空地协同的广域蔬菜种植传感数据采集研究

Research on air-ground collaborative vegetable planting sensor data collection for wide-area

关键词:  精准农业  蔬菜种植  智慧农业  传感网络  无人机

Key words:  precision agriculture  vegetable cultivation  smart agriculture  sensor networks  drones

摘要:

【目的】 针对大规模蔬菜种植区域传感器部署成本高昂、数据远距离传输困难等问题，设计高效的数据收集策略。【方法】 文章结合蔬菜种植区域的分布特征，采用动态K均值聚类算法对区域进行分簇，通过贪心算法基于最近插入原则生成初始的卡车与无人机协同路径。之后利用基于进化的人工蜂群算法对路径进行优化，结合动态Metropolis准则、动态禁忌搜索和精英选择策略进一步提升解的质量。【结果】 该方法显著减少了数据收集的总时间，同时大幅降低了空地采集系统的能耗。【结论】 该文提出的方法为广域蔬菜种植区域的智能化数据收集提供了一种高效的解决方案，为实现精准农业的广泛应用提供了技术支撑。

Abstract:

［Purpose］ To promote the development of vegetable farming towards intelligence and precision，this paper proposes a truck-drone collaborative wide-area sensor data collection method. An efficient data collection strategy is designed to address the high cost of sensor deployment and the challenges of long-distance data transmission in large-scale vegetable farming areas.［Method］ Based on the distribution characteristics of vegetable farming areas，a dynamic K-means clustering algorithm was used to partition the area into clusters. An initial truck-drone collaborative path was then generated using a greedy algorithm based on the nearest insertion principle. Subsequently，the path was optimized using an evolutionary artificial bee colony algorithm that incorporated the dynamic metropolis criterion，dynamic tabu search，and elite selection strategies to further improve solution quality.［Result］ Experimental results showed that the proposed method effectively reduced the total data collection time and significantly lowered the energy consumption of the aerial-ground collection system.［Conclusion］ The proposed method provides an efficient solution for intelligent data collection in wide-area vegetable farming and lays a solid foundation for the widespread application of precision agriculture.