Agricultural Drone Zoning and Deployment Strategy with Multiple Flights Considering Takeoff Point Reach Distance Minimization
Keywords:
Drone, Routing, Rule, Spraying, Zone Control
Abstract
In the agricultural sector, drones are used to spray
chemicals for the plants. A lawn mowing movement
pattern is one of the widely used methods when
deploying the drones because of its simplicity. A route
planner determines some pre-set routes before making
the drones to fly based on them. Each drone flight is
limited by its battery level or level of spray liquids. To
efficiently complete the spraying task, multiple drones
need to be deployed simultaneously. In this study, we
study a multiple drone zoning and deployment strategy
that minimizes the cost to set up equipment at the
takeoff points, e.g., between flights. We propose a
method to set the flight starting points and directions
appropriately, given various target areas to cover. This
is the first study that discusses the spraying drone
zoning and deployment plan while minimizing the
number of takeoff points, which plays an important role
in reducing the drone set up and deployment costs. The
suggested procedure helps drone route planners to
generate good routes within a short time. The generated
routes could be used by the planner for their chemical
spraying activity and could be used as initial input for
their design, which can be improved with the planners’
experience. Our study shows that when generating an
efficient route, we must consider the number of flight
area levels, directions of the drone movements, the
number of U-turns of the drones, and the start points of
the drone flights
References
Ahmed, S., Qiu, B., Ahmad, F., Kong, C.-W., & Xin, H. (2021). A State-of-the-Art
Analysis of Obstacle Avoidance Methods from the Perspective of an
Agricultural Sprayer UAV’s Operation Scenario. Agronomy, 11, 1069.
Amarat, S. B., & Zong, P. (2019). 3D Path Planning, Routing Algorithms and Routing
Protocols for Unmanned Air Vehicles: A Review. Aircraft Engineering and
Aerospace Technology, 91(9), 1245–1255.
Arnold, F., & Sörensen, K. (2019). What Makes a VRP Solution Good? The Generation
of Problem-Specific Knowledge for Heuristics. Computers & Operations Research,
106, 280–288.
Avellar, G. S. C., Pereira, G. A. S., Pimenta, L. C. A., & Iscold, P. (2015). Multi-UAV
Routing for Area Coverage and Remote Sensing with Minimum Time. Sensors,
15, 27783–27803.
Barna, R., Solymosi, K., & Stettner, E. (2019). Mathematical Analysis of Drone Flight
Path. Journal of Agricultural Informatics, 10(2), 15–27.
Barrientos, A., Colorado, J., del Cerro, J., Martinez, A., Rossi, C., Sanz, D., & Valente,
J. (2011). Aerial Remote Sensing in Agriculture: A Practical Approach to Area
Coverage and Path Planning for Fleetsof Mini Aerial Robots. Journal of Field
Robotics, 28(5), 667–689.
Cabreira, T. M., Brisolara, L. B., & Paulo R. Jr., F. (2019). Survey on Coverage Path
Planning with Unmanned Aerial Vehicles. Drones, 3(1), 4.
Chakrabortty, R. K., Rahman, H. F., & Ryan, M. J. (2020). Efficient Priority Rules for
Project Scheduling Under Dynamic Environments: A Heuristic Approach.
Computers & Industrial Engineering, 140, 106287.
Chung, S. H., Sah, B., & Lee, J. (2020). Optimization for Drone and Drone-Truck
Combined Operations: A Review of The State of the Art and Future Directions.
Computers and Operations Research, 123, 105004.
Coutinho, W. P., Battara, M., & Fliege, J. (2018). The Unmanned Aerial Vehicle Routing
and Trajectory Optimisation Problem, A Taxonomic Review. Computers &
Industrial Engineering, 120, 116–128.
Ðurasević, M., & Jakobović, D. (2020). Comparison of Schedule Generation Schemes
for Designing Dispatching Rules with Genetic Programming in the Unrelated
Machines Environment. Applied Soft Computing Journal, 96, 106637.
Erdelić, T., & Carić, T. (2019). A Survey on the Electric Vehicle Routing Problem:
Variants and Solution Approaches. Journal of Advanced Transportation, 5075671.
Faiçal, B. S., Freitas, H., Gomes, P. H., Mano, L. Y., Pessin, G., de Carvalho, A. C. P. L.
