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关于无人机喷雾系统的研究与网格原子化滴滴相结合
Xiuyun Xue1,2,3,4, Yu Tian1, Zhenyu Yang1
1College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou, China.
Frontiers in plant science
|January 18, 2024
概括
优化无人机 (UAV) 在果园中喷可以改善农药沉积,减少漂流. 机器学习模型准确预测喷效应,提高作物保护效率.
科学领域:
- 农业工程 农业工程
- 精准农业 精准农业 精准农业
- 环境科学 环境科学
背景情况:
- 无人驾驶飞行器 (UAV) 提供了在中国山区果园中作物保护的潜力.
- 挑战包括农药滴滴漂移和有限的溶液容量,阻碍效率和增加使用.
- 优化无人机喷技术对于提高作物保护和减少环境影响至关重要.
研究的目的:
- 提出和评估一种新的植物保护无人机喷方法.
- 调查操作参数对滴滴沉积和漂移的影响.
- 开发一种机器学习模型,用于预测无人机喷性能.
主要方法:
- 在三种树类型 (传统,矮树,对冲) 上进行了无人机喷雾和电网冲击测试.
- 在不同的粒子大小和无人机高度下分析了滴滴沉积和漂移率.
- 使用机器学习 (BP,REGRESS,ELM,RBFNN) 来建模和预测喷雾效应.
主要成果:
- 矮树和杆树的滴水沉积量明显高于传统树木.
- 固定高度的网格改善了密集的树木和状树木的沉积,但减少了矮树的沉积.
- 采样点高度影响最大的沉积;风向距离影响最大的漂移.
- ELM模型实现了滴滴漂移率的最高预测准确度 (R2>0.95,最低的RMSE).
结论:
- 拟议的无人机喷方法,结合优化的操作参数,可以增强沉积和减少漂移.
- 机器学习,特别是ELM,提供了精确的喷雾效应预测,有助于技术优化.
- 根据不同的树木架构量身定制喷策略,对于最大限度地提高果园的效率至关重要.
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