Zhang Pengyang, Liu Yangyang, Du Hongbin
College of Horticulture and Forestry, Tarim University, Alar, China.
Xinjiang Production and Construction Corps Key Laboratory of Facility Agriculture, Alar, China.
Front Plant Sci. 2024 Sep 26;15:1440234. doi: 10.3389/fpls.2024.1440234. eCollection 2024.
UAV-based plant protection represents an efficient, energy-saving agricultural technology with significant potential to enhance tea production. However, the complex terrain of hilly and mountainous tea fields, coupled with the limited endurance of UAVs, presents substantial challenges for efficient route planning. This study introduces a novel methodological framework for UAV-based precision plant protection across multiple tea fields, addressing the difficulties in planning the shortest routes and optimal flights for UAVs constrained by their endurance. The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. The method involves two primary steps: cluster partitioning and sortie allocation for multiple tea fields based on UAV range capabilities, followed by refining the UAV's flight path using a combination of hyperbolic genetic and simulated annealing algorithms with an adaptive temperature control mechanism. Simulation experiments and UAV route validation tests confirm the effectiveness of ACHAGA. The algorithm consistently identified optimal solutions within an average of 40 iterations, demonstrating robust global search capabilities and stability. It achieved an average reduction of 45.75 iterations and 1811.93 meters in the optimal route, with lower variation coefficients and extreme deviations across repeated simulations. ACHAGA significantly outperforms these algorithms, GA, GA-ACO, AFSA and BSO, which are also heuristic search strategies, in the multi-tea field route scheduling problem, reducing the optimal routes by 4904.82 m, 926.07 m, 3803.96 m and 800.11 m, respectively. Field tests revealed that ACHAGA reduced actual flight routes by 791.9 meters and 359.6 meters compared to manual and brainstorming-based planning methods, respectively. Additionally, the algorithm reduced flight scheduling distance and the number of turns by 11 compared to manual planning. This study provides a theoretical and technical foundation for managing large-scale tea plantations in challenging landscapes and serves as a reference for UAV precision operation planning in complex environments.
基于无人机的植保技术是一种高效节能的农业技术，在提高茶叶产量方面具有巨大潜力。然而，丘陵山地茶园地形复杂，加上无人机续航能力有限，给高效航线规划带来了巨大挑战。本研究针对多茶园基于无人机的精准植保，引入了一种新的方法框架，解决了受续航能力限制的无人机最短航线和最优飞行规划难题。该框架采用双曲遗传退火算法（ACHAGA）优化无人机植保航线，目标是最小化飞行距离、减少转弯次数并提高航线稳定性。该方法包括两个主要步骤：基于无人机航程能力对多个茶园进行聚类划分和架次分配，然后结合双曲遗传算法和模拟退火算法以及自适应温度控制机制对无人机飞行路径进行优化。仿真实验和无人机航线验证测试证实了ACHAGA的有效性。该算法平均在40次迭代内就能持续找到最优解，展现出强大的全局搜索能力和稳定性。它在最优航线上平均减少了45.75次迭代和1811.93米，在重复模拟中具有更低的变异系数和极端偏差。在多茶园航线调度问题上，ACHAGA明显优于同样是启发式搜索策略的遗传算法（GA）、遗传蚁群算法（GA - ACO）、人工鱼群算法（AFSA）和布谷鸟搜索算法（BSO），分别将最优航线缩短了4904.82米、926.07米、3803.96米和800.11米。田间测试表明，与人工规划和头脑风暴式规划方法相比，ACHAGA分别将实际飞行航线缩短了791.9米和359.6米。此外，与人工规划相比，该算法还将飞行调度距离和转弯次数分别减少了11。本研究为在具有挑战性的地形中管理大规模茶园提供了理论和技术基础，也为复杂环境下无人机精准作业规划提供了参考。
