Intelligent monitoring of fruit and vegetable freshness in supply chain based on 3D printing and lightweight deep convolutional neural networks (DCNN).

In this study, an innovative intelligent system for supervising the quality of fresh produce was proposed, which combined 3D printing technology and deep convolutional neural networks (DCNN). Through 3D printing technology, sensitive, lightweight, and customizable dual-color CO2 monitoring labels were fabricated using bromothymol blue and methyl red as indicators. These labels were applied to sensitively monitor changes in CO2 levels during the storage of vegetables such as green vegetables, cucumbers, okras, plums, and jujubes. The ΔE of the labels was found to have a significant positive correlation with CO2 levels and weight loss rate, while showing a strong inverse relationship with hardness, indirectly reflecting the freshness of the produce. In addition, four lightweight DCNN models (GhostNet, MobileNetv2, ShuffleNet, and Xception) were applied to recognize label images from different storage days, with MobileNetv2 achieving the best performance. The classification accuracy for three freshness levels of okra was 96.06 %, 91.12 %, and 93.86 %, respectively. A mobile application was developed based on this model, which demonstrated excellent performance in recognizing labels at different storage stages, making it suitable for practical applications and effectively distinguishing freshness levels. By combining the novel labels with advanced DCNN models, the accuracy and real-time capabilities of food monitoring can be significantly improved.