| 植保无人机喷施雾滴沉积特性的荧光示踪分析 |
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| 引用本文： | 张瑞瑞,李龙龙,文瑶,陈立平,唐青,伊铜川,宋佳星.植保无人机喷施雾滴沉积特性的荧光示踪分析[J].农业工程学报,2020,36(6):47-55. |
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| 作者姓名： | 张瑞瑞 李龙龙 文瑶 陈立平 唐青 伊铜川 宋佳星 |
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| 作者单位： | 北京农业智能装备技术研究中心,北京100097;北京农业智能装备技术研究中心,北京100097;北京农业智能装备技术研究中心,北京100097;北京农业智能装备技术研究中心,北京100097;北京农业智能装备技术研究中心,北京100097;北京农业智能装备技术研究中心,北京100097;北京农业智能装备技术研究中心,北京100097 |
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| 基金项目： | 北京市科技新星计划项目（Z181100006218029）；国家自然科学基金项目（31771674）；北京市农林科学院青年科研基金项目（QNJJ202009）；北京市农林科学院2018创新能力建设专项（KJCX20180424） |
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| 摘 要： | 航空施药雾滴沉积特性的准确检测对施药参数选择和施药质量优化至关重要。该研究以3WQF-80-10型植保无人机为试验对象,选取典型飞行工况,通过水敏纸和基于荧光示踪的航空施药沉积检测系统同步获取展向雾滴沉积覆盖率,研究对比了该机型在典型飞行工况条件下的雾滴沉积离散性和连续性分布,评估基于荧光示踪的沉积检测系统对雾滴沉积检测效果与适用性。试验结果表明:荧光示踪法与水敏纸法所得雾滴沉积率分布曲线整体趋于一致,2种方法的检测结果相关性拟合优度(R2)为0.88~0.96;由于植保无人机旋翼下洗风场胁迫细小雾滴沉降至非水敏纸布样位置,致使荧光示踪法测得的平均雾滴覆盖率曲线出现更多峰值,覆盖率值高于水敏纸法测量结果。植保无人机飞行速度为2 m/s,飞行高度为3 m的作业条件下,与水敏纸离散布样方式相比,荧光示踪连续布样方式测得雾滴覆盖率提高16.92%,当飞行速度为4 m/s,飞行高度为9 m时,后者较前者提高97.77%。植保无人机作业工况对施药雾滴沉积覆盖率影响方面,飞行速度2 m/s,飞行高度3 m的工况下,雾滴沉积覆盖率最高,为8.34%和7.14%;随着植保无人机飞行高度和速度增加,雾滴沉积覆盖率降低。针对植保无人机下洗风场作用下施药雾滴沉积质量检测,与离散布样方式相比,连续性样品采集可获取更丰富的雾滴空间沉积分布细节。该研究可为无人机低空低量施药雾滴沉积检测和无人机下洗风场对雾滴沉积分布影响研究提供方法参考。
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| 关 键 词： | 无人机 农药 雾滴沉积分布 光谱分析 荧光示踪剂 |
| 收稿时间： | 2019/7/28 0:00:00 |
| 修稿时间： | 2020/2/7 0:00:00 |
Fluorescence tracer method for analysis of droplet deposition patterncharacteristics of the sprays applied via unmanned aerial vehicle |
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| Zhang Ruirui,Li Longlong,Wen Yao,Chen Liping,Tang Qing,Yi Tongchuan and Song Jiaxing.Fluorescence tracer method for analysis of droplet deposition patterncharacteristics of the sprays applied via unmanned aerial vehicle[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(6):47-55. |
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| Authors: | Zhang Ruirui Li Longlong Wen Yao Chen Liping Tang Qing Yi Tongchuan and Song Jiaxing |
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| Institution: | 1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 2. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 3. National Center for International Research on Agricultural Aerial Application Technology, Beijing 100097, China; 4. Beijing Engineering Laboratory for Beidou Navigation of Agricultural Machinery and Intelligent Measurement and Control, Beijing 100097, China,1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 2. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 3. National Center for International Research on Agricultural Aerial Application Technology, Beijing 100097, China; 4. Beijing Engineering Laboratory for Beidou Navigation of Agricultural Machinery and Intelligent Measurement and Control, Beijing 100097, China,1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 2. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 3. National Center for International Research on Agricultural Aerial Application Technology, Beijing 100097, China; 4. Beijing Engineering Laboratory for Beidou Navigation of Agricultural Machinery and Intelligent Measurement and Control, Beijing 100097, China,1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 2. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 3. National Center for International Research on Agricultural Aerial Application Technology, Beijing 100097, China; 4. Beijing Engineering Laboratory for Beidou Navigation of Agricultural Machinery and Intelligent Measurement and Control, Beijing 100097, China,1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 2. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 3. National Center for International Research on Agricultural Aerial Application Technology, Beijing 100097, China; 4. Beijing Engineering Laboratory for Beidou Navigation of Agricultural Machinery and Intelligent Measurement and Control, Beijing 100097, China,1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 2. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 3. National Center for International Research on Agricultural Aerial Application Technology, Beijing 100097, China; 4. Beijing Engineering Laboratory for Beidou Navigation of Agricultural Machinery and Intelligent Measurement and Control, Beijing 100097, China and 1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 2. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; 3. National Center for International Research on Agricultural Aerial Application Technology, Beijing 100097, China; 4. Beijing Engineering Laboratory for Beidou Navigation of Agricultural Machinery and Intelligent Measurement and Control, Beijing 100097, China |
