An innovative sensor-based artificial intelligence system equipped with a twin delayed deep deterministic policy gradient (TD3++) algorithm and a multimodal IoT sensor network was developed to address challenges in the agri-food supply chain of the Guangdong-Hong Kong-Macao Greater Bay Area in China (GBA).Using 5.2 million sensor data points collected from 632 cold chain nodes, the system integrated the following: (1) a TD3++ routing optimizer that fused real-time global positioning system data, thermal imaging from FLIR A65 Image Temperature Sensor, and accelerometer data in a micro-electro-mechanical system; (2) a crossmodal attention mechanism; and (3) an edge-cloud detection system with hyperspectral sensor arrays using a quantized You Only Look Once version 5 model.The results showed a 23% reduction in transportation costs, a 92.7% temperature compliance, and an F1-score of 0.89 in crop disease identification.At the Shenzhen Agricultural Logistics Hub, cold chain breaches were reduced by 53% and carbon dioxide emissions by 18.3% compared with conventional long short-term memory (LSTM)-based systems.These baseline metrics for conventional LSTM performance are derived from industry standards for predictive logistics controllers.The results validate the applicability of sensor-driven AI in achieving sustainable agriculture while providing a scalable roadmap for regional cold chain modernization.Since the present study was limited to GBA and the initial deployment costs of high-precision sensor arrays, further study is required to explore hardware integration and system validation in different regions to enhance the global scalability of the framework.
Haidong Li (Mon,) studied this question.