Early Monitoring of Cotton Verticillium Wilt by Leaf Multiple “Symptom” Characteristics

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 20; p. 5241
• Main Authors: Yang, Mi, Huang, Changping, Kang, Xiaoyan, Qin, Shizhe, Ma, Lulu, Wang, Jin, Zhou, Xiaoting, Lv, Xin, Zhang, Ze
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Algorithms; Analytic hierarchy process; band selection; Carotenoids; Chlorophyll; Classification; Cotton; Entropy; Fungal diseases; hyperspectral reflectance; Leaves; Monitoring; Physiology; Plant diseases; Signs and symptoms; Software; spectral disease indices; Test sets; Variables; Vegetation; Vegetation index; Verticillium wilt; Weighting methods
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14205241

Early diagnosis of cotton verticillium wilt (VW) and accurate assessment of the disease degree are important prerequisites for preventing the large-scale development of cotton VW. Hyperspectral techniques have been widely used for monitoring the extent of plant diseases, but early detection of VW disease in cotton remains a challenge. In this study, the Boruta algorithm was used to select the key physiological characteristics (leaf temperature, chlorophyll a content, and equivalent water thickness) of cotton leaves at the early stage of VW disease, and then the Relief-F algorithm was used to select the spectral features indicating multiple “symptoms” of cotton VW disease at the early stage. In addition, a new cotton VW early monitoring indicator (CVWEI) was constructed by combining the weights of the new index and related bands using a hierarchical analysis (AHP) and entropy weighting method (EWM). The study showed that the physiological indices constructed under VW stress were better indicators of VW disease than traditional vegetation indices; CVEWI achieved a high accuracy of 95% in the test set, with a Kappa coefficient of 0.89; and the test set R2 was 0.73 and RMSE was 3.15% for monitoring disease severity, compared to the optimal classification constructed using a single spectral index. The results may provide new ideas and methods for early and accurate monitoring of VW and other fungal diseases.