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

Abstract

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 < 51.48), followed by the NDTI model (R2 > 0.47, RRMS < 60.81) and the CI model (R2 > 0.33, RRMSE < 52.58). The PSO-BP and GWO-ELM were further used to model the DI estimation with different input variables. Regardless of the period, the fusion of three data sources (VIs + CIs + TIs) was preferable to a single data source or a combination of two data sources for different model inputs. In terms of different modeling algorithms, GWO-ELM combining VIs, CIs, and NDTIs had the highest estimation accuracy compared with SR and PSO-BP, with a validated R2 values of 0.65 (RRMSE = 42.96) at the flowering stage, 0.66 (RRMSE = 20.00) at the flower and boll stage, and 0.88 (RRMSE = 10.53) at the boll stage. This study demonstrated that the estimation accuracy of DI was significantly improved using collaborative modeling with multiple data sources. This study provides ideas and methods for monitoring crop disease conditions using low-altitude remote sensing.