1. Hyperspectral techniques have been used to monitor the extent of plant diseases, but early detection of VW disease in cotton remains a challenge.
2. The Boruta and Relief-F algorithms were used to select key physiological characteristics and spectral features indicating multiple “symptoms” of cotton VW disease at the early stage.
3. A new cotton VW early monitoring indicator (CVWEI) was constructed, which achieved a high accuracy of 95% in the test set for monitoring disease severity.
The article is generally reliable and trustworthy, as it provides evidence for its claims through the use of algorithms such as Boruta and Relief-F, as well as providing results from tests that demonstrate the effectiveness of the new indicator (CVWEI). However, there are some potential biases that should be noted. For example, the article does not explore any counterarguments or alternative methods for detecting VW disease in cotton at an early stage. Additionally, there is no discussion of possible risks associated with using this method or any other potential drawbacks that could arise from its implementation. Furthermore, while the article does provide evidence for its claims, it does not present both sides equally; instead it focuses solely on promoting its own method without considering any other alternatives.