Cotton Verticillium wilt monitoring based on UAV multispectral-visible multi-source feature fusion

• Published in: Computers and electronics in agriculture 2024-02, Vol.217, p.108628, Article 108628
• Main Authors: Ma, Rui, Zhang, Nannan, Zhang, Xiao, Bai, Tiecheng, Yuan, Xintao, Bao, Hao, He, Daidi, Sun, Wujun, He, Yong
• Format: Article
• Language: English
• Subjects: Cotton verticillium wilt; Machine learning; Multi-source fusion; Remote sensing monitoring; Unmanned aerial vehicle
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2024.108628

•Three features were extracted to estimate Cotton Verticillium wilt disease index (DI).•Compared to color index and texture, vegetation index was significantly correlated with DI.•The introduction of normalized difference texture index combined with two textures can improve the accuracy of DI estimation.•Collaborative modeling of multiple sources of data to estimate DI has yielded good results. Verticillium wilt seriously jeopardizes cotton growth and restricts cotton yields. Therefore, it is important to accurately, rapidly, and non-destructively estimate the extent of cotton Verticillium wilt (CVW). The focus of this study was to explore the potential of combining the vegetation index (VI), color index (CI), and texture features to improve the accuracy of CVW disease severity estimation based on hexacopter Unmanned Aerial Vehicle (UAV) images. Simple Linear Regression (LR) and Multiple Linear Regression (MLR) methods were used to determine correlations between VI, CI, texture, and normalized difference texture index (NDTI) variables and cotton Verticillium wilt disease index (DI). The LR model based on VI, CI, and NDTI was constructed, VIs, CIs, and NDTIs were fused, and Grey Wolf Optimizer (GWO) Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) Backpropagation Neural Network (BP) models were constructed to comparatively explore the estimation ability of each model on the degree of CVW disease. The results showed that VI was significantly correlated with DI, followed by NDTI, and CI. Compared with texture, NDTI containing both texture features was more significantly correlated with DI. The accuracy of the DI estimation using LR was highest for the one-factor VI model (R2 > 0.48, RRMSE  0.47, RRMS  0.33, RRMSE