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

Abstract

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.

Roles of Physiological and Nonphysiological Information in Sun-Induced Chlorophyll Fluorescence Variations for Detecting Cotton Verticillium Wilt

“…3 revealed a more pronounced decline in VW3 than that in VW4. This finding is consistent with previous studies that conducted physicalchemical experiments on leaves exposed to various stress levels, which concluded that chlorophyll content was lower in more severely stressed leaves [6], [44], [45]. This is also evident in Fig.…”

“…Therefore, we separated PAR as an independent factor and combined fPAR and fesc to represent non-physiological state of plant. In this study, we used NIRv and FCVI to approximate the characterization of fPAR×fesc [32], [33], [34], then (1) convert into (6).…”

“…Under VW stress, the morphology and physiological state of cotton undergo changes, resulting in alterations in some characteristic bands of the spectrum compared to healthy plants. Previous studies have used remote sensing technology to extract spectral characteristics at both leaf [4], [5], [6] and canopy [7], [8], [9], [10], [11] scales, aiming to monitor the impact of VW on cotton plants and to determine the degree of VW stress. Disease monitoring based on reflectance spectral characteristics relies on spectral responses to variations in physiological, biochemical and canopy structure indicators, such as vegetation chlorophyll content, leaf water content and leaf area index [12].…”

HairNet2: deep learning to quantify cotton leaf hairiness, a complex genetic and environmental trait

“…Mechanical approaches are particularly useful for fibre quality and fibre yield [ 1 ]. Interestingly, in recent years an increasing number of digital methods to capture detailed plant information have been developed, utilizing RGB cameras [ 2 – 8 ], hyperspectral sensors [ 9 – 11 ], thermal cameras [ 1 , 12 ], and LiDAR-based sensors [ 13 , 14 ]. These techniques offer unique advantages for characterizing various cotton plant phenotypes, can be non-destructive, and enable higher throughput applications.…”

Early detection of cotton verticillium wilt based on root magnetic resonance images

“…These methods are destructive and relatively time-consuming. With the advancement of technology, some non-destructive detection techniques have been brought up, such as hyperspectral imaging and thermal imaging ( Poblete et al., 2021 ; Yang et al., 2022 ). However, they can only detect VW based on symptoms in the above-ground parts of the plant.…”