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随着农业智能监测对高质量遥感数据需求的提升，无人机路径规划已成为保障深度学习模型输入数据质量的关键环节。为此，提出一种改进苔藓生长优化算法(Improved Moss Growth Optimization Algorithm, IMGO),以优化农业场景下的无人机路径规划问题。该算法通过强化学习动态调整步长参数及复生机制触发，融合种群划分、计算风向、自适应边界策略，优化无人机路径，避开局部最优解。算法迭代优化生成高效平稳飞行路径，确保高质量农田图像采集以满足深度学习需求。仿真实验表明，相较传统算法，IMGO显著减少飞行时间与能耗，提升图像采集的覆盖率与质量，为农作物生长评估、病虫害监测、土壤肥力分析等农业指标的精确预测提供了数据支撑。该算法适用于复杂农田环境下的无人机路径规划，对推动农业智能化监测技术的实际应用具有重要意义。
Abstract：With the growing demand for high-quality remote sensing data in agricultural intelligent monitoring, UAV path planning has become a critical component in ensuring the input data quality for deep learning models. To address this, an Improved Moss Growth Optimization Algorithm(IMGO) is proposed to enhance UAV path planning in agricultural scenarios. Using reinforcement learning, the algorithm dynamically adjusts step size and triggering of the regeneration mechanism. By integrating strategies such as population division, wind direction calculation, and adaptive boundary handling, it optimizes UAV flight paths and avoids local optima. Through iterative optimization, the algorithm generates efficient, smooth flight paths, ensuring high-quality farmland images for deep learning. Simulations show that the IMGO significantly reduces flight time and energy consumption while enhancing image coverage and quality. This supports accurate agricultural predictions, such as crop growth, pest monitoring, and soil fertility analysis. The algorithm is effective for UAV path planning in complex farmland environments, advancing agricultural monitoring.
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基本信息:
中图分类号:S127;TP18
引用信息:
[1]高俊杰,代永强.基于改进苔藓生长优化算法的无人机路径规划[J].无线电工程,2025,55(08):1695-1702.
基金信息:
甘肃省自然科学基金(25JRRA355)~~
2025-06-11
2025-06-11
2025-06-11