Rapeseed is the most important oil crop in China. In production practices, combined harvesters are commonly employed for one-time harvesting, resulting in significant variations in chlorophyll content among harvested rapeseeds. These variations adversely affect oil quality and increase processing costs, making real-time, on-site chlorophyll content detection crucial during rapeseed procurement and processing. To develop a portable, low-cost, and non-destructive rapid chlorophyll detection device for rapeseed, an insertion-type probe was specifically designed based on the storage characteristics of bulk rapeseed. A low-cost, non-destructive chlorophyll detection device was developed using a multispectral sensor. A partial least squares (PLS) prediction model for rapeseed chlorophyll content was established based on the developed device. After preprocessing with spectral shape feature (SSF) and successive projections algorithm (SPA), the model achieved a coefficient of determination (R2) of 0.9414 and a root mean square error (RMSE) of 2.0261mg/kg. Additionally, a real-time analysis and control software was developed by using C++ on Raspberry Pi 3B with QT5, enabling one-click human-machine interaction. Finally, the accuracy and stability of the device were externally validated by using 25 rapeseed samples not involved in modeling. Each sample was measured three times, yielding an average coefficient of variation (CV) of 3.27% for chlorophyll content. The predicted values exhibited an R2 of 0.9358 and an RMSE of 1.9071mg/kg compared with that of reference physicochemical measurements. The results demonstrated that the insertion-type non-destructive chlorophyll detection device met the requirements for on-site real-time detection, providing technical support for the industrialization of rapeseed processing.