The grand challenge facing global agriculture today is the need to increase food production to feed a rapidly growing population, amid diminishing natural and human resources and climate pressures. With the global population expected to exceed 9.5 billion by 2050, and with several key resources being depleted (see sidebar), the agricultural community is turning to a digital revolution to secure the future of our food production. Touted Agriculture 4.0, this new movement is deploying digital technologies at scale, including field and aerial sensing, automation, and other smart devices to monitor and track resources and to improve operational efficiency. Artificial intelligence (AI) technologies are playing a central role in driving this revolution: enabling real-time decision support using spatiotemporal data collected on farms, augmenting human labor with automated decision making and robotics, estimating and forecasting risks due to extreme weather, and aiding in longer-term planning under climate-imposed uncertainties. To propel the development and deployment of AI tools and technologies for U.S. agriculture, since 2020 the U.S. Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA) has made a strategic investment in five AI institutes.

The pursuit of quality improvements and accountability in the food supply chains, especially how they relate to food-related outcomes, such as hunger, has become increasingly vital, necessitating a comprehensive approach that encompasses product quality and its impact on various stakeholders and their communities. Such an approach offers numerous benefits in increasing product quality and eliminating superfluous measurements while appraising and alleviating the broader societal and environmental repercussions. A traceable impact management model (TIMM) provides an impact structure and a reporting mechanism that identifies each stakeholder's role in the total impact of food production and consumption stages. The model aims to increase traceability's utility in understanding the impact of changes on communities affected by food production and consumption, aligning with current and future government requirements, and addressing the needs of communities and consumers. This holistic approach is further supported by an ontological model that forms the logical foundation and a unified terminology. By proposing a holistic and integrated solution across multiple stakeholders, the model emphasizes quality and the extensive impact of championing accountability, sustainability, and responsible practices with global traceability. With these combined efforts, the food supply chain moves toward a global tracking and tracing process that not only ensures product quality but also addresses its impact on a broader scale, fostering accountability, sustainability, and responsible food production and consumption.

Artificial intelligence (AI) offers a new way to track the insect pollinators essential to farming. In a new study, we installed miniature digital cameras and computers inside a greenhouse at a strawberry farm in Victoria, Australia, to track bees and other insects as they flew from plant to plant pollinating flowers. Using custom AI software, we analysed several days' video footage from our system to build a picture of pollination behaviour over a wide area. In the same way that monitoring roads can help traffic run smoothly, our system promises to make pollination more efficient. This will enable better use of resources and increased food production.

Artificial intelligence (AI) offers a new way to track the insect pollinators essential to farming. In a new study, we installed miniature digital cameras and computers inside a greenhouse at a strawberry farm in Victoria, Australia, to track bees and other insects as they flew from plant to plant pollinating flowers. Using custom AI software, we analysed several days' video footage from our system to build a picture of pollination behaviour over a wide area. In the same way that monitoring roads can help traffic run smoothly, our system promises to make pollination more efficient. This will enable better use of resources and increased food production.

Artificial intelligence (AI) offers a new way to track the insect pollinators essential to farming. In a new study, we installed miniature digital cameras and computers inside a greenhouse at a strawberry farm in Victoria, Australia, to track bees and other insects as they flew from plant to plant pollinating flowers. Using custom AI software, we analysed several days' video footage from our system to build a picture of pollination behaviour over a wide area. In the same way that monitoring roads can help traffic run smoothly, our system promises to make pollination more efficient. This will enable better use of resources and increased food production.

Wendy Gonzalez is the CEO of Sama, the provider of accurate data for ambitious AI. The population has doubled since 1974, and today, there are 8 billion humans living on this planet. While population growth has slowed, at the current rate, we should reach 9 billion in 15 years. What does it take to feed all these people? When John McCarthy coined the term "artificial intelligence" in 1955 as "the science and engineering of making intelligent machines," could he have imagined technology's effect on our ever-growing food supply needs?

AI in food production with predictive analytics will monetize the food industry in novel ways Like many other industries, artificial intelligence (AI) is having a major impact on the food and beverage industry. Companies in the industry are becoming more aware of how AI can increase efficiency and profits, reduce waste, and protect against supply chain disruptions. These are all part of what is known as Industry 4.0. With AI, businesses can traditionally predict what their customers want through research and adapting to new trend. By mining vast amounts of data about sales patterns and taste preferences by demographic, manufacturers can now predict future trends and design new products that capitalize on them more quickly.

I am spending my last weeks on thinking about climate actions, sustainability and economical aspects of these. I listened to a lot of podcasts about these topics, and I have tens of notes from these podcasts. I decided to express my ideas about this topic. But no, it is not my turn to spread the ideas. Only thing I wrote for this article is headline and a couple of keywords.

The global population is growing at an alarming rate, and with it comes the challenge of finding new ways of producing enough food to feed everyone. The current state of the food production industry calls for a shift in strategy if we want to ensure that everyone has access to healthy and nutritious food in the future. Artificial intelligence (AI) is one of the key tools we need to achieve this goal. With AI, we can tackle complex problems, optimize processes, and produce results that would be much harder or even impossible to do manually. In this article, we will explore how AI will change how we feed the world, offer some valuable insights to farmers, and show where AI currently succeeds in food production and where it still has its limitations.

Over the last several decades, genetic research has seen incredible advances in gene sequencing technologies. In 2004, scientists completed the Human Genome Project, an ambitious project to sequence the human genome, which cost $3 billion and took 10 years. Now, a person can get their genome sequenced for less than $1,000 and within about 24 hours. Scientists capitalized on these advances by sequencing everything from the elusive giant squid to the Ethiopian eggplant. With this technology came promises of miraculous breakthroughs: all diseases would be cured and world hunger would be a thing of the past.