Development of a Guidance System for an Agricultural Wheeled Robotic Platform in Row Crop Fields

Document Type : Original Research

Authors

Biosystem Mechanical Engineering Department, Faculty of Agriculture, Tabriz University, Tabriz, Iran.

Abstract

Smart and precision agriculture seeks to boost the efficiency of operations and crop yield by using modern technology. Modern tools such as sensors, imagery cameras, and deep learning enable farmers to identify and control weeds, pests, and diseases in real-time. A robotic platform can carry these modern types of equipment and achieve the mentioned objectives precisely. Automatic and accurate navigation of this autonomous robot in agricultural fields is essential for performing these precision tasks. An agricultural robotic platform was designed and developed for row crop fields. The robot navigation system comprises two main components: a vision-based row detection system for path tracking and a motion controller system. The vision-based guidance system processes acquired image data from a tilted camera in front of the robot to identify the crop row's position. The Hough transform method was used to determine the position of the crop rows. Using the resultant guidance line equations, the motion controller directs the robot to move automatically between rows without harming the crops. Differential speed steering allows both wheels on the robot to rotate at different speeds. The steering system improved the robot position error by controlling both powered wheel speeds. To move the robot among the crop rows, it generates the wheel speed difference command. The robotic platform effectively followed the rows of sugar beets at a velocity of 0.5 m/s, exhibiting an average lateral offset of 12 mm and a standard deviation of 22 mm.

