Maize is susceptible to rapid quality deterioration and fungal infection due to complex environmental fluctuations during "North-to-South Grain Transfer" strategies. However, conventional detection is often time-consuming, destructive, and labor-intensive under different storage and transportation environments. It is an urgent need to non-destructively and rapidly identify the mold ratio, and then continuously predict the quality index. In this study, a synchronous prediction was proposed for maize mold ratio and storage/transportation quality using advanced image processing and deep learning technologies. Maize samples with a controlled gradient mold ratio ranging from 0 to 12% were selected as the research objects. A systematic simulation was conducted on typical temperature and humidity environments of both waterway and overland transportation routes. Key quality indices of the maize were measured to quantify the deterioration rates in the simulated storage and transportation periods, including moisture content, fatty acid value, and electrical conductivity. Simultaneously, maize images were collected using a standard smartphone. Digital image processing was also integrated with the Vision Transformer (ViT) deep learning model and statistical modeling. The mold ratio was then detected to precisely predict the quality indices. The results indicated that the high-humidity environment of waterway transportation accelerated the deterioration of maize kernel quality (P<0.05). Specifically, the moisture content of the waterway samples rapidly exceeded the threshold of 14% in the national safe storage standard when the mold ratio reached 2%. In contrast, the moisture content of the overland transportation samples remained stable in the safe range of 12.207% to 12.772%. Furthermore, the fatty acid value and electrical conductivity increased by 57.070% and 38.357%, respectively, under waterway conditions, as the maize mold ratio increased progressively. These deterioration rates were higher than those under overland conditions, indicating the lower increases of 29.035% and 27.714%, respectively. In terms of the deep learning algorithms, the ViT architecture achieved exceptionally high precision in identifying moldy maize kernels, reaching an impressive overall accuracy of 99.00%. Subsequently, a Mean Absolute Error (MAE) of only 0.52% was achieved, indicating the accurate and reliable prediction of the overall maize mold ratio. Visual features were extracted and further screened to construct Multiple Linear Regression (MLR) models for quality evaluation. In the waterway samples, the coefficients of determination (R²) of the prediction models reached 0.859, 0.955, and 0.942, respectively, for moisture content, fatty acid value, and electrical conductivity. In the overland samples, the R² values of prediction models were 0.930 and 0.937, respectively, for the fatty acid value and electrical conductivity, indicating accurate prediction for the quality of maize during storage and transportation. In conclusion, the dynamic quality deterioration of moldy maize can provide a low-cost, easy-to-operate, and entirely non-destructive pathway for maize quality detection. This finding can also offer an effective and practical analytical tool to dynamically monitor quality and safety for risk early warning during the complex grain circulation.