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.
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Claims 5
Claim · model-written summary
Physiological indices constructed under VW stress were better indicators of VW disease than traditional vegetation indices.
- Taxon
- cotton
Gossypium hirsutumt0 - Interactor
- VerticilliumVerticilliumt0pathogen
- Outcome
- indicators of VW diseasee1
- Direction
- bettere1
- Comparator
- traditional vegetation indicese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Cotton Verticillium Wilt”
e1“the physiological indices constructed under VW stress were better indicators of VW disease than traditional vegetation indices”
Claim · model-written summary
The CVEWI index achieved 95% accuracy in the test set.
- Taxon
- cottonGossypium hirsutumt0
- Interactor
- VerticilliumVerticilliumt0pathogen
- Outcome
- accuracye2
- Value
- 95%e2
- Qualifier
- in the test sete2
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Cotton Verticillium Wilt”
e2“CVEWI achieved a high accuracy of 95% in the test set, with a Kappa coefficient of 0.89”
Claim · model-written summary
The CVEWI index achieved a Kappa coefficient of 0.89.
- Taxon
- cottonGossypium hirsutumt0
- Interactor
- VerticilliumVerticilliumt0pathogen
- Outcome
- Kappa coefficiente2
- Value
- 0.89e2
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Cotton Verticillium Wilt”
e2“CVEWI achieved a high accuracy of 95% in the test set, with a Kappa coefficient of 0.89”
Claim · model-written summary
In the test set, the R2 for monitoring disease severity was 0.73, compared with the optimal single-index classification.
- Taxon
- cottonGossypium hirsutumt0
- Interactor
- VerticilliumVerticilliumt0pathogen
- Outcome
- test set R2e3
- Value
- 0.73e3
- Comparator
- optimal classification constructed using a single spectral indexe3
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Cotton Verticillium Wilt”
e3“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”
Claim · model-written summary
In the test set, the RMSE for monitoring disease severity was 3.15%, compared with the optimal single-index classification.
- Taxon
- cottonGossypium hirsutumt0
- Interactor
- VerticilliumVerticilliumt0pathogen
- Outcome
- RMSEe3
- Value
- 3.15%e3
- Qualifier
- for monitoring disease severitye3
- Comparator
- optimal classification constructed using a single spectral indexe3
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Cotton Verticillium Wilt”
e3“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”