Intelligent inspection of agricultural assets and design of UAV autonomous path planning system for rural revitalization

In this paper, an intelligent inspection of agricultural assets and autonomous path planning system of UAV for rural revitalization are proposed, aiming at solving the problems of low efficiency, high cost and poor adaptability to dynamic environment in traditional agricultural asset management. The system adopts hierarchical architecture, including cloud decision-making layer, edge computing layer and UAV execution layer. Three-dimensional environmental information of farmland is obtained by multi-sensor fusion technology, and multi-objective path planning is carried out by using improved adaptive genetic algorithm (AGA), which comprehensively considers the optimization of task time, energy consumption and coverage. The experimental results show that AGA is significantly superior to traditional A* algorithm and standard GA (SGA) in terms of average task time, energy consumption, coverage and path re-planning, showing its superior performance in complex dynamic agricultural environment. In addition, the system also constructs an edge-cloud collaborative reasoning mechanism, which combines the dual-mode transmission of 4G/ Beidou and the communication architecture of LoRa ad hoc network to ensure the stability of data transmission and the real-time performance of task execution, and provides an efficient and intelligent solution for the development of smart agriculture under the rural revitalization strategy.