Abstract: Hami melon has been one of the most favorite fruits in Xinjiang of Northwest China. In this study, a portable spectral detection device was developed for the on-site non-destructive testing on the soluble solids content (SSC) in the hami melons. The dominant varieties of hami melons were selected as Jinsenianhua, Huangjinmi, and Xizhoumi25. A general prediction model was then constructed for the mixed varieties. A diffuse transmission spectroscopy was also integrated with the interchangeable light source and a spectroscopy acquisition module. A systematic analysis was implemented to clarify the spectral response of the internal quality of hami melons under different detection postures and light source powers. The spectroscopy acquisition scheme was then optimized to design the light source arrangement and spectroscopy acquisition module. The stability and accuracy of the spectral acquisition were improved for the diffuse transmission spectroscopy acquisition of hami melons. Spectra data was then collected at 90° intervals along the equatorial part of each hami melon. The averaged spectra from each position were fused as the original input data. Five spectral pre-processing approaches were adopted to improve the data quality: detrending (DT), standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SG), and first derivative (FD). The background noise was reduced to correct the baseline drift. The scattering artifacts were removed after modification. According to the pre-processed spectra, four algorithms of the characteristic wavelength selection—competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variables elimination (UVE), and synergistic interval partial least squares (SiPLS) were adopted to screen the variables of the pre-processed spectra, and then extract the key wavelengths. A general prediction model was constructed for the SSC of hami melons. Three modeling approaches were established after prediction, namely partial least squares regression (PLSR), support vector regression (SVR), and one-dimensional convolutional neural network(1D-CNN). The results showed that the FD-UVE-1D-CNN model was achieved the best prediction performance. The correlation coefficients between the correction and the prediction set (R_C and R_P) were 0.913 and 0.896, respectively, while the root mean square errors (RMSEC and RMSEP) were 0.700% and 0.670%, respectively, and the residual prediction deviation (RPD) reached 2.256. An improved one-dimensional residual convolutional neural network (1D-Res-CNN) model was performed the feature fusion on the convolutional layers. A residual module was introduced to further improve the prediction accuracy and robustness of the model. Furthermore, the performance of this model was significantly improved after FD-UVE pre-processing, compared with the traditional 1D-CNN. Its RC and RP increased to 0.947 and 0.923, respectively, while the RMSEC and RMSEP decreased to 0.550% and 0.581%, respectively. Compared with the traditional model, the RC and RP increased by 3.7% and 3.0%, respectively, while RMSEC and RMSEP decreased by 21.4% and 13.3%, respectively. The RPD also increased to 2.601, with an increase of 15.3%. The accuracy and stability of the general model and non-destructive testing device were verified to predict the SSC in the different varieties of hami melons. The general prediction model was deployed into the device control module, particularly for the FD-UVE-1D-Res-CNN mixed variety. Ten Jinsenianhua, ten Huangjinmi and ten Xizhoumi25 were selected without modelling before. The coefficient of variation and residual analysis were conducted on the SSC prediction of the three varieties. The average coefficients of the variation of Xizhoumi25, Jinsenianhua and Huangjinmi were 2.204%, 2.794%, and 2.096%, respectively, with the maximum absolute residuals of 0.60%, 0.50%, and 0.67%, respectively. The coefficient of variation of the SSC predictions were lower than 4.5% for all varieties, and the prediction residuals were mainly concentrated within ±0.7%, indicating the cross-variety generalization and robustness. The SCC non-destructive testing device for the hami melons was effectively treated with the spectral differences among varieties and environmental noise interference, fully meeting the requirements for the prediction stability and accuracy in field applications. The better portability and field application of the device were achieved, compared with the traditional experimental platforms and large-scale detection. The overall dimensions of the machine were 300 mm×240 mm×150 mm, with a weight of 3.6 kg. A compact structure was integrated with a power module to uniformly supply power for the main control unit, light source and heat dissipation. The stable operation was equipped with the "Hami melon SSC detection system" software, which was integrated data collection, analysis, prediction and data output functions. The plug-and-play was supported for only 5 s to detect a single fruit. This device was suitable for various on-site detection scenarios, such as orchards, markets and sorting centers. In conclusion, the hardware-software solution was integrated for the rapid, non-destructive SSC detection in hami melons. Advanced spectral pre-processing was combined with the feature screening, machine learning and deep learning. The modelling accuracy and stability were improved significantly after processing. The portable detection device with the universal model of mixed varieties can meet the on-site rapid detection requirements in the various varieties of hami melons. The finding can provide a strong reference and technical support to the fruit quality and non-destructive detection of the internal quality of the fruits.