Abstract
As the need for food is growing continuously, the intelligent agriculture replaces the traditional agriculture by deploying various agricultural machineries to perform the agricultural missions. However, the intelligent agriculture is not smart enough, so it requires massive of human interventions. These interventions are always based on the previous experience, leading to an imprecise control over the intelligent agriculture system, especially in mission planning and resource allocation. This paper proposes a precision farming system (PFS) and treats it as a multi-agent system (MAS) with an improved federal architecture. The machineries in this system are regarded as the agents with different functionalities. Regarding the mission planning in the PFS, it is actually a multi-objective optimization problem (MOP) and this paper defines the optimization objectives. An improved algorithm, GAPSO, is proposed to plan the established missions. During the mission planning process, each agent allocates the benefits through an auction. After receiving the bids from the agent candidates, the auction host runs the GAPSO to compute the Pareto optimal solution, which is the best strategy for mission planning and resource allocation. Then, the selected agents form an agent coalition according to the computed solution and execute the agricultural missions cooperatively. In order to verify the feasibility of proposed mission planning approach, a simulation is designed to execute an agricultural mission by several machineries. The simulation result illustrates that the mission is planned properly and the resource is allocated reasonably. The proper mission planning and resource allocation enables the intelligent agriculture to increase the productivity and decrease the cost. Meanwhile, the precise use of the pesticide decreases the harm to farming fields. The theoretical analysis and simulation is a good foundation for the further study. Once the mission planning approach applies to the real scenario, it is expected to bring high economic benefits.