F., Krishnamachari, B., & Ueyama, J. (2017). An Adaptive Approach for UAVbased Pesticide Spraying in Dynamic Environments. Computers and Electronics
in Agriculture, 138, 210–223.
Fernández-Caramés, T. M., Blanco-Novoa, O., Froiz-Míguez, I., & Fraga-Lamas, P.
(2019). Towards an Autonomous Industry 4.0 Warehouse: A UAV and
Blockchain-Based System for Inventory and Traceability Applications in Big
Data-Driven Supply Chain Management. Sensors, 19(10), 2394.
Gil-Gala, F. J., Mencía, C., Sierra, M. R., & Varela, R. (2019). Evolving Priority Rules
for On-Line Scheduling of Jobs on a Single Machine with Variable Capacity
Over Time. Applied Soft Computing Journal, 85, 105782.
Gu, Q., Fan, T., Pan, F., & Zhang, C. (2020). A Vehicle-UAV Operation Scheme for
Instant Delivery. Computers & Industrial Engineering, 149, 106809.
Hobby Hangar. (2022). ALIGN RM41501XW M4 High-Performance Agricultural
Drone. https://www.hobbyhangar.co.nz/align-rm41501xw-m4-highperformance-agricultural-drone.
Jorge, H. G., de Santos, L. M. G., Álvarez, N. F., Sánchez, J. M., & Medina, F. N. (2021).
Operational Study of Drone Spraying Application for the Disinfection of
Surfaces against the COVID-19 Pandemic. Drones, 5(1), 18.
Kakamoukas, G. A., Sarigiannidis, P. G., & Economides, A. A. (2022). FANETs in
Agriculture - A Routing Protocol Survey. Internet of Things, 18, 100183.
Kim, S. J., & Lim, G. J. (2018). A Hybrid Battery Charging Approach for Drone-Aided
Border Surveillance Scheduling. Drones, 2(4), 38.
Ma, F., Xu, Z., & Xiong, F. (2019). Research on Route Planning of Plant Protection UAV
Based on Area Modular Division. 2019 11th International Conference on Intelligent
Human-Machine Systems and Cybernetics (IHMSC) (p. 101–104).
Macrina, G., Pugliese, L. D. P., Guerriero, F., & Laporte, G. (2020). Drone-aided
Routing: A Literature Review. Transportation Research Part C, 120, 102762.
Maddikunta, P. K. R., Hakak, S., Alazab, M., Bhattacharya, S., Gadekallu, T. R., Khan,
W. Z., & Pham, Q.-V. (2021). Unmanned Aerial Vehicles in Smart Agriculture:
Applications, Requirements, and Challenges. IEEE Sensors Journal, 21(16),
17608–17619.
Mehrabi, Z., McDowell, M. J., Ricciardi, V., Levers, C., Martinez, J. D., Mehrabi, N.,
Wittman, H., Ramankutty, N., & Jarvis, A. (2021). The global divide in datadriven farming. Nature Sustainability, 4, 154–160.
Moadab, A., Farajzadeh, F., & Valilai, O. F. (2022). Drone Routing Problem Model for
Last-Mile Delivery using The Public Transportation Capacity as Moving
Charging Stations. Scientific Reports, 12, 6361.
Moghdani, R., Salimifard, K., Demir, E., & Benyettou, A. (2021). The Green Vehicle
Routing Problem: A Systematic Literature Review. Journal of Cleaner Production,
279, 123691.
Moon, S.-W., & Shim, H.-C. (2009). Study on Path Planning Algorithms for Unmanned
Agricultural Helicopters in Complex Environment. International Journal of
Aeronautical & Space Sciences, 10(2), 1–11.
Nguyen, V. N., Jenssen, R., & Roverso, D. (2018). Automatic Autonomous VisionBased Power Line Inspection: A Review of Current Status and the Potential
Role of Deep Learning. International Journal of Electrical Power & Energy Systems,
99, 107–120.
Otto, A., Agatz, N., Campbell, J., Golden, B., & Pesch, E. (2018). Optimization
Approaches for Civil Applications of Unmanned Aerial Vehicles (UAVs) or
Aerial Drones: A Survey. Networks, 72(4), 411–458.