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| Abstract: | With the development of agricultural aviation technologies and their application in agricultural production, plant protection unmanned aerial vehicle(UAV) has been widely used to control pests and diseases of crops. The high speed rotation of the rotor in the UAV produces a powerful downwash affecting the distribution of pesticide droplets on the ground.Understanding spatial distribution of these droplets on the ground is important to evaluate application quality of the pesticides and plays an important role in improving the spray system in UAV and optimizing its operating parameters. Current methods for measuring the droplet deposition distributions use a number of collectors placed regularly on the ground to receive the droplets and measured their sizes;it is difficult for them to effectively obtain the deposition of all droplets resultdue to the downwash of UAV. This paper presents a new method to resolve this problem by improving accuracy and spatial continuity of pesticide droplets measurement applied by an unmanned helicopter. The flying parameters of a 3 WQF-80-10 unmanned helicopter used to spray pesticides were obtained from the high-precision Beidou navigation system, and the RQT-C-3 fluorescent whitening tracer with mass fraction of 1.0% was used as the proxy for the pesticides. Two droplet collection methods: one used continuous strip paper and the other one used individual water sensitive paper, were used to measure the droplets deposition distribution. We divided the experimental field into three areas, with Areas 1 and 2 spaced 3 m apart, and Areas 2 and 3 spaced 1 m apart. A metal bracket 8 m log and 0.5 m away from the ground was placed in each area. Prior to the experiment, a paper tape was fixed on the surface of the bracket and the water-sensitive paper cards were placed evenly in the area 0.5 m away from the paper tape. There were one paper tape and 15 water sensitive papers in each area, and a total of six spray tests were performed based on pro-designed flight parameters. The combinations of flight speed and flight height were:2 m/s and 3 m, 2 m/s and 6 m, 2 m/s and 9 m, 3 m/s and 3 m, 3 m/s and 6 m, and 4 m/s and 9 m. The paper tape was detected by fluorescence spectroscopy analysis, and the water sensitive papers were scanned using an image processing software to obtain droplet deposition coverage rate. The results showed that distribution curves of the coverage rate obtained by the paper tape method coupled with the fluorescence spectrum tracer were consistent with that obtained from the images of the water sensitive paper method, with the R2 being 0.88~0.96. Because not all fine droplets fell on the water sensitive papers due to the effect of the high speed rotating rotor, the coverage rate curve measured by the continuous fluorescence method had multiple peaks and the value of its coverage rate was higher than that measured from the water sensitive paper method. When the unmanned helicopter flew at speed of 2 m/s and height of 3 m, the coverage ratio obtained from the continuous fluorescence method was up 16.92% compared to that sampled from the individual water-sensitive paper method, while when the flight speed was 4 m/s at height of 9 m, the coverage ratio in the latter was 97.77% higher than in the former. In terms of the impacts of unmanned helicopter operating conditions on coverage rate, when the helicopter flew at 2 m/s and height of 3 m, the coverage rate of the droplets obtained from the two methods were the highest, being 8.34% for the continuous fluorescence method and 7.14% for the individual paper method. With the flight height and speed increasing, the spatial coverage rate of the droplets decreased. In summary, the high-speed rotor of UAV generates a downwash, making the droplets of pesticides move in different directions and resulting in a large spatial difference in their deposition on the ground. Therefore, the continuous sampling method is more adequate to evaluate the spatial distribution of the droplets. This study has implication for study on detecting deposition of pesticides and other agrochemicals applied by UAV. |
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| Keywords: | unmanned aerial vehicle pesticide droplet deposition distribution spectral analysis fluorescent tracer |
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