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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.

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