Patrik, A., Utama, G., Gunawan, A. A. S., Chowanda, A., Suroso, J. S., Shofiyanto, R.,
& Budiharto, W. (2019). GNSS-based Navigation Systems of Autonomous
Drone for Delivering Items. Journal of Big Data, 6, 53.
Pergher, I., Frej, E. A., Roselli, L. R. P., & de Almeida, A. T. (2020). Integrating
Simulation and FITradeoff Method for Scheduling Rules Selection in Job-Shop
Production Systems. International Journal of Production Economics, 227, 107669.
Pulver, A., & Wei, R. (2018). Optimizing the Spatial Location of Medical Drones.
Applied Geography, 90, 9–16.
Qin, Y., Kishk, M. A., & Alouini, M.-S. (2021). On the Influence of Charging Stations
Spatial Distribution on Aerial Wireless Networks. IEEE Transactions on Green
Communications and Networking, 5(3), 1395–1409.
Santangeli, A., Chen, Y., Kluen, E., Chirumamilla, R., Tiainen, J., & Loehr, J. (2020).
Integrating Drone‑Borne Thermal Imaging with Artifcial Intelligence to Locate
Bird Nests on Agricultural Land. Scientific Reports, 10, 10993.
Singgih, I. K., Lee, J., & Kim, B.-I. (2020). Node and Edge Drone Surveillance Problem
with Consideration of Required Observation Quality and Battery Replacement.
IEEE Access, 8, 44125–44139.
Sordan, J. E., Oprime, P., Pimenta, M. L., Chiabert, P., & Lombardi, F. (2022). Industry
4.0: A Bibliometric Analysis in the Perspective of Operations Management.
Operations and Supply Chain Management, 15(1), 93–104.
Su, J., Yi, D., Su, B., Mi, Z., Liu, C., Hu, X., Xu, X., Guo, L., & Chen, W.-H. (2021). Aerial
Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust
Monitoring. IEEE Transactions on Industrial Informatics, 17(3), 2242–2249.
Tu, Y.-H., Phinn, S., Johansen, K., Robson, A., & Wu, D. (2020). Optimising Drone
Flight Planning for Measuring Horticultural Tree Crop Structure. ISPRS Journal
of Photogrammetry and Remote Sensing, 160, 83–96.
Wang, D., Hu, P., Du, J., Zhou, P., Deng, T., & Hu, M. (2019). Routing and Scheduling
for Hybrid Truck-Drone Collaborative Parcel Delivery with Independent and
Truck-Carried Drones. IEEE Internet of Things Journal, 6(6), 10483–10495.
Zhao, J., Mao, M., Zhao, X., & Zou, J. (2021). A Hybrid of Deep Reinforcement
Learning and Local Search for the Vehicle Routing Problems. IEEE Transactions
on Intelligent Transportation Systems, 22(11), 7208–7218
Analysis of Obstacle Avoidance Methods from the Perspective of an
Agricultural Sprayer UAV’s Operation Scenario. Agronomy, 11, 1069.
Amarat, S. B., & Zong, P. (2019). 3D Path Planning, Routing Algorithms and Routing
Protocols for Unmanned Air Vehicles: A Review. Aircraft Engineering and
Aerospace Technology, 91(9), 1245–1255.
Arnold, F., & Sörensen, K. (2019). What Makes a VRP Solution Good? The Generation
of Problem-Specific Knowledge for Heuristics. Computers & Operations Research,
106, 280–288.
Avellar, G. S. C., Pereira, G. A. S., Pimenta, L. C. A., & Iscold, P. (2015). Multi-UAV
Routing for Area Coverage and Remote Sensing with Minimum Time. Sensors,
15, 27783–27803.
Barna, R., Solymosi, K., & Stettner, E. (2019). Mathematical Analysis of Drone Flight
Path. Journal of Agricultural Informatics, 10(2), 15–27.
Barrientos, A., Colorado, J., del Cerro, J., Martinez, A., Rossi, C., Sanz, D., & Valente,
J. (2011). Aerial Remote Sensing in Agriculture: A Practical Approach to Area
Coverage and Path Planning for Fleetsof Mini Aerial Robots. Journal of Field
Robotics, 28(5), 667–689.
Cabreira, T. M., Brisolara, L. B., & Paulo R. Jr., F. (2019). Survey on Coverage Path
Planning with Unmanned Aerial Vehicles. Drones, 3(1), 4.
