Constructing an Agricultural Products Regional Brand Design Framework Based on Artificial Intelligence and Digital Twins Technology

Brand building for agricultural products in the mountainous regions of southwest China faces significant challenges, including complex terrain, fragmented industrial-chain data, and low market recognition. Addressing these issues requires technological innovation to enhance brand value. This study proposes a novel regional brand design paradigm that integrates Digital Twins (DT) technology with Artificial Intelligence (AI) to create a brand empowerment system capable of dynamic mapping, intelligent decision-making, and full-chain traceability. A dual-driven DT framework was established, encompassing both physical entities and virtual models. Specifically, a terrain-adaptive perception network was developed to tackle fragmented data in complex mountainous environments. AI algorithm clusters were implemented, including an improved ResNet50 model for appearance defect detection, achieving 95.2% accuracy, and a coupled Graph Neural Network–Deep Q-Network (GNN-DQN) model for supply chain risk scheduling, reducing response time by 67.4%. Pilot studies in the Zunyi chili and Pu’er tea regions validated the methodology using rigorous quantitative metrics. This study develops a pilot-validated closed-loop digital branding system that transforms ecological, quality, logistics, and consumer data into measurable outcomes, including brand awareness, perceived quality, consumer trust, willingness-to-pay, and consumer engagement. The proposed system provides a technology-enabled pathway for regional agricultural product branding, precision agricultural management, and rural industrial development.