Abstract: The wet bubble disease of Agaricus bisporus is a highly destructive fungal disease caused by Mycogone perniciosa, which is difficult to detect and time-consuming, often leading to the failure of mushroom houses and the serious loss of income of the mushroom farmers. Early detection and treatment for this disease can effectively solve the economic loss caused by the diseases and the quality control problems such as excessive pesticide residues. Therefore, in this study, the hyperspectral imaging technology, which could be used for the rapid nondestructive detection, was applied to the early identification of Agaricus bisporus diseases. By using the fruiting body of Agaricus bisporus as the test material, the full-band (401-1046 nm) visible/near-infrared hyperspectral image information of the pileus was collected from the fruiting body samples at the early growth stage of healthy infected Agaricus bisporus. The multiplicative scatter correction (MSC) was used for the preprocessing. The decision tree (DT) was used to extract the characteristic bands. The accuracy of the two models, Random Forest (RF) and Extreme Learning Machine (ELM), for the identification of healthy and infected Agaricus bisporus was compared. By DT, 401.00, 951.59, 978.09, 1006.59 and 1044.90 nm were selected as the characteristic bands for the disease identification. By comparing the effect of RF model and ELM model, the MSC-DT-ELM model had the best detection effect, and the identification accuracy of the test set and the prediction set were 92.39% and 91.32%, respectively. The results showed that the MSC-DT-ELM model could effectively improve the early identification accuracy of the wet bubble disease of Agaricus bisporus based on full band, and obtain a convenient and accurate model for the early identification of Agaricus bisporus disease based on the hyperspectral imaging technology. At the same time, the theoretical basis and method were provided for the further development of multispectral equipment for the rapid and nondestructive identification of Agaricus bisporus diseases in the early stage.