Optical methods can provide measurements without coming into contact with the sample. In the agrifood sector, this feature can be exploited to measure the physical properties of crops. In particular, we focused our research on moisture content (MC) and density estimation. These two physical quantities of the crop are extremely important not only to determine future treatments to be performed, e.g., drying methods and processes but also for estimating the value of the product. In this article, we propose a new model for simultaneous estimation of crop MC and density, using Fourier transform near-infrared spectroscopy combined with partial least square multivariate methods. The model has been developed considering 140 fresh Medicago sativa samples properly harvested. MC ranged from 9.4% to 83.9% whereas density from 46 to 236 kg/m³. Reference MC was computed according to the American Society of Agricultural and Biological Engineers standard whereas reference density was determined by estimating the volume of a sample of known mass. The obtained results indicated that crop MC and density information can be recovered from the near-infrared absorption spectrum of the sample with coefficients of determination $R^{2} =0.925$ and $R^{2} =0.681$ for the MC and density, respectively. The mean root mean square relative errors of the estimation were 13.8% and 14.4% for the MC and density, respectively.