Assessing the Severity of Verticillium Wilt in Cotton Fields and Constructing Pesticide Application Prescription Maps Using Unmanned Aerial Vehicle (UAV) Multispectral Images
文献类型： 外文期刊
作者： Li, Xiaojuan 1 ; Liang, Zhi 1 ; Yang, Guang 1 ; Lin, Tao 2 ; Liu, Bo 1 ;
作者机构： 1.Xinjiang Univ, Sch Mech Engn, Urumqi 830017, Peoples R China
2.Xinjiang Acad Agr Sci, Inst Cash Crops, Urumqi 830091, Peoples R China
关键词： cotton Verticillium wilt; unmanned aerial vehicle (UAV) remote sensing; monitoring model; precision spraying; prescription map
期刊名称：DRONES （ 影响因子：4.8； 五年影响因子：5.5 ）
ISSN：
年卷期： 2024 年 8 卷 5 期
页码：
收录情况： SCI
摘要： Cotton Verticillium wilt is a common fungal disease during the growth of cotton, leading to the yellowing of leaves, stem dryness, and root rot, severely affecting the yield and quality of cotton. Current monitoring methods for Verticillium wilt mainly rely on manual inspection and field investigation, which are inefficient and costly, and the methods of applying pesticides in cotton fields are singular, with issues of low pesticide efficiency and uneven application. This study aims to combine UAV remote sensing monitoring of cotton Verticillium wilt with the precision spraying characteristics of agricultural drones, to provide a methodological reference for monitoring and precision application of pesticides for cotton diseases. Taking the cotton fields of Shihezi City, Xinjiang as the research subject, high-resolution multispectral images were collected using drones. Simultaneously, 150 sets of field samples with varying degrees of Verticillium wilt were collected through ground data collection, utilizing data analysis methods such as partial least squares regression (PLSR) and neural network models; additionally, a cotton Verticillium wilt monitoring model based on drone remote sensing images was constructed. The results showed that the estimation accuracy R2 of the PLSR and BP neural network models based on EVI, RENDVI, SAVI, MSAVI, and RDVI vegetation indices were 0.778 and 0.817, respectively, with RMSE of 0.126 and 0.117, respectively. Based on this, an analysis of the condition of the areas to be treated was performed, combining the operational parameters of agricultural drones, resulting in a prescription map for spraying against cotton Verticillium wilt.
- 相关文献
作者其他论文 更多>>
-
Optimizing biochar application for enhanced cotton and sugar beet production in Xinjiang: a comprehensive study
作者：Li, Yi;Wang, Shibin;Xie, Lulu;Li, Yi;Qi, Xingyun;Wang, Xiaofang;Liang, Jiaping;Zhang, Fucang;Wang, Chunli;Li, Yi;Siddique, Kadambot H. M.;Siddique, Kadambot H. M.;Luo, Honghai;Hou, Zhenan;Yang, Guang;Xie, Xiangwen;Liu, De Li;Liu, De Li;Liu, De Li
关键词：cotton; sugar beet; production; biochar management; evaluation
-
Optimizing biochar application rates for improved soil chemical environments in cotton and sugarbeet fields under trickle irrigation with plastic mulch
作者：Qi, Xingyun;Li, Yi;Shi, Penghui;Wang, Shibin;Wang, Xiaofang;Liang, Jiaping;Wu, Shufang;Yang, Guang;Hou, Zhenan;Sun, Benhua;Tian, Xiaohong;Siddique, Kadambot H. M.;Siddique, Kadambot H. M.;Feng, Hao;Yu, Qiang;Xie, Xiangwen
关键词：Saline-alkali land; Soil chemical environment; Biochar; Cotton; Sugarbeet
-
Integration of Unmanned Aerial Vehicle Spectral and Textural Features for Accurate Above-Ground Biomass Estimation in Cotton
作者：Chen, Maoguang;Yin, Caixia;Liu, Haijun;Wang, Zhenyang;Jiang, Pingan;Tang, Qiuxiang;Lin, Tao;Lin, Tao;Ali, Saif;Jin, Xiuliang
关键词：unmanned aerial vehicle (UAV); cotton; above-ground-biomass (AGB); spectral features; textural features
-
Estimating leaf age of maize seedlings using UAV-based RGB and multispectral images
作者：Bai, Yi;Liu, Shuaibing;Nie, Chenwei;Shao, Mingchao;Yu, Xun;Cheng, Minghan;Liu, Yadong;Jin, Xiuliang;Bai, Yi;Shi, Liangsheng;Zha, Yuanyuan;Jin, Xiuliang;Bai, Yi;Liu, Shuaibing;Nie, Chenwei;Xu, Honggen;Yang, Hongye;Shao, Mingchao;Yu, Xun;Cheng, Minghan;Liu, Yadong;Jin, Xiuliang;Lin, Tao;Cui, Ningbo;Wu, Wenbin;Wu, Wenbin;Jin, Xiuliang
关键词：Maize; Leaf age; UAV; Machine learning model; RGB and Multispectral image
-
LncRNAs exert indispensable roles in orchestrating the interaction among diverse noncoding RNAs and enrich the regulatory network of plant growth and its adaptive environmental stress response
作者：Zhang, Lingling;Zhu, Guoning;Zhu, Hongliang;Lin, Tao;Wu, Bin;Zhang, Chunjiao
关键词：
-
YOLO-C: An Efficient and Robust Detection Algorithm for Mature Long Staple Cotton Targets with High-Resolution RGB Images
作者：Liang, Zhi;Cui, Gaojian;Xiong, Mingming;Li, Xiaojuan;Jin, Xiuliang;Lin, Tao
关键词：long-staple cotton; boll detection; deformable convolution; SENet; WIoU
-
Effect of Irrigation Water Amount and Planting Pattern on Water Use and Yield of Cotton in Northern Xinjiang, China
作者：Li, Jie;Ma, Tengfei;Lin, Tao;He, Hong;Maimaiti, Paerhati;Maimaiti, Tuerxunjiang;Zhang, Pengzhong;Lou, Shanwei;Wang, Chunwu;Aimaiti, Tuoheti
关键词：Cotton; Evapotranspiration (ET); Evaporation; Yield; Water use efficiency (WUE)
