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A total of 34 strains of bacteria were isolated from peanut rhizosphere soil, and all showed some in vitro inhibition of the pathogen Sclerotium rolfsii in co-culture. Strain YB-1632 produced the highest level of inhibition and also produced relatively high levels of biofilm in culture. Cell-free culture extracts and volatiles from it were also inhibitory to S. rolfsii. Based on 16S rDNA, gyrA, and gyrB sequences, it was identified as Bacillus siamensis. In the greenhouse, seed treatment resulted in a level of control of peanut sclerotium blight (PSB) comparable to that of a standard fungicide seed treatment. In addition to its antifungal activity, YB-1632 could induce disease resistance in peanut seedlings based on increasing peanut defense enzyme activities and gene expression. The priming of defense gene expression against a necrotrophic pathogen is consistent with Induced Systemic Resistance (ISR). In addition, YB-1632 produced enzyme activities in culture associated with root colonization and plant growth promotion. In the greenhouse, it increased peanut seedling growth, indicating the YB-1632 is a plant growth-promoting rhizobacterium (PGPR). In summary, YB-1632 is a promising novel PSB biocontrol agent and PGPR of peanut. Full article

Peanut southern blight is a soil-borne fungal disease caused by Agroathelia rolfsii (syn. Sclerotium rolfsii) Sacc, which seriously affects peanut yield. The disease mainly affects the stem, pod, and root of the plant, and it is difficult to detect the disease by visual interpretation. Detecting peanut southern blight using existing technology is an urgent problem that needs to be solved. To address this issue, field experiments were conducted in September 2022 to determine whether hyperspectral techniques could be used to assess the severity of peanut southern blight. In this study, we obtained 610 canopy-scale spectral data through field experiments. Firstly, 18 traditional spectral features were calculated. Then, wavelengths of 544 nm, 678 nm, and 769 nm were selected as sensitive by the Relief-F algorithm, and the NDSI_SB and NSI_SB were constructed using normalization and ratio calculation methods. Finally, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and ANN were used to evaluate the diagnostic ability of all spectral features to assess disease severity levels. The results showed that the NSI_SB had the highest association with peanut southern blight (R² = 0.817), exceeding the other spectral features. Compared to the other three models, CatBoost demonstrated superior accuracy, with an overall accuracy (OA) and Kappa coefficient of 84.18% and 78.31%, respectively. The findings of this study can serve as a reference for estimating the severity levels of peanut southern blight using ground-based hyperspectral data. Full article