Chakrabortty, R. K., Rahman, H. F., & Ryan, M. J. (2020). Efficient Priority Rules for
Project Scheduling Under Dynamic Environments: A Heuristic Approach.
Computers & Industrial Engineering, 140, 106287.
Chung, S. H., Sah, B., & Lee, J. (2020). Optimization for Drone and Drone-Truck
Combined Operations: A Review of The State of the Art and Future Directions.
Computers and Operations Research, 123, 105004.
Coutinho, W. P., Battara, M., & Fliege, J. (2018). The Unmanned Aerial Vehicle Routing
and Trajectory Optimisation Problem, A Taxonomic Review. Computers &
Industrial Engineering, 120, 116–128.
Ðurasević, M., & Jakobović, D. (2020). Comparison of Schedule Generation Schemes
for Designing Dispatching Rules with Genetic Programming in the Unrelated
Machines Environment. Applied Soft Computing Journal, 96, 106637.
Erdelić, T., & Carić, T. (2019). A Survey on the Electric Vehicle Routing Problem:
Variants and Solution Approaches. Journal of Advanced Transportation, 5075671.
Faiçal, B. S., Freitas, H., Gomes, P. H., Mano, L. Y., Pessin, G., de Carvalho, A. C. P. L.
F., Krishnamachari, B., & Ueyama, J. (2017). An Adaptive Approach for UAVbased Pesticide Spraying in Dynamic Environments. Computers and Electronics
in Agriculture, 138, 210–223.
Fernández-Caramés, T. M., Blanco-Novoa, O., Froiz-Míguez, I., & Fraga-Lamas, P.
(2019). Towards an Autonomous Industry 4.0 Warehouse: A UAV and
Blockchain-Based System for Inventory and Traceability Applications in Big
Data-Driven Supply Chain Management. Sensors, 19(10), 2394.
Gil-Gala, F. J., Mencía, C., Sierra, M. R., & Varela, R. (2019). Evolving Priority Rules
for On-Line Scheduling of Jobs on a Single Machine with Variable Capacity
Over Time. Applied Soft Computing Journal, 85, 105782.
Gu, Q., Fan, T., Pan, F., & Zhang, C. (2020). A Vehicle-UAV Operation Scheme for
Instant Delivery. Computers & Industrial Engineering, 149, 106809.
Hobby Hangar. (2022). ALIGN RM41501XW M4 High-Performance Agricultural
Drone. https://www.hobbyhangar.co.nz/align-rm41501xw-m4-highperformance-agricultural-drone.
Jorge, H. G., de Santos, L. M. G., Álvarez, N. F., Sánchez, J. M., & Medina, F. N. (2021).
Operational Study of Drone Spraying Application for the Disinfection of
Surfaces against the COVID-19 Pandemic. Drones, 5(1), 18.
Kakamoukas, G. A., Sarigiannidis, P. G., & Economides, A. A. (2022). FANETs in
Agriculture - A Routing Protocol Survey. Internet of Things, 18, 100183.
Kim, S. J., & Lim, G. J. (2018). A Hybrid Battery Charging Approach for Drone-Aided
Border Surveillance Scheduling. Drones, 2(4), 38.
Ma, F., Xu, Z., & Xiong, F. (2019). Research on Route Planning of Plant Protection UAV
Based on Area Modular Division. 2019 11th International Conference on Intelligent
Human-Machine Systems and Cybernetics (IHMSC) (p. 101–104).
Macrina, G., Pugliese, L. D. P., Guerriero, F., & Laporte, G. (2020). Drone-aided
Routing: A Literature Review. Transportation Research Part C, 120, 102762.
Maddikunta, P. K. R., Hakak, S., Alazab, M., Bhattacharya, S., Gadekallu, T. R., Khan,
W. Z., & Pham, Q.-V. (2021). Unmanned Aerial Vehicles in Smart Agriculture:
Applications, Requirements, and Challenges. IEEE Sensors Journal, 21(16),
17608–17619.
Mehrabi, Z., McDowell, M. J., Ricciardi, V., Levers, C., Martinez, J. D., Mehrabi, N.,
Wittman, H., Ramankutty, N., & Jarvis, A. (2021). The global divide in datadriven farming. Nature Sustainability, 4, 154–160.
