Introduction Verticillium wilt is a severe soil-borne disease that affects cotton growth and yield. Traditional monitoring methods, which rely on manual investigation, are inefficient and impractical for large-scale applications. This study introduces a novel approach combining machine learning with feature selection to identify sensitive spectral features for accurate and efficient detection of cotton Verticillium wilt.Methods We conducted comprehensive hyperspectral measurements using handheld devices (350-2500 nm) to analyze cotton leaves in a controlled greenhouse environment and employed Unmanned Aerial Vehicle (UAV) hyperspectral imaging (400-995 nm) to capture canopy-level data in field conditions. The hyperspectral data were pre-processed to extract wavelet coefficients and spectral indices (SIs), enabling the derivation of disease-specific spectral features (DSSFs) through advanced feature selection techniques. Using these DSSFs, we developed detection models to assess both the incidence and severity of leaf damage by Verticillium wilt at the leaf scale and the incidence at the canopy scale. Initial analysis identified critical spectral reflectance bands, wavelet coefficients, and SIs that exhibited dynamic responses as the disease progressed.Results Model validation demonstrated that the incidence detection models at the leaf scale achieved a peak classification accuracy of 85.83%, which is about 10% higher than traditional methods without feature selection. The severity detection models showed improved precision as disease severity of damage increased, with accuracy ranging from 46.82% to 93.10%. At the canopy scale, UAV-based hyperspectral data achieved a remarkable classification accuracy of 93.0% for disease incidence detection.Discussion This study highlights the significant impact of feature selection on enhancing the performance of hyperspectral-based remote sensing models for cotton wilt monitoring. It also explores the transferability of sensitive spectral features across different scales, laying the groundwork for future large-scale early warning systems and monitoring cotton Verticillium wilt.

Salt stress is a significant abiotic stress that adversely affects pepper plant growth which can accelerate the development of plant pathogens and increase plant susceptibility to diseases. Verticillium dahliae, which causes pepper wilt disease, is an important biotic stress factor. Funneliformismosseae and biochar organic wastes help to take nutrients from the soil by establishing symbiotic connections with plant roots and, are effective in treating plant diseases, plant growth, and stress tolerance. This study aims to determine the effects of F. mosseae (Fm) and 2% biochar (Bc) against V. dahliae (Vd) on some plant physiological properties, plant nutrient uptake, soil pH, and EC value in pepper plants grown under salt stress (50mM, 100mM, 150mM). As a result of the study, the use of F. mosseae alone or in interaction with 2% biochar significantly increased some physiological parameters and some minerals (P, K, Mg, and Mn) contents of the plant. Moreover, pepper plants showed remarkable resistance to salt and stress factors caused by V. dahliae. In addition, the interaction between F. mosseae and biochar significantly lowered the soil EC value under conditions of severe salt stress. On the other hand, biochar was more effective than F.mosseae in terms of soil pH and Ca/Na ratio. The results showed that biochar and F. mosseae were beneficial in reducing biotic ( V. dahliae) and abiotic stress (salt stress) damage while enhancing plant growth and nutrient absorption. Therefore, this study yields excellent and novel results, particularly in the field of employing beneficial microorganisms for sustainable agriculture.