监测成本降至进口1/5！，“智探粮芯”系统为粮仓装上“AI大脑”

Core Insights - The "Smart Grain Core" monitoring and early warning system, developed by Professor Mao Bo and his student team from Nanjing University of Finance and Economics, provides a new generation of intelligent solutions for food security in China, integrating multi-modal perception, AI recognition, and predictive decision-making [1][3]. Group 1: Technology and Innovation - The system represents a shift from traditional grain management methods, which relied on experience, to a comprehensive approach that combines multi-modal hardware, intelligent algorithms, and service platforms for real-time perception, intelligent analysis, and proactive management of grain storage environments [3][4]. - The core device, a self-developed multi-modal intelligent probe, integrates five functions: temperature, humidity, moisture, gas concentration, and pest image recognition, achieving a cost that is approximately one-fifth of imported equipment while covering more monitoring dimensions [4][6]. Group 2: Performance and Impact - The system has demonstrated significant improvements in grain storage management, allowing for real-time monitoring and early detection of pest issues, which enhances safety and efficiency [4][6]. - The algorithm employs an improved YOLOv8-seg model for high-precision pest identification, achieving over 90% accuracy even in challenging conditions, and utilizes an LSTM time-series prediction model to forecast pest risks, providing a 4-7 day intervention window [6][7]. Group 3: Achievements and Future Plans - The "Smart Grain Core" project has secured five software copyrights, two invention patents, and one high-level paper, and has been successfully piloted in major grain storage enterprises such as COFCO and China Grain Reserves Corporation [7]. - The team is working on building an integrated platform for algorithms and computing power, aiming to expand the intelligent monitoring and management capabilities to more grain storage enterprises, injecting new momentum into the traditional industry [7].