Tea is one of the health drinks favored by the consumer, but during the process of tea machine harvesting and processing, it is easy to be mixed with tea stalks and foreign insect bodies. It resulted in pollution and influenced the quality and safety of tea products. In the future, we should focus on preventing and detecting of foreign bodies. X-ray imaging technology, based on the density difference between food substrate and foreign bodies, is widely applied to detect metal foreign bodies and extended to high-density plastic. However, it is not suitable for low-density organic foreign bodies such as tea stem insects, so it is urgent to develop a new and non-destructive detection technology and method. In order to solve the problem of overlapping and covering foreign bodies in tea leaves, a scheme of electromagnetic vibration feeding assisted near-infrared spectroscopy(NIRS), and fluorescence image was proposed to online detect endogenous foreign bodies of tea stalks and insects.A total of 600 NIRSranging from 600 to 1 050 nm, and 65 channel images including R, G, B and N were collected by electromagnetic vibration-assisted NIRS and fluorescence imaging system. Among them, 451 spectra were used to develop the model, and the remaining 149 spectra were used to evaluate model performance. The effects of different correction methods such as detrending, multiplicative scatter correction (MSC), standard normal variate transformation (SNV), variable sorting for normalization(VSN), adaptive iteratively reweighted penalized least squares(airPLS), alternative least squares(ALS),optical path length estimation and correction (OPLEC) were compared. OPLEC could eliminate the scattering effect better, and the correct recognition rate of the partial least squares discriminant analysis (PLS-DA) model of NIRS increased from 78% to 85%. The results showed that the calibration method of OPLEC combined with the PLS-DA model could- detect foreign bodies in tea more accurately.Compared with the accurate measurement of NIRS, imaging technologyprovided a wider range of detection means. Sixty-five clear blue (B) channel images were analyzed. Using threshold segmentation by maximum interclass variance method, inversing operation, median filtering, connected component labeling and feature extraction, we extracted four feature variables of long axis length, short axis length, short axis ratio and eccentricity, a total of 355 objects of interest.The linear discriminant analysis (LDA) model was established with 267 interesting targets, and 88 interested targets not involved in modeling were used to evaluate the model’s prediction ability. The correct recognition rate reached 64%.The experimental results show that electromagnetic vibration feeding assisted NIRS and fluorescence image is feasible for online detection of tea stalk and foreign insect bodies, providing a low-cost solution for online detection of organic foreign bodies in food.