Current Science

Open Access Open Access  Restricted Access Subscription Access

Adigar: a drone simulator for agriculture


Affiliations
1 University of Colombo School of Computing, UCSC Building Complex, 35, Reid Avenue, Colombo 7, 00700, Sri Lanka
2 Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, United Kingdom
 

Adigar is a drone simulator developed to reduce the adverse effects of pesticides during the spraying process. Here, we propose a path planning algorithm to cover all arable areas of a farmland, while avoiding unsafe areas. The proposed solution outputs the optimal path for the farmland and the drone can fly over along this path to spray pesticides without human intervention. This approach highlights the concept of using drones for agricultural purposes with minimum human intervention.

Keywords

Agriculture, autonomous drones, human intervention, reinforcement learning, pesticides.
User
Notifications
Font Size

  • Jayasekara, K. B., Dissanayake, D. M., Sivakanesan, R., Ranasinghe, A., Karunarathna, R. H. and Priyantha Kumara, G. W. G., Epidemiology of chronic kidney disease, with special emphasis on chronic kidney disease of uncertain etiology, in the north central region of Sri Lanka. J. Epidemiol., 2015, 25, 275–280.

  • Hill, N. R. et al., Global prevalence of chronic kidney disease – a systematic review and meta-analysis. PLoS ONE, 2016, 11, e0158765.

  • Forget, G., Goodman, T. and De Villiers, A. J., Impact of pesticide use on health in developing countries, International Development Research Centre, 1993.

  • Pesticide exposure of users and agricultural workers | Anses – Agence nationale de sécurité sanitaire de l’alimentation, de l’environnement et du travail; https://www.anses.fr/en/content/ pesticide-exposure-users-and-agricultural-workers (accessed on 25 April 2021).

  • Six ways drones are revolutionizing agriculture. MIT Technology Review; https://www.technologyreview.com/s/601935/six-ways-dronesare-revolutionizing-agriculture/ (accessed on 1 April 2018).

  • Amarasinghe, A., Wijesuriya, V. B., Ganepola, D. and Jayaratne, L., A swarm of crop spraying drones solution for optimising safe pesticide usage in arable lands: poster abstract. In Proceedings of the 17th Conference on Embedded Networked Sensor Systems Association for Computing Machinery, New York, USA, 2019, pp. 410–411.

  • Amarasinghe, A., Wijesuriya, V. B., Ganepola, D. and Jayaratne, L., Adigar, 2019.

  • Amarasinghe, A., Wijesuriya, V. B. and Jayaratne, L., A path planning algorithm for an autonomous drone against the overuse of pesticides. In IEEE Tenth International Conference on Information and Automation for Sustainability, Institute of Electrical and Electronics Engineers Inc, Negambo, Sri Lanka, 2021, pp. 446–451.

  • Drone Flight Simulators: Your Guide to the Top 8 Drone Simulators of 2020; https://uavoach.com/drone-flight-simulator/ (accessed on 11 January 2022).

  • González, V., Monje, C. A., Moreno, L. and Balaguer, C., UAVs mission planning with flight level constraint using fast marching square method. Robot Autonom. Syst., 2017, 94, 162–171.

  • Hasanbeig, M., Abate, A. and Kroening, D., Logically-constrained neural fitted Q-iteration. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS International Foundation for Autonomous Agents and Multiagent Systems, Montreal QC, Canada, 2019, pp. 2012–2014.

  • Sutton, R. S. and Barto, A. G., Reinforcement learning: an introduction, by Sutton. Trends Cogn. Sci., 1999, 3, 360.

  • DJI flight simulator – DJI; https://www.dji.com/simulator (accessed on 11 January 2022).

  • 9 Best drone flight simulators for 2022 (FPV and commercial) – Droneblog.com; https://www.droneblog.com/drone-flight-simulator/(accessed on 11 January 2022).

  • FPV Freerider by FPV Freerider.

  • Baier, C. and Katoen, J., Principles of Model Checking (Representation and Mind Series), The MIT Press, USA, 2008.

  • Gould, R. J., Advances on the Hamiltonian Problem – A Survey, 2002.


Abstract Views: 218

PDF Views: 88




  • Adigar: a drone simulator for agriculture

Abstract Views: 218  |  PDF Views: 88

Authors

Akarshani Amarasinghe
University of Colombo School of Computing, UCSC Building Complex, 35, Reid Avenue, Colombo 7, 00700, Sri Lanka
Lakshman Jayaratne
University of Colombo School of Computing, UCSC Building Complex, 35, Reid Avenue, Colombo 7, 00700, Sri Lanka
Viraj B. Wijesuriya
Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, United Kingdom

Abstract


Adigar is a drone simulator developed to reduce the adverse effects of pesticides during the spraying process. Here, we propose a path planning algorithm to cover all arable areas of a farmland, while avoiding unsafe areas. The proposed solution outputs the optimal path for the farmland and the drone can fly over along this path to spray pesticides without human intervention. This approach highlights the concept of using drones for agricultural purposes with minimum human intervention.

Keywords


Agriculture, autonomous drones, human intervention, reinforcement learning, pesticides.

References





DOI: https://doi.org/10.18520/cs%2Fv122%2Fi8%2F945-950