Abstract: In order to identify the species of the most dominant mold on moldy tobacco leaves rapidly, a method of rapid identification of the most dominant mold on moldy tobacco leaves based on rapid test strips and near infrared spectroscopy (NIR) technology was proposed for the first time in this paper. The mold on moldy tobacco leaves was diluted into different gradients by physiological saline to prepare rapid test strip samples with mold. NIR technology was applied for collecting the spectra of rapid test strips with the messages of different species of the most dominant mold. The spectra were pretreated by the discrete wavelet transformation (DWT) method, which was optimized by using different wavelet basis function and decomposition layers. The training set of the most dominant mold was modeled based on wavelet coefficients by using random forest (RF). The correct recognition rate of training dataset was 98.25%, while that for testing dataset was 99.30%. The model had good classification performance and could be used to identify the most dominant mold species in tobacco. Application of rapid test strips combined with NIR technology could identify the species of the most dominant mold on moldy tobacco leaves rapidly and accurately, providing a new train of thought for microorganism identification.