Using Simulation Modeling for Efficient Drone Application in Agriculture

Using Simulation Modeling for Efficient Drone Application in Agriculture


BlueKei Solutions is an Indian consulting company that transforms businesses for technology adoption and integration, through a methodological and systematic approach. They applied system engineering in order to deploy drones for their client in the agricultural sector.


Scenarios for drone applications are various: terrain mapping, rescue missions, safety missions, rations and medical supplies, as well as agriculture. The use of drones within the agricultural industry is wide and includes:

The customer of BlueKei Solutions was a service provider. This company wanted to know how many drones they needed to deploy for spraying a field.

The drone support system included drone carriers, operators, technicians, and charging infrastructure.

The drone application contractor required more drones than the manufacturer could provide. So, it was necessary to optimize drone operation.


BlueKei Solutions used agent-based simulation in AnyLogic for their needs.

The model architecture

The model architecture (click to enlarge)

For the correct decisions to be made, the following questions were considered: How many batteries were needed? How many drone carriers were required? What type of drone was appropriate?

Decisions to be made with AnyLogic

Decisions to be made with AnyLogic (click to enlarge)

The engineers modeled the environment, the field, drone behavior, and its control center with AnyLogic software to test different scenarios using GIS map, statecharts, and schedules.

The model of the environment

The model of the environment

17 fields were simulated by the model developers. They modeled agents, drone operators’ shifts, and how many times the batteries should be changed.

The statechart of the drone behavior

The statechart of the drone behavior

Engineers were tracking a lot of parameters and inputs: charging and discharging rates for a drone, average flying speed, battery life, and swapping time.


As a result of the simulation provided by BlueKei Solutions with AnyLogic, several insights have been identified.

On the X axis there were the number of drones. On the Y axis there were the simulation time in days (blue) and the number of swapped batteries (orange). It shows that after four drones were deployed, additional drones didn't provide further substantial effectiveness. The number of times the batteries were swapped was constant. Energy consumption was similar. However, the time it took was best around 3 days if you look at the costs to the number of days.

The simulation results

The simulation results

The number of days for completion of spraying fields varied depending on the number of drones. Also, the number of times that the battery in the drones should be changed, varied depending on which fields the drones operated.

The outputs of the simulation modeling showed that there was no need to invest in the number of planned drones because deploying more drones didn’t help. By using AnyLogic simulation in planning, the client could reduce the costs while not wasting money on excessive drone use.

To optimize results the following parameters must be considered for the planning of drone deployment:

These factors would directly impact on the overall performance.

The simulation modeling was successfully applied in the agricultural industry, specifically for spraying a field, and could also be used for weed detection, crop health monitoring, and geofence planning.

Watch the video about the case study presented by BlueKei Solutions at the AnyLogic Conference 2021:

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