Influence of flight pattern on the effectiveness of unmanned aerial vehicles application in a mountain Nanguo pear orchard

飞行模式对无人机在南果山梨园应用效果的影响

阅读:2

Abstract

INTRODUCTION: The application of unmanned aerial vehicles (UAVs) in orchards has been gradually emerging. Due to the complex architecture of tree canopies and the planting environment, choosing a reasonable UAV flight pattern to effectively enhance droplet deposition on critical target areas remains a challenge. METHODS: This study employed Nanguo pear trees as the application target, with an electric multi-rotor UAV, the EA-30X, chosen as the spraying platform. Through comprehensive droplet assessment methodologies, five different flight patterns (intra-row, intra-row-high-speed, intra-row-half-rate, inter-row, verti-row) were analyzed and compared to assess droplet deposition in the tree canopy. RESULTS: Measurements revealed that 71.85% of the droplet coverage is in the 0-5% range and the droplet density is in the 0-200 drops·cm(-)² range. The results also showed that there was no statistically significant difference in droplet deposition between the inner and outer zones of the fruit tree canopy in the horizontal direction among the treatments (p > 0.05). DISCUSSION: The results indicate that, under the conditions of constant spray volume rate (60 L/ha) and flight height (2.5 m), particularly when natural wind speeds are excessive, using a UAV for two-pass spraying patterns (intra-row-high-speed, intra-row-half-rate) is not recommended. Intra-row, inter-row and verti-row are viable options, but the selection should be made flexibly based on operational requirements. Different flight patterns lead to changes in the droplet deposition distribution trends across vertical layers and between inner and outer zones. This study provides scientific and precise operational guidance and reference for pest and disease control in Nanguo pear orchards.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。