Design, development, and performance evaluation of a GPS and machine vision-based navigation system for an intelligent field guard robot

Document Type : Research Paper

Authors

1 Department of Agrotechnology, College of Aburaihan, University of Tehran, Tehran, I. R. Iran

2 Department of Biosystems Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, I. R. Iran

10.22099/iar.2026.53745.1698

Abstract

An innovative approach to protecting farmland from wild animals is the use of an intelligent field‑guard robot. In this study, a fully featured field‑guard robot was designed and developed using image processing and a global positioning system (GPS). To achieve this, the robot’s tracking system, which combines a machine‑vision camera and a GPS receiver, was evaluated. For automatic tracking, several control points were established within an experimental field, and their latitude and longitude coordinates were provided to the robot to enable GPS‑based navigation. As the robot moved through the field, it captured and processed images to detect and pursue moving objects, such as approaching or attacking animals. The performance of the tracking system, the image‑processing algorithm, and the robot’s ability to detect and chase animals were investigated. The results showed that the robot’s tracking system performed better in sunny weather compared to cloudy and partly cloudy conditions. To evaluate the image‑processing algorithm, RGB, HSV, and Lab color models were examined, and the RGB color model was found to be the most suitable. A normalized standard deviation of 10% in the image provided the best performance for detecting the attacking animals. Evaluation of the robot’s performance in repelling attacking animals showed promising results, with successful repulsion of four out of five attacking animals under sunny and partly cloudy weather conditions.

Graphical Abstract

Design, development, and performance evaluation of a GPS and machine vision-based navigation system for an intelligent field guard robot

