摘要
为了探究植保无人机旋翼风场对雾滴在水稻植株上黏附量的影响规律,该研究以大疆T30植保无人机为施药平台,分别以清水、1%迈飞和0.5%迈图Target助剂溶液为喷洒溶液,基于航空风洞和粒子图像测速系统(Particle Image Velocimetry,PIV)测量了植保无人机旋翼风场作用下的雾流场、溶液的动态表面张力、黏度和密度以及雾滴在水稻叶片表面的动态接触角,分析了植保无人机旋翼风场对雾滴沉降速度的影响,以及飞防助剂对溶液性质参数、喷嘴雾化性能和雾滴在水稻叶片表面润湿铺展能力的影响规律。在此基础上,结合雾滴拦截模型和雾滴与作物叶片表面碰撞模型,建立了应用于植保无人机施药技术领域的雾滴黏附量预测模型,并对模型计算的准确率进行了田间验证试验。试验结果表明,助剂溶液对溶液性质、喷嘴雾化性能、雾滴在水稻叶片表面的润湿铺展能力以及雾滴在水稻植株上的黏附量方面均有不同程度的影响。与清水溶液相比,添加1%迈飞与0.5%迈图Target助剂溶液后,溶液表面张力分别降低了46.81%、62.21%;喷嘴雾化雾滴的粒径均呈增大趋势,约增大9.3%;雾滴在水稻叶片表面的接触角分别降低了27.74%、46.37%;雾滴在每公顷水稻植株上的黏附量分别增加了800.78%和1051.49%。无人机旋翼风场对雾滴沉降速度和雾滴在水稻植株上的黏附量均有明显影响,旋翼系统开启后,雾滴沉降速度明显增加,且更快达到稳定运动状态,当无人机旋翼转速由0增加至1000、1800 r/min时,雾滴沉降速度分别增加了366.67%、663.67%。与旋翼关闭状态相比,旋翼系统开启后,1%迈飞和0.5%迈图Target助剂溶液在水稻植株上的黏附量分别降低了26.78%和29.75%。本文建立的黏附量模型预测清水、1%迈飞和0.5%迈图Target 3种溶液在水稻植株上黏附量的准确率分别为48.59%、79.07%和79.29%。该研究为植保无人机对水稻进行施药作业时筛选助剂提供理论参考与指导,并提供旋翼风场作用下雾滴在水稻植株上黏附量的预测模型。
Spray technology of plant protection Unmanned Aviation Vehicle(UAV)is the highly efficient pesticide application in agricultural aviation,particularly for the zero growth of pesticides.The number,application area and scope of plant protection UAVs are ever increasing in China in the past 10 years.The application performance has also attracted much attention in recent years.Taking the DJI T30 plant protection UAV as the research object,this study aims to investigate the effect of rotor wind field of plant protection UAV and adjuvant on the droplet retention on the rice plant.The pure water,1%Maifei,and 0.5%Maitu Target adjuvant formulation were taken as the spray formulation.The aviation wind tunnel and Particle Image Velocimetry(PIV)were utilized to measure the spray flow field under the action of the rotor wind field of UAV.Some parameters were evaluated,including the dynamic surface tension,viscosity,and density in the formulations,as well as the dynamic contact angle of droplets on the surface of rice leaves.A quantitative analysis was made to clarify the effect of the rotor wind field on the droplets movement velocity and the retention of droplet on rice,the influence of adjuvant on formulation properties,the nozzle atomization performance,the droplet wetting and spreading performance on the rice leaves.A prediction model of droplet retention on the rice plant was established in the field of plant protection UAV spray technology,especially combining with the droplet interception and the droplet impact model.A field validation test was then conducted to verify the model.The results showed that the adjuvant formulation posed a significant effect on the droplet size,the formulation properties,the nozzle atomization performance,the wetting and spreading performance of the droplets on the surface of rice leaves,as well as the retention of the droplets on the rice plants.The surface tension values of 1%Maifei and 0.5%Maitu Target adjuvant formulations were reduced by 46.81%,and 62.21%,respectively,compared with water;The static contact angle of the droplets on the rice leaves was reduced by 27.74%,and 46.37%,respectively;The retention on the per hectare rice increased by 800.78%and 1051.49%,respectively.The droplet size increased by at about 9.3%.There was a significant effect of UAV rotor wind field on the droplet movement velocity and the droplet retention on the rice.The droplet movement velocity increased significantly,after the rotor system was turned on and more quickly reached the stable velocity.When the UAV rotor speed increased from 0 to 1000 and 1800 r/min,the droplet movement velocity increased by 366.67%,and 663.67%in turn.Compared with the droplet retention on the rice when the rotor system was turn off,the droplet retention of 1%Maifei and 0.5%Maitu Target adjuvant formulation decreased by 26.78%,and 29.75%,respectively,after the rotor system turn on.The accuracies of the retention model were 48.59%,79.07%,and 79.29%,respectively,in order to predict the retention of the three solutions on the rice plants.
作者
张海艳
兰玉彬
文晟
陈春玲
许童羽
陈盛德
Zhang Haiyan;Lan Yubin;Wen Sheng;Chen Chunling;Xu Tongyu;Chen Shengde(College of Information and Electrical Engineering,Shenyang Agricultural University,Shenyang 110866,China;College of Electronic Engineering(College of Artificial Intelligence),South China Agricultural University,Guangzhou 510642,China;National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology,Guangzhou 510642,China;College of Engineering,South China Agricultural University,Guangzhou 510642,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第18期40-50,共11页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(31901411)
国家自然科学基金面上项目(32271985)
广东省引进领军人才项目(2016LJ06G689)
广东省自然科学基金项目(2022A1515011008)
辽宁省科技厅重点研发项目(2019JH2/10200002)。