Core Viewpoint - Artificial intelligence (AI) is transforming the food industry by shifting from extensive management to precise management, significantly reducing food loss and waste, which represents a substantial hidden cost in the industry [1][2]. Group 1: AI in Food Industry Management - AI acts as a "calculating master" in reducing food loss and waste, with significant potential for savings in the food supply chain [1][2]. - In the procurement phase, AI visual recognition systems enhance sampling efficiency and fairness, minimizing losses caused by delays and misjudgments [2]. - AI-driven sensors and IoT in storage facilities enable continuous monitoring and analysis of grain conditions, providing automatic alerts and controlling environmental systems to protect grain quality [2]. Group 2: AI in Agricultural Production - AI is pivotal in the agricultural sector, facilitating precision farming through accurate fertilization, irrigation, and pest diagnosis, which reduces chemical usage and conserves water [3]. - AI can predict crop yields months in advance by analyzing weather data, historical yields, and real-time crop images, thus supporting informed decision-making [3]. - The integration of AI with blockchain technology creates a traceable quality assurance system from farm to table, enhancing food safety and brand trust [3]. Group 3: AI as an Innovation Engine - AI is driving the food industry from initial processing to full-value utilization, uncovering hidden values in by-products and enhancing industry resilience [4]. - By optimizing processes, AI converts by-products like rice bran and soybean residue into high-value products, alleviating feed resource shortages and increasing profitability [4]. - AI supports food innovation, leading to the development of new products such as soybean ice cream, thereby extending the industry chain and improving risk management [4]. Group 4: AI and Industry Ecosystem - AI is reshaping the industry ecosystem, giving rise to new business models and collaborative supply chains [5]. - Fully automated "dark factories" controlled by robots and AI systems enable continuous production, significantly enhancing efficiency and product quality [5]. - The combination of AI and big data facilitates integrated platforms that connect various stakeholders in the food supply chain, addressing issues of fragmentation and information flow [5]. Group 5: Challenges and Future Directions - Despite the potential of AI in the food sector, challenges such as inconsistent agricultural data standards, high costs, and talent shortages remain [5]. - There is a need for national policy implementation, collaborative efforts, and the development of low-cost, modular AI solutions to enhance industry adoption [5]. - Establishing a comprehensive data collection system and fostering interdisciplinary talent development are essential for AI to effectively empower the food industry [5].

Group 1 - The core viewpoint of the articles highlights the significant advancements in China's grain processing machinery industry, showcasing the transition from reliance on imports to self-innovation and the emergence of domestic equipment as a competitive force in the market [1][2][3] - The seventh China Grain Trade Conference featured hundreds of domestic processing equipment, including intelligent rice milling machines and AI sorting machines, indicating a robust development in grain machinery localization [1] - The promotion of intelligent equipment is expected to drive the grain processing industry towards refinement, effectively reducing breakage rates, nutrient loss, and energy waste during processing [1] Group 2 - Under the dual drive of policy and market, a number of specialized "little giant" enterprises have emerged in China's grain machinery industry, mastering core technologies and breaking the long-standing technological monopoly [2] - Regional innovation hubs have formed, such as Hefei being recognized as the "capital of sorting machines," showcasing a complete optical sorting industry chain with leading companies [2] - The domestic grain machinery industry is experiencing a cluster development trend, with various regions excelling in different aspects of grain processing technology and equipment [2] Group 3 - The rise of domestic equipment is reshaping the industry, with improved quality and lower prices making them favorable among domestic grain processing enterprises, leading to some equipment achieving import substitution and even reverse export [3] - The increase in the localization rate of grain processing equipment ensures the autonomy and control of the grain processing industry chain, alleviating concerns about external dependencies [3] - Future challenges include the need for enhanced performance of high-end equipment and the establishment of a comprehensive standard system, necessitating a deep integration of production, education, research, and application [3]