Abstract: During the strategic "North-to-South Grain Transfer" process, maize is highly susceptible to rapid quality deterioration and fungal infection due to complex environmental fluctuations. Traditional detection methods are often time-consuming, destructive, and labor-intensive, creating an urgent need for non-destructive evaluation methods capable of rapid mold ratio identification and continuous quality index prediction. To address this critical issue, this study proposes a novel synchronous prediction method for maize mold ratio and storage/transportation quality based on advanced image processing and deep learning technologies. Maize samples with a meticulously controlled gradient mold ratio ranging from 0 to 12% were selected as the primary research objects. The typical temperature and humidity environments of both waterway and overland transportation routes were systematically simulated in laboratory settings. Following the simulated storage and transportation periods, key intrinsic quality indices of the maize, including moisture content, fatty acid value, and electrical conductivity, were systematically measured to comprehensively quantify the deterioration process. Simultaneously, maize images were collected using a standard smartphone. By integrating digital image processing technology, a Vision Transformer (ViT) deep learning model, and sophisticated statistical modeling, the synchronous detection of the mold ratio and the precise prediction of quality indices were successfully achieved. The experimental results clearly indicated that the high-humidity environment characteristic of waterway transportation significantly accelerated the deterioration of maize kernel quality (P < 0.05). Specifically, the moisture content of the waterway samples rapidly exceeded the national safe storage standard threshold of 14% when the mold ratio reached just 2%. In stark contrast, the moisture content of the overland transportation samples remained relatively stable within the safe range of 12.207% to 12.772%. Furthermore, as the maize mold ratio progressively increased, the fatty acid value and electrical conductivity under waterway conditions exhibited significant increases of 57.070% and 38.357%, respectively. These deterioration rates were notably higher than those observed under overland conditions, which demonstrated comparatively lower increases of 29.035% and 27.714%, respectively. Regarding the performance of the deep learning algorithms, the utilized ViT architecture achieved exceptionally high precision in identifying moldy maize kernels, reaching an impressive overall accuracy of 99.00%. The subsequent prediction of the overall maize mold ratio was highly accurate and reliable, yielding a Mean Absolute Error (MAE) of only 0.52%. Building upon these robust results, visual features were further extracted and screened to construct Multiple Linear Regression (MLR) models for quality evaluation. For the waterway samples, the coefficients of determination (R²) for the prediction models of moisture content, fatty acid value, and electrical conductivity reached 0.859, 0.955, and 0.942, respectively. For the overland samples, the R² values for the fatty acid value and electrical conductivity prediction models were 0.930 and 0.937, respectively, indicating that this research method can accurately predict the quality of maize during storage and transportation. In conclusion, this study not only systematically reveals the dynamic quality deterioration rules of moldy maize under different storage and transportation environments but also provides a low-cost, easy-to-operate, and entirely non-destructive technological pathway for comprehensive maize quality detection. This research ultimately offers an effective and practical analytical tool for dynamic quality safety monitoring and risk early warning during the complex grain circulation process.