基于室内飞行模拟器中散射分布分析的变化系数,对UASS的控制逻辑进行建模
Adhitya Saiful Hanif1, Xiongzhe Han1,2, Seung-Hwa Yu3
1College of Agricultural and Life Sciences, Interdisciplinary Program in Smart Agriculture, Kangwon National University, Chuncheon, Republic of Korea.
Frontiers in plant science
|September 6, 2023
概括
这项研究开发了一种机器学习模型,以优化无人空中喷雾系统 (UASS),以减少在风暴条件下减少农药漂移. 随机森林回归模型准确预测了喷变化,使得流速可以更好地控制.
科学领域:
- 农业工程 农业工程
- 精准农业 精准农业 精准农业
- 机器学习应用 机器学习应用
背景情况:
- 无人机空中喷雾系统 (UASS) 提供一种有效的作物处理解决方案,可适应各种条件.
- 现有的UASS正在与大量农药漂移作斗争,特别是在风的环境中,这限制了它们的运营效率和环境安全.
研究的目的:
- 开发一个预测模型,优化UASS喷参数,以最大限度地减少农药漂移.
- 通过基于环境和系统变量来管理流量,增强UASS的操作控制.
主要方法:
- 在室内飞行模拟器中模拟喷雾,不同的参数如飞行速度,高度和风力状况.
- 采用机器学习算法,包括随机森林回归,梯度增强和线性回归,以预测喷变化系数 (CV).
- 使用CV作为数据集来开发控制开口和流量率的模型.
主要成果:
- 随机森林回归模型的准确性很高,R2为0.96%,RMSE为0.04%.
- 开发的模型成功模拟了不同开口配置 (A和AB) 的喷操作.
结论:
- 经过验证的机器学习模型提供了一种可靠的方法,可以根据CV和农药数量控制UASS开口.
- 这种方法使UASS能够在各种环境条件下更有效地运行,减少漂移并提高应用准确性.
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