Keywords

Main Subjects


Adeodu, A. O., Bodunde, O. P., Daniyan, I. A., Omitola, O. O., Akinyoola, J. O., & Adie, U. C. (2019). Development of an autonomous mobile plant irrigation robot for a semi-structured environment. Procedia Manufacturing, 35, 9-15. https://doi.org/10.1016/j.promfg.2019.05.004
Asefpour Vakilian, K., & Massah, J. (2017). A farmer-assistant robot for nitrogen fertilizing management of greenhouse crops. Computers and Electronics in Agriculture, 139, 153-163. http://dx.doi.org/10.1016/j.compag.2017.05.012
Al-Muhammed, M. J., and Abu Zitar, R. (2018). Probability-directed random search algorithm for an unconstrained optimization problem. Applied Soft Computing. 71, 165-182. https://doi.org/10.1016/j.asoc.2018.06.043
Aureli, M., Fiorilli, F., & Porfiri, M. (2012). Portraits of self-organization in fish schools interacting with robots. Physica D: Nonlinear Phenomena, 241, 908-920.
Bapat, V., Kale, P., Shinde, V., Deshpande, N., & Shaligram, A. (2017). WSN application for crop protection to divert animal intrusions in the agricultural land. Computers and Electronics in Agriculture, 133, 88-96. https://doi.org/10.1016/j.compag.2016.12.007
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
Chen, B., Zi, B., Qin, L., & Pan, Q. (2020). State-of-the-art research in robotic hip exoskeletons: A general review. Journal of Orthopaedic Translation, 20, 4-13. https://doi.org/10.1016/j.jot.2019.09.006
Chrzanowski, A., Detko, P., & Stefanski, T. P. (2019). Intelligent autonomous robot supporting small pets in domestic environment. IFAC-PapersOnLine, 52, 194-199. https://doi.org/10.1016/j.ifacol.2019.08.070
Craven, S. R., and Hygnstrom, S. E. (1994). Deer. The Handbook: Prevention and Control of Wildlife Damage, University of Wisconsin: Extension publication G3083.
Gianelli, S., Harland, B., & Fellous, J. M. (2018). A new rat-compatible robotic framework for spatial navigation behavioral experiments. Journal of Neuroscience Methods, 294, 40-50. https://doi.org/10.1016/j.jneumeth.2017.10.021
Grahn, S., Gopinath, V., Wang, X. V., & Johansen, K. (2018). Exploring a model for production system design to utilize large robots in human-robot collaborative assembly cells. Procedia Manufacturing, 25, 612-619. https://doi.org/10.1016/j.promfg.2018.06.094
Gribovskiy, A., Halloy, J., Deneubourg, J. L., & Mondada, F. (2018). Designing a socially integrated mobile robot for ethological research. Robotics and Autonomous Systems, 103, 42-55. https://doi.org/10.1016/j.robot.2018.02.003
Grift, T. E. (2007). Robotics in crop production. Encyclopedia of Agricultural, Food, and Biological Engineering. Retrieved from: researchgate.net
Guerra, R. D. S., Aonuma, H., Hosoda, K., Asada, M. (2010). Semi-automatic behavior analysis using robot/insect mixed society and video tracking. Journal of Neuroscience Methods, 191, 138-144.
Hashemi, A., Asefpour Vakilian, K., Khazaei, J., & Massah, J. (2014). An artificial neural network modeling for force control system of a robotic pruning machine. Journal of Information and Organizational Sciences, 38, 35-41. https://doi.org/10.31341/jios.2014.38.006
Hildreth, A. M., Hygnstrom, S. E., Blankenship, E. E., & Vercauteren, K. C. (2012). Use of partially fenced fields to reduce deer damage to corn. Wildlife Society Bulletin. 36, 199-203.
Howard Jr, V.W. (1994). Kangaroo Rats. The Handbook: Prevention and control of wildlife damage, cooperative extension division. Institute of Agriculture and Natural Resources University of Nebraska – Lincoln
Ishii, H., Shi, Q., Miyaagishima, S., Fumino, S., Konno, S., Okabayashi, S., Iida, N., Kimura, H., Tahara, Y., Shibata, S., & Takanishi, A. (2012). Stress exposure with a small mobile robot, both in the immature and mature periods, induces mental disorder in rats. The Fourth IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, 24-27 June, Roma, Italy.
Kouzehgar, M., Tamilselvam, Y. K., Heredia, M. V., & Elara, M. R. (2019). Self-reconfigurable façade-cleaning robot equipped with deep-learningbased crack detection based on convolutional neural networks. Automation in Construction, 108, 102959. https://doi.org/10.1016/j.autcon.2019.102959
Kulich, M., Miranda-Bront, J. J., & Preucil, L. (2017). A meta-heuristic-based goal-selection strategy for mobile robot search in an unknown environment. Computers and Operations Research, 84, 178-187. https://doi.org/10.1016/j.cor.2016.04.029
Malavazi, F. B. P., Guyonneau, R., Fasquel, J. B., Lagrange, S., & Mercier, F. (2018). LiDAR-only based navigation algorithm for an autonomous agricultural robot. Computers and Electronics in Agriculture 154, 71-79. https://doi.org/10.1016/j.compag.2018.08.034
