proceedings of the American Control Conference (ACC), pp. 4679–4684 (2011)

    Google Scholar 

  29. Harper, N., McKerrow, P.: Recognising plants with ultrasonic sensing for mobile robot navigation. Robot. Auton. Syst. 34, 71–82 (2001)

    Article  Google Scholar 

  30. Hellström, T.: Autonomous navigation for forest machines. A project pre-study in the Department of Computer Science Umea University, Sweden (2002)

    Google Scholar 

  31. Iida, M., Burks, T.: Ultrasonic sensor development for automatic steering control of orchard tractor. In: Proceedings of the Automation Technology for Off-Road Equipment, Chicago, Illinois, p. 221229 (2002)

    Google Scholar 

  32. Kaizu, Y., Imou, K.: A dual-spectral camera system for paddy rice seedling row detection. Comput. Electron. Agric. 63, 49–56 (2008)

    Article  Google Scholar 

  33. Libby, J., Kantor, G.: Deployment of a point and line feature localization system for an outdoor agriculture vehicle. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, pp. 1565–1570 (2011)

    Google Scholar 

  34. Kise, M., Zhang, Q., Rovira Más, F.: A stereovision-based crop row detection method for tractor-automated guidance. Biosys. Eng. 90, 357–367 (2005)

    Article  Google Scholar 

  35. Kise, M., Noguchi, N., Ishii, K., Terao, H.: The development of the autonomous tractor with steering controller applied by optimal control. In: Proceedings of the Conference on Automation Technology for Off-Road Equipment, Chicago, USA, pp. 367–373 (2002)

    Google Scholar 

  36. Kurashiki, K., Fukao, T., Ishiyama, K., Kamiya, T., Murakami, N.: Orchard traveling UGV using particle filter-based localization and inverse optimal control. In: Proceedings of the 2010 IEEE/SICE International Symposium on System Integration (SII). IEEE, pp. 31–36 (2010)

    Google Scholar 

  37. Leemans, V., Destain, M.-F.: Line cluster detection using a variant of the Hough transform for culture row localisation. Image Vis. Comput. 24, 541–550 (2006)

    Article  Google Scholar 

  38. Li, M., Imou, K., Wakabayashi, K., Yokoyama, S.: Review of research on agricultural vehicle autonomous guidance. Int. J. Agric. Biol. Eng. 2, 116 (2009)

    Google Scholar 

  39. Anuradha, K.K., Singh, S.: Two stage classification model for crop disease prediction. Int. J. Comput. Sci. Mob. Comput. 4(6), 254–259 (2015)

    Google Scholar 

  40. Lulio, L.C., Tronco, M.L., Porto, A.J.: Cognitive-merged statistical pattern recognition method for image processing in mobile robot navigation. In: Proceedings of the 2012 Brazilian Robotics Symposium and Latin American Robotics Symposium. IEEE, pp. 279–283 (2012)

    Google Scholar 

  41. Marichal, G., Acosta, L., Moreno, L., Mendez, J., Rodrigo, J., Sigut, M.: Obstacle avoidance for a mobile robot: a neuro-fuzzy approach. Fuzzy Sets Syst. 124, 171–179 (2001)

    Article  MathSciNet  Google Scholar 

  42. Martín, D., Guinea, D., García-Alegre, M., Villanueva, E., Guinea, D.: Fuzzy steering control of a hydraulic tractor and laser perception obstacle avoidance strategies. In: Proceedings of the XV CongresoEspanolSobreTecnologıas y Logica Fuzzy (ESTYLF), Huelva, Spain, pp. 349–355 (2010)

    Google Scholar 

  43. Nagasaka, Y., Umeda, N., Kanetai, Y., Taniwaki, K., Sasaki, Y.: Autonomous guidance for rice transplanting using global positioning and gyroscopes. Comput. Electron. Agric. 43, 223–234 (2004)

    Article  Google Scholar 

  44. Okamoto, H., Hamada, K., Kataoka, T., Terawaki, M., Hata, S.: Automatic guidance system with crop row sensor. In: Proceedings of the Automation Technology for Off-road Equipment, Chicago, USA, pp. 307–316 (2002)

    Google Scholar 

  45. Noguchi, N., Kise, M., Ishii, K., Terao, H.: Field automation using robot tractor. In: Proceedings of the Automation Technology for Off-Road Equipment, Chicago, USA, pp. 239–245 (2002)

    Google Scholar 

  46. Nørremark, M., Griepentrog, H.W., Nielsen, J., Søgaard, H.T.: The development and assessment of the accuracy of an autonomous GPS-based system for intra-row mechanical weed control in row crops. Biosys. Eng. 101, 396–410 (2008)

    Article  Google Scholar 

  47. Ortiz, J.M., Olivares, M.: A vision-based navigation system for an agricultural field robot. In: IEEE 3rd Latin American Robotics Symposium, 2006, LARS'06, pp. 106–114 (2006)

    Google Scholar 

  48. Rovira-Mas, F., Han, S., Wei, J., Reid, J.: Fuzzy logic model for sensor fusion of machine vision and GPS in autonomous navigation. In: 2005 ASAE Annual International Meeting, Tampa, FL, USA, Chapter Number 051156 (2005)

    Google Scholar 

  49. Rovira-Más, F.: Sensor architecture and task classification for agricultural vehicles and environments. Sensors 10, 11226–11247 (2010)

    Article  Google Scholar 

  50. Rovira-Más, F., Zhang, Q., Reid, J.F.: Stereo vision three-dimensional terrain maps for precision agriculture. Comput. Electron. Agric. 60, 133–143 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing Science and Engineering, Galgotias University, Greater Noida, India

    Kuldeep Singh Kaswan

  2. Department of Computer Science & Applications, PDM University, Bahadurgarh, Haryana, India

    Jagjit Singh Dhatterwal

  3. Chitkara University Institute of Engineering & Technology, Chitkara University, Rajpura, Punjab, India

    Anupam Baliyan

  4. Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, India

    Vishal Jain

Authors
  1. Kuldeep Singh Kaswan
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  2. Jagjit Singh Dhatterwal
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  3. Anupam Baliyan
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  4. Vishal Jain
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Editor information

Editors and Affiliations

  1. Faculty of Computer and AI, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Deepak Gupta

  3. Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Ashish Khanna

  4. Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland

    Adam Slowik

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Kaswan, K.S., Dhatterwal, J.S., Baliyan, A., Jain, V. (2022). Special Sensors for Autonomous Navigation Systems in Crops Investigation System. In: Hassanien, A.E., Gupta, D., Khanna, A., Slowik, A. (eds) Virtual and Augmented Reality for Automobile Industry: Innovation Vision and Applications. Studies in Systems, Decision and Control, vol 412. Springer, Cham. https://doi.org/10.1007/978-3-030-94102-4_4

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