Moadab, A., Farajzadeh, F., & Valilai, O. F. (2022). Drone Routing Problem Model for
Last-Mile Delivery using The Public Transportation Capacity as Moving
Charging Stations. Scientific Reports, 12, 6361.
Moghdani, R., Salimifard, K., Demir, E., & Benyettou, A. (2021). The Green Vehicle
Routing Problem: A Systematic Literature Review. Journal of Cleaner Production,
279, 123691.
Moon, S.-W., & Shim, H.-C. (2009). Study on Path Planning Algorithms for Unmanned
Agricultural Helicopters in Complex Environment. International Journal of
Aeronautical & Space Sciences, 10(2), 1–11.
Nguyen, V. N., Jenssen, R., & Roverso, D. (2018). Automatic Autonomous VisionBased Power Line Inspection: A Review of Current Status and the Potential
Role of Deep Learning. International Journal of Electrical Power & Energy Systems,
99, 107–120.
Otto, A., Agatz, N., Campbell, J., Golden, B., & Pesch, E. (2018). Optimization
Approaches for Civil Applications of Unmanned Aerial Vehicles (UAVs) or
Aerial Drones: A Survey. Networks, 72(4), 411–458.
Patrik, A., Utama, G., Gunawan, A. A. S., Chowanda, A., Suroso, J. S., Shofiyanto, R.,
& Budiharto, W. (2019). GNSS-based Navigation Systems of Autonomous
Drone for Delivering Items. Journal of Big Data, 6, 53.
Pergher, I., Frej, E. A., Roselli, L. R. P., & de Almeida, A. T. (2020). Integrating
Simulation and FITradeoff Method for Scheduling Rules Selection in Job-Shop
Production Systems. International Journal of Production Economics, 227, 107669.
Pulver, A., & Wei, R. (2018). Optimizing the Spatial Location of Medical Drones.
Applied Geography, 90, 9–16.
Qin, Y., Kishk, M. A., & Alouini, M.-S. (2021). On the Influence of Charging Stations
Spatial Distribution on Aerial Wireless Networks. IEEE Transactions on Green
Communications and Networking, 5(3), 1395–1409.
Santangeli, A., Chen, Y., Kluen, E., Chirumamilla, R., Tiainen, J., & Loehr, J. (2020).
Integrating Drone‑Borne Thermal Imaging with Artifcial Intelligence to Locate
Bird Nests on Agricultural Land. Scientific Reports, 10, 10993.
Singgih, I. K., Lee, J., & Kim, B.-I. (2020). Node and Edge Drone Surveillance Problem
with Consideration of Required Observation Quality and Battery Replacement.
IEEE Access, 8, 44125–44139.
Sordan, J. E., Oprime, P., Pimenta, M. L., Chiabert, P., & Lombardi, F. (2022). Industry
4.0: A Bibliometric Analysis in the Perspective of Operations Management.
Operations and Supply Chain Management, 15(1), 93–104.
Su, J., Yi, D., Su, B., Mi, Z., Liu, C., Hu, X., Xu, X., Guo, L., & Chen, W.-H. (2021). Aerial
Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust
Monitoring. IEEE Transactions on Industrial Informatics, 17(3), 2242–2249.
Tu, Y.-H., Phinn, S., Johansen, K., Robson, A., & Wu, D. (2020). Optimising Drone
Flight Planning for Measuring Horticultural Tree Crop Structure. ISPRS Journal
of Photogrammetry and Remote Sensing, 160, 83–96.
Wang, D., Hu, P., Du, J., Zhou, P., Deng, T., & Hu, M. (2019). Routing and Scheduling
for Hybrid Truck-Drone Collaborative Parcel Delivery with Independent and
Truck-Carried Drones. IEEE Internet of Things Journal, 6(6), 10483–10495.
Zhao, J., Mao, M., Zhao, X., & Zou, J. (2021). A Hybrid of Deep Reinforcement
Learning and Local Search for the Vehicle Routing Problems. IEEE Transactions
on Intelligent Transportation Systems, 22(11), 7208–7218
Published
2021-12-26
How to Cite
[1]
I. Singgih, “Agricultural Drone Zoning and Deployment Strategy with Multiple Flights Considering Takeoff Point Reach Distance Minimization”, INJIISCOM, vol. 2, no. 2, pp. 66-79, Dec. 2021.