Keywords

References

Åstrand, B., & Baerveldt, A.-J. (2002). An agricultural mobile robot with vision-based perception for mechanical weed control. Autonomous robots, 13, 21-35. https://doi.org/10.1023/A:1015674004201
Bai, Y., Zhang, B., Xu, N., Zhou, J., Shi, J., & Diao, Z. (2023). Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review. Computers and Electronics in Agriculture, 205, 107584. https://doi.org/10.1016/j.compag.2022.107584
Bak, T., & Jakobsen, H. (2004). Agricultural robotic platform with four wheel steering for weed detection. Biosystems Engineering, 87(2), 125-136. https://doi.org/10.1016/j.biosystemseng.2003.10.009
Bakker, T., Van Asselt, K., Bontsema, J., Müller, J., & van Straten, G. (2010). A path following algorithm for mobile robots. Autonomous robots, 29, 85-97. https://doi.org/10.1007/s10514-010-9182-3
Bakker, T., Wouters, H., Van Asselt, K., Bontsema, J., Tang, L., Müller, J., & van Straten, G. (2008). A vision based row detection system for sugar beet. Computers and Electronics in Agriculture, 60(1), 87-95. https://doi.org/10.1016/j.compag.2007.07.006
Bawden, O., Ball, D., Kulk, J., Perez, T., & Russell, R. (2014). A lightweight, modular robotic vehicle for the sustainable intensification of agriculture. Proceedings of the 16th Australasian Conference on Robotics and Automation 2014,
Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94-111. https://doi.org/10.1016/j.biosystemseng.2016.06.014
Bijay, R., Amarendra, M., & Asim, D. (2023). Steer guidance of autonomous agricultural robot based on pure pursuit algorithm and LiDAR based vector field histogram. Journal of Applied Science and Engineering, 26(10), 1363-1372.
Blasco, J., Aleixos, N., Roger, J., Rabatel, G., & Moltó, E. (2002). AE—Automation and emerging technologies: Robotic weed control using machine vision. Biosystems Engineering, 83(2), 149-157. https://doi.org/10.1006/bioe.2002.0109
Bonadies, S., & Gadsden, S. A. (2019). An overview of autonomous crop row navigation strategies for unmanned ground vehicles. Engineering in Agriculture, Environment and Food, 12(1), 24-31. https://doi.org/10.1016/j.eaef.2018.09.001
Chen, P., Ma, X., Wang, F., & Li, J. (2021). A new method for crop row detection using unmanned aerial vehicle images. Remote Sensing, 13(17), 3526. https://doi.org/10.3390/rs13173526
Choi, K. H., Han, S. K., Han, S. H., Park, K.-H., Kim, K.-S., & Kim, S. (2015). Morphology-based guidance line extraction for an autonomous weeding robot in paddy fields. Computers and Electronics in Agriculture, 113, 266-274. https://doi.org/10.1016/j.compag.2015.02.014
Dong, F., Heinemann, W., & Kasper, R. (2011). Development of a row guidance system for an autonomous robot for white asparagus harvesting. Computers and Electronics in Agriculture, 79(2), 216-225. https://doi.org/10.1016/j.compag.2011.10.002
Fontaine, V., & Crowe, T. (2006). Development of line-detection algorithms for local positioning in densely seeded crops. Canadian biosystems engineering, 48, 7.
García-Santillán, I., Peluffo-Ordoñez, D., Caranqui, V., Pusdá, M., Garrido, F., & Granda, P. (2018). Computer vision-based method for automatic detection of crop rows in potato fields. Proceedings of the International Conference on Information Technology & Systems (ICITS 2018), https://doi.org/10.1007/978-3-319-73450-7_34
Grimstad, L., & From, P. J. (2017). The Thorvald II agricultural robotic system. Robotics, 6(4), 24. https://doi.org/10.3390/robotics6040024
Hague, T., & Tillett, N. (2001). A bandpass filter-based approach to crop row location and tracking. Mechatronics, 11(1), 1-12. https://doi.org/10.1016/S0957-4158(00)00003-9
Jurik, T. W., & Zhang, S. (1999). Tractor wheel traffic effects on weed emergence in central Iowa. Weed Technology, 13(4), 741-746. https://doi.org/10.1017/S0890037X00042160
Kamandar, M. R., Massah, J., & Khoshnam, F. (2022). Measuring Some Mechanical Properties of Boxwood and Privet Plants by an Izod Impact Tester. Biomechanism and Bioenergy Research, 1(2), 7-12. https://doi.org/10.22103/BBR.2022.20351.1014
Kise, M., Zhang, Q., & Más, F. R. (2005). A stereovision-based crop row detection method for tractor-automated guidance. Biosystems Engineering, 90(4), 357-367. https://doi.org/10.1016/j.biosystemseng.2004.12.008
Marchant, J. A., & Brivot, R. (1995). Real-time tracking of plant rows using a Hough transform. Real-time imaging, 1(5), 363-371. https://doi.org/10.1006/rtim.1995.1036
Mueller-Sim, T., Jenkins, M., Abel, J., & Kantor, G. (2017). The Robotanist: A ground-based agricultural robot for high-throughput crop phenotyping. 2017 IEEE international conference on robotics and automation (ICRA), https://doi.org/10.1109/ICRA.2017.7989418
Rovira-Más, F., Zhang, Q., & Reid, J. F. (2008). Stereo vision three-dimensional terrain maps for precision agriculture. Computers and Electronics in Agriculture, 60(2), 133-143. https://doi.org/10.1016/j.compag.2007.07.007
Sainz-Costa, N., Ribeiro, A., Burgos-Artizzu, X. P., Guijarro, M., & Pajares, G. (2011). Mapping wide row crops with video sequences acquired from a tractor moving at treatment speed. Sensors, 11(7), 7095-7109. https://doi.org/10.3390/s110707095
Sarri, D., Lombardo, S., Lisci, R., De Pascale, V., & Vieri, M. (2020). AgroBot smash a robotic platform for the sustainable precision agriculture. Innovative Biosystems Engineering for Sustainable Agriculture, Forestry and Food Production: International Mid-Term Conference 2019 of the Italian Association of Agricultural Engineering (AIIA), https://doi.org/10.1007/978-3-030-39299-4_85
Shi, J., Bai, Y., Diao, Z., Zhou, J., Yao, X., & Zhang, B. (2023). Row detection BASED navigation and guidance for agricultural robots and autonomous vehicles in row-crop fields: methods and applications. Agronomy, 13(7), 1780. https://doi.org/10.3390/agronomy13071780
Søgaard, H. T., & Olsen, H. J. (2003). Determination of crop rows by image analysis without segmentation. Computers and Electronics in Agriculture, 38(2), 141-158. https://doi.org/10.1016/S0168-1699(02)00140-0
Tillett, N., Hague, T., & Miles, S. (2002). Inter-row vision guidance for mechanical weed control in sugar beet. Computers and Electronics in Agriculture, 33(3), 163-177. https://doi.org/10.1016/S0168-1699(02)00005-4
Van Evert, F. K., Van Der Heijden, G. W., Lotz, L. A., Polder, G., Lamaker, A., De Jong, A., . . . Van Der Zalm, T. (2006). A mobile field robot with vision-based detection of volunteer potato plants in a corn crop. Weed Technology, 20(4), 853-861. https://doi.org/10.1614/WT-05-132.1
Wu, X., Xu, M., & Wang, L. (2013). Differential speed steering control for four-wheel independent driving electric vehicle. 2013 IEEE International Symposium on Industrial Electronics, https://doi.org/10.1109/ISIE.2013.6563667
Zhang, W., Miao, Z., Li, N., He, C., & Sun, T. (2022). Review of current robotic approaches for precision weed management. Current robotics reports, 3(3), 139-151. https://doi.org/10.1007/s43154-022-00086-5
Biomechanism and Bioenergy Research
Volume 3, Issue 1
June 2024
Pages 14-25

Files

History

  • Receive Date: 23 October 2023
  • Revise Date: 21 May 2024
  • Accept Date: 30 May 2024

Share

How to cite

Statistics