In order to solve the problems of long artificial conception cycle, unstable regional semantic expression and lack of consistency of series styles in the packaging graphics design of regional agricultural products, this paper constructs a packaging graphics design method based on multimodal AI generation technology. In this study, the product name, origin description, cultural keywords, regional pattern images, product photos and layout constraints are integrated into the unified computing framework. Through multi-modal feature extraction, cross-modal alignment, condition generation and discriminant evaluation, the intelligent generation of packaging graphics is realized. The experiment was carried out based on 4260 groups of multimodal samples, and the training set, validation set and test set were divided by 7:2:1. The results show that the SSIM of the proposed model reaches 0.871, the PSNR reaches 31.84 dB, the text-image semantic consistency reaches 0.903, and the comprehensive color coordination score is 8.74, which are better than those of the template stitching method, the single-modal text-generated image model and the basic condition generation model. In the tea packaging task, SSIM is further improved to 0.884 and PSNR reaches 32.41 dB. Research shows that this method can better coordinate the relationship between regional culture translation, commodity recognition and visual organization, and provide a more operational implementation path for the combination of agricultural product packaging graphic design and computer generation technology.
