A novel approach for rapid (15s) detection and quantification of predominant cannabinoids in hemp was developed using Fourier-transformed near-infrared spectroscopy (FT-NIR), enabling real-time and field-based applications. Hemp samples (n = 91) were obtained from certified online vendors, the OARDC Weed Lab, and a local Ohio farm. Reference data of major cannabinoids content were determined by uHPLC-MS/MS. Spectral data were collected by a miniaturized, battery-operated FT-NIR instrument, and combined with the reference data to generate partial least squares regression (PLSR) models. uHPLC-MS/MS analysis showed two samples had over 0.36% of Δ9-tetrahydrocannabinol (Δ9-THC), and 64% (32 out of 50) of online-bought hemp samples were not in compliance with their total cannabidiol (CBD) content declaration. PLSR prediction models showed excellent correlation (Rpre = 0.91–0.95) and a low standard error of prediction (SEP = 0.02–0.61%). This method could be used as an alternative to traditional methods for in-situ assessment of hemp quality.