The watermelon breed testing system combining machine vision and hyperspectral imaging was developed to enhance efficiency, reduce labor, and improve consistency in quality evaluation. The system included an automatic image acquisition device, featuring a Gige camera and a weighing platform, to capture watermelon phenotypic data. It used hyperspectral imaging on selected regions of interest to model SSC distribution within the watermelon, with measurements of transverse and longitudinal diameters achieved through minimum external rectangle fitting and rind thickness estimated using Canny edge detection. Convolutional smoothing (SGS) algorithm was used to preprocess the spectral data by combining three algorithms, namely multivariate scattering correction (MSC), standard normal transform (SNV), and unit vector normalisation (UVN), respectively, and then the best preprocessed spectral data were filtered by competitive adaptive reweighting algorithm (CARS), successive projection algorithm (SPA), and one-time combined dimensionality reduction algorithm (CARS+SPA) for feature wavelength screening, and finally the screened spectral data were used to build PLSR models and) analyzed for comparison. The results showed that the system had the highest accuracy of 98.68%, 98.82% and 93.81% for the measured values of transverse diameter, longitudinal diameter and rind thickness of watermelon, respectively, with the root mean square error of 2.43 mm, 2.08 mm and 1.63 mm, respectively, as compared with the manual measurements. The best model for predicting watermelon brix was (SGS+UVN)-CARS-PLSR, with prediction correlation coefficients and of 0.920 4 and 0.912 7, root mean square errors RMSEC and RMSEP of 0.376 0°Brix and 0.466 8°Brix, respectively, and a relative analytical error of prediction RPD of 2.49.