数字农科院2.0

Control effect on cotton aphids of insecticides sprayed with unmanned aerial vehicles under different flight heights and spray volumes

文献类型： 外文期刊

作者： Hongyan Hu; Xiangliang Ren; Xiaoyan Ma; Huanhuan Li; Yajie Ma;Dan Wang; Xianpeng Song; Yanhua Meng; Yan Ma

关键词： unmanned aerial vehicle; flight parameter; spray volume; droplet deposition; cotton aphids

期刊名称： International Journal of Precision Agricultural Aviation

ISSN：

年卷期： 2021 年

页码：

摘要： Unmanned aerial vehicles (UAVs) are an emerging technology increasingly used to control plant diseases and pests in China. However, the efficacy of UAV spraying in cotton fields remains unclear. This paper assesses the droplet deposition of UAV spraying and analyzes how flight heights and spray volumes affect aphids control in cotton fields. Allura red was used as a tracer and Kromekote cards were used to collect the droplets. The research results demonstrated that droplet uniformity, droplet coverage, and droplet density were all higher at a flight height of 1.5 m and a spray volume of 22.5 L/ha. Control efficacy on the first day after spraying was correlated with the number of droplets deposited on the underside of leaves, and higher droplet coverage and density resulted in better pest control. Optimal control efficacy on the seventh day after spraying was achieved at the flight height of 1.0 m and 1.5 m, with control efficacy rates ranging from 57.93% to 80.53%. Field trials of five different insecticides verified the efficacy of UAV spraying controls against cotton aphids. These results provide a theoretical reference and technical support for subsequent UAV spraying to control for diseases and pests.

分类号：

  • 相关文献

[1]Photoregime Affects Development, Reproduction, and Flight Performance of the Invasive Fall Armyworm (Lepidoptera: Noctuidae) in China. He Limei,Ge Shishuai,Zhang Haowen,He Wei,Yan Ran,Wu Kongming. 2021

[2]Adult nutrition affects reproduction and flight performance of the invasive fall armyworm, Spodoptera frugiperda in China. . 2020

[3]Application Of Multi-Rotor Unmanned Aerial V.ehicle Application In Management O f Stem Borer (Lepidoptera) In Sugarcane. Li, Yang:Rui,Wei, Chun:Yan,Li, De:Wei,Wei, Jin:Ju,Zhang, Xiao:Qiu,Liang, Yong:Jian,Song, Xiu:Peng,Qin, Zhen:Qiang. 2019

[4]Polymer Additives Regulate The Deposition Behavior Of Pesticide Droplets On Target Plants. Song, YY, Huang, GZ, Zheng, L, Huang, QL, Cao, LD, Li, FM, Zhao, PY, Zhang, L, Cao, C. 2021

[5]Effects Of Spray Parameters Of Drone On The Droplet Deposition In Sugarcane Canopy. Zhang, XQ, Song, XP, Liang, YJ, Qin, ZQ, Zhang, BQ, Wei, JJ, Li, YR, Wu, JM. 2020

[6]Numerical Simulation And Analysis On S.pray Drift Movement Of M ultirotor Plant Protection Unmanned Aerial Vehicle. Yang, Fengbo,Sun, Zhu,Zhou, Qingqing,Cai, Chen,Xue, Xinyu,Yang, Fengbo. 2018

[7]Droplet deposition and control effect of insecticides sprayed with an unmanned aerial vehicle against plant hoppers. Qin, Wei-Cai,Qiu, Bai-Jing,Qin, Wei-Cai,Xue, Xin-Yu,Chen, Chen,Xu, Zhu-Feng,Zhou, Qing-Qing.

[8]Comparison Of Spray Deposition, Control E.fficacy On Wheat Aphids A nd Working Efficiency In The Wheat Field Of The Unmanned Aerial Vehicle With Boom Sprayer And Two Conventional Knapsack Sprayers. Lan, Yubin,Wang, Guobin,Yuan, Huizhu,Ouyang, Fan,Chen, Pengchao,Han, Yuxing,Qi, Haixia. 2019

[9]Estimation Of Land Surface Temperature F.rom Unmanned Aerial Vehicle L oaded Thermal Imager Data. Si, M, Tang, BH, Li, ZL. 2018

[10]Estimating maize seedling number with UAV RGB images and advanced image processing methods. Liu, Shuaibing,Yin, Dameng,Feng, Haikuan,Li, Zhenhai,Xu, Xiaobin,Shi, Lei,Jin, Xiuliang. 2022

[11]Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments. Cheng, Qian,Xu, Honggang,Fei, Shuaipeng,Li, Zongpeng,Chen, Zhen. 2022

[12]Comparison of Different Dimensional Spectral Indices for Estimating Nitrogen Content of Potato Plants over Multiple Growth Periods. Fan Y.,Feng H.,Yue J.,Liu Y.,Jin X.,Xu X.,Song X.,Ma Y.,Yang G.. 2023

[13]Detecting spatial variability of soil compaction using soil apparent electrical conductivity and maize traits. Ren, Lidong,D'Hose, Tommy,Borra-Serrano, Irene,Lootens, Peter,Hanssens, Daan,De Smedt, Philippe,Cornelis, Wim M.,Ruysschaert, Greet. 2022

[14]Faba Bean (Vicia faba L.) Yield Estimation Based on Dual-Sensor Data. Cui, Yuxing,Ji, Yishan,Liu, Rong,Li, Weiyu,Liu, Yujiao,Liu, Zehao,Zong, Xuxiao,Yang, Tao. 2023

[15]Above-Ground Biomass Estimation And Yield Prediction In Potato By Using Uav-Based Rgb And Hyperspectral Imaging. Li, B, Xu, XM, Zhang, L, Han, JW, Bian, CS, Li, GC, Liu, JG, Jin, LP. 2020

[16]Deep convolutional neural networks for estimating maize above-ground biomass using multi-source UAV images: a comparison with traditional machine learning algorithms. Yu, Danyang,Zha, Yuanyuan,Sun, Zhigang,Li, Jing,Jin, Xiuliang,Zhu, Wanxue,Bian, Jiang,Ma, Li,Zeng, Yijian,Su, Zhongbo. 2022

[17]Dynamic Harvest Index Estimation of Winter Wheat Based on UAV Hyperspectral Remote Sensing Considering Crop Aboveground Biomass Change and the Grain Filling Process. Jianqiang Ren,Ningdan Zhang,Xingren Liu,Shangrong Wu,Dandan Li. 2022

[18]UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Fei, Shuaipeng,Hassan, Muhammad Adeel,Xiao, Yonggui,Su, Xin,Chen, Zhen,Cheng, Qian,Duan, Fuyi,Chen, Riqiang,Ma, Yuntao. 2022

[19]Exploring Multisource Feature Fusion and Stacking Ensemble Learning for Accurate Estimation of Maize Chlorophyll Content Using Unmanned Aerial Vehicle Remote Sensing. Weiguang Zhai,Changchun Li,Qian Cheng,Fan Ding,Zhen Chen. 2023

[20]Automatic counting of rapeseed inflorescences using deep learning method and UAV RGB imagery. Li J.,Li Y.,Qiao J.,Li L.,Wang X.,Yao J.,Liao G.. 2023

作者其他论文 更多>>