本文探讨了人工智能和机器学习在食品生产中日益重要的作用。从农业到工厂，AI可以提高效率、优化供应链，并加速新型健康产品的研发。AI在农业中帮助农民监测作物健康，优化投入，提高收割效率。在实验室中，AI驱动基因编辑实验，提高作物抗逆性和营养价值。对于加工食品，AI优化生产经济，改善替代蛋白和健康零食的质地和风味，并加强食品安全流程。尽管潜力巨大，但行业采用仍然滞后，数据共享有限，企业需求和能力差异大。报告指出，预测分析正在加速作物和食品科学的研发周期，AI正在为分散的供应链带来数据驱动的见解，伙伴关系对于最大限度地发挥各自优势至关重要。

🌱AI在农业领域的应用：AI技术正帮助农民更有效地监测作物健康状况，精准调整投入，并实现更准确、高效的收割，从而优化农业生产流程。

🧪AI加速食品研发：AI通过减少新食品产品实验所需的时间和资源，将传统的试错周期转变为更高效的数据驱动型发现，从而加速了作物和食品科学的研发周期。

🤝AI赋能供应链优化：AI能够打破运营孤岛，将海量数据转化为可操作的智能，从而彻底改变食品行业复杂的价值链。大型语言模型（LLM）和聊天机器人可以充当数字翻译，为农民和种植者普及数据分析，并使食品公司能够做出更明智的战略决策。

💡合作是关键：大型农业公司在人工智能实施方面处于领先地位，但有希望的突破往往来自战略合作，这些合作利用学术机构和初创企业的互补优势。大型公司贡献广泛的数据集和行业经验，而初创公司则带来创新、创造力和清洁的数据。

📊数据驱动决策：AI通过预测分析和数据驱动的洞察力，帮助食品生产商做出更明智、更有效的决策，从而优化资源利用，提高生产效率。

There has never been a more pressing time for food producers to harness technology to tackle the sector’s tough mission. To produce ever more healthy and appealing food for a growing global population in a way that is resilient and affordable, all while minimizing waste and reducing the sector’s environmental impact. From farm to factory, artificial intelligence and machine learning can support these goals by increasing efficiency, optimizing supply chains, and accelerating the research and development of new types of healthy products. 

In agriculture, AI is already helping farmers to monitor crop health, tailor the delivery of inputs, and make harvesting more accurate and efficient. In labs, AI is powering experiments in gene editing to improve crop resilience and enhance the nutritional value of raw ingredients. For processed foods, AI is optimizing production economics, improving the texture and flavor of products like alternative proteins and healthier snacks, and strengthening food safety processes too. 

But despite this promise, industry adoption still lags. Data-sharing remains limited and companies across the value chain have vastly different needs and capabilities. There are also few standards and data governance protocols in place, and more talent and skills are needed to keep pace with the technological wave. 

All the same, progress is being made and the potential for AI in the food sector is huge. Key findings from the report are as follows: 

Predictive analytics are accelerating R&D cycles in crop and food science. AI reduces the time and resources needed to experiment with new food products and turns traditional trial-and-error cycles into more efficient data-driven discoveries. Advanced models and simulations enable scientists to explore natural ingredients and processes by simulating thousands of conditions, configurations, and genetic variations until they crack the right combination. 

AI is bringing data-driven insights to a fragmented supply chain. AI can revolutionize the food industry’s complex value chain by breaking operational silos and translating vast streams of data into actionable intelligence. Notably, large language models (LLMs) and chatbots can serve as digital interpreters, democratizing access to data analysis for farmers and growers, and enabling more informed, strategic decisions by food companies. 

Partnerships are crucial for maximizing respective strengths. While large agricultural companies lead in AI implementation, promising breakthroughs often emerge from strategic collaborations that leverage complementary strengths with academic institutions and startups. Large companies contribute extensive datasets and industry experience, while startups bring innovation, creativity, and a clean data slate. Combining expertise in a collaborative approach can increase the uptake of AI. 

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.