Massah, J., Asefpour Vakilian, K., Shabanian, M., & Shariatmadari, S. M. (2021). Design, development, and performance evaluation of a robot for kiwifruit yield estimation. Computers and Electronics in Agriculture, 185,1-10. https://doi.org/10.1016/j.compag.2021.106132
Massah, J., & Ghazavi, M. R. (2009). Effect of force control system on power and time consumption of tree pruning machine. Cercetari Agronomice in Moldova (Romania), 42, 1-7.
Meinecke, L., Soofi, M., Riechers, M., Khorozyan, I., Hosseini, H., Schwarze, S., & Waltert, M. (2018). Crop variety and prey richness affect spatial patterns of human-wildlife conflicts in Iran's Hyrcanian forests. Journal for Nature Conservation, 43, 165-172.
Nabi, D. G., Rashid Tak, Sh., Kangoo, K. A., & Halwai, M. A. (2009). Increasing incidence of injuries and fatalities inflicted by wild animals in Kashmir. Injury, 40, 87-89.
Patle, B. K., Babu L, G., Pandey, A., Parhi, D. R. K., & Jagadeesh, A. (2019). A review: On path planning strategies for navigation of mobile robot. Defence Technology, 15, 582-606. https://doi.org/10.1016/j.dt.2019.04.011
Ren, Q., Xu, J., & Li, X. (2015). A data-driven motion control approach for a robotic fish. Journal of Bionic Engineering, 12, 382-394. https://doi.org/10.1016/S1672-6529(14)60130-X
Sudhakara, P., Ganapathy, V., Priyadharshini, B., & Sundaran, K. (2018). Obstacle avoidance and navigation planning of a wheeled mobile robot using amended artificial potential field method. Procedia Computer Science, 133, 998-1004. https://doi.org/10.1016/j.procs.2018.07.076
Vallvé, J., & Cetto, J. A. (2015). Potential information fields for mobile robot exploration. Robotics and Autonomous Systems, 69, 68-79. http://dx.doi.org/10.1016/j.robot.2014.08.009
Vaughan, R., Sumpter, N., Henderson, J., Frost, A., & Cameron, S. (2000). Experiments in automatic flock control. Robotics and Autonomous Systems, 31, 109-117.
Vercauteren, K. C., Lavelle, M. J., & Hygnstrom, S. (2006). Fences and deer-damage management: A review of designs and efficacy. Wildlife Society Bulletin, 34, 191-200.
Vidović, I., & Scitovski, R. (2014). Center-based clustering for line detection and application to crop rows detection. Computers and Electronics in Agriculture, 109, 212-220. https://doi.org/10.1016/j.compag.2014.10.014
Vijayan, S., & Pati, B. P. (2002). Impact of changing cropping patterns on man-animal conflicts in the gir protected area, with specific reference to the talala sub-district, Gujarat, India. Population and Environment, 23, 541-559.
Vroegindeweij, B. A., IJsselmuiden, J., & Van Henten, E. J. (2016). Probabilistic localisation in repetitive environments: Estimating a robot’s position in an aviary poultry house. Computers and Electronics in Agriculture, 124, 303-317. https://doi.org/10.1016/j.compag.2016.04.019
Wu, Y., Ta, X., Xiao, R., Wei, Y., An, D., & Li, D. (2019). Survey of underwater robot positioning navigation. Applied Ocean Research, 90, 101845. https://doi.org/10.1016/j.apor.2019.06.002
Xue, J., Zhang, L., & Grift, T. E. (2012). Variable field-of-view machine vision-based row guidance of an agricultural robot. Computers and Electronics in Agriculture, 84, 85-91.
Yu, J., Wang, K., Tan, M., & Zhang, J. (2014). Design and Control of an Embedded Vision Guided Robotic Fish with Multiple Control Surfaces, Hindawi Publishing Corporation. The Scientific World Journal, 13631296. https://doi.org/10.1155/2014/631296
Yuan, J., Wu, Z., Yu, J., Zhou, C., & Tan, M. (2017). Design and 3D motion modeling of a 300-m gliding robotic dolphin. IFAC-PapersOnLine, 50, 12685-12690. https://doi.org/10.1016/j.ifacol.2017.08.2251
Deepika, R., Shalini, P., Sona Saran, S., Sruthi, S., Suvarnamala, T. & Poongothai, M. (2024). Agro guard edge AI - development of sustainable IoT framework for wildlife intrusion detection. Journal of Electrical Engineering and Automation, 6(4), 325-342. https://doi.org/10.36548/jeea.2024.4.005
Reddy, B. S. V., Deepika, G., Rajendra, M., & Gorijala, L. S. (2023). Animal intrusion detection in fields using convnets-2D with cloud service AWS SES alerts. International Journal of Scientific Development and Research, 8(4), 785-788. https://ijsdr.org/papers/IJSDR2304137.pdf
Thenmozhi, A., Abdullah, A., Saqlain Musthaq, S., Shynu, T., & Mohamed Sameer Ali, M. (2025). IoT-enabled precision animal trespass identification and deterrence system for smart agriculture. American Journal of Science on Integration and Human Development, 3(1), 1-12.