Assessing the Severity of Verticillium Wilt in Cotton Fields and Constructing Pesticide Application Prescription Maps Using Unmanned Aerial Vehicle (UAV) Multispectral Images

• Published in: Drones (Basel) Vol. 8; no. 5; p. 176
• Main Authors: Li, Xiaojuan, Liang, Zhi, Yang, Guang, Lin, Tao, Liu, Bo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Agricultural production; Algorithms; Back propagation networks; Classification; Control; Cotton; Cotton verticillium wilt; Crop diseases; Crops; Data analysis; Data collection; Datasets; Drone aircraft; Drones; Field investigations; Flowers & plants; Fungal diseases; Image resolution; Least squares method; Leaves; Machine learning; Medical imaging; Methods; monitoring model; Neural networks; Pesticides; Plant diseases; precision spraying; prescription map; Remote monitoring; Remote sensing; Rice; Spraying; unmanned aerial vehicle (UAV) remote sensing; Unmanned aerial vehicles; Vegetation; Vegetation index; China
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones8050176

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.