中经评论：人工智能重塑粮食产业价值链

Core Insights - Artificial intelligence (AI) is a key driver of the new technological revolution and industrial transformation in the food industry, enhancing production efficiency and reshaping the value chain [1][2][3] Group 1: AI in Food Industry Efficiency - AI is becoming a "calculating machine" for reducing food waste, transitioning the food supply chain from extensive to refined management [1] - In the procurement phase, AI visual recognition systems significantly improve sampling efficiency and fairness, reducing losses caused by waiting and misjudgment [1] - AI monitors grain conditions in storage through sensors and IoT, providing continuous analysis and automatic alerts to optimize storage conditions [1] Group 2: Data-Driven Agriculture - AI facilitates a shift from experience-driven to data-driven practices in agriculture, optimizing resource allocation and production decisions [2] - In agricultural production, AI applications include precision fertilization and irrigation, significantly reducing the misuse of fertilizers and pesticides while conserving water [2] - AI can predict crop yields months in advance by analyzing meteorological data and historical yield information, providing scientific support for decision-making [2] Group 3: Value Addition and Resilience - AI acts as an "innovation engine," transforming the food industry from primary processing to full-value utilization, uncovering hidden value in by-products [3] - AI optimizes processes to convert by-products like rice bran and soybean residue into high-value products, alleviating feed resource shortages [3] - The technology supports food innovation, creating new products such as soybean ice cream, enhancing the value of high-quality soybean resources [3] Group 4: Reshaping Industry Ecosystem - AI is reshaping the industry ecosystem, leading to new industrial models and full-chain collaboration, exemplified by fully automated "dark factories" [4] - The integration of AI and big data enables seamless connections among farmers, storage companies, processing plants, and sales terminals, addressing pain points in the food supply chain [4] - Future potential for AI in agriculture faces challenges such as inconsistent data standards and high costs, necessitating policy support and talent development [4]