As the global population hurtles towards an estimated 9.3 billion by 2050, the issue of food security looms large. We face an urgent need not only to increase food production, but also ensure its nutritional quality while mitigating the environmental toll of agriculture.
As the UK government laid out multimillion-pound measures to address food security and food crisis at the Global Food Security Summit last November, the mandate is clear: we must grow more food sustainably to nourish a burgeoning population.
The case for a reinvented world of agriculture isstrong. We’re standing at the cusp of a transformative era, with the rapid development of AI and large language models (LLMs). These technologies hold the promise to revolutionise the entire food supply chain, reshaping the farm of the future into a beacon of sustainability, efficiency, and nutritional excellence.
Central to this vision is the concept of a data-driven agri-food system. Imagine a scenario in which every entity in the food supply chain – from input companies to retailers and farmers to end consumers – harnesses data, digitalisation, and AI to optimise decision-making. This shift promises not only operational efficiencies but also transparency, traceability, and enhanced sustainability across the entire supply chain.
The team at Microsoft Research has been dedicating our work towards this reimagined, AI-enabled future for the past decade, with four key visions across the entire food production line.
1. Shaping the farm of the future
The cornerstone of the AI-powered agriculture future lies in empowering farmers. Traditional farming often relies on instinct and experience with a small dose of guesswork, but, with AI and precision farming techniques, we can now augment farmers’ hands-on expertise with actionable insights derived from data.
Take, for instance, Andrew Nelson, a farmer in eastern Washington who embraced AI and precision farming. The key lies in aggregating and analysing data sources across the entire farm. Collecting data from sensors, drones, weather stations, satellites, and beyond provides invaluable insights for farmers for informed decision-making. By leveraging data to support decisions on irrigation, fertilisation, and chemical application, Nelson reduced chemical usage by a staggering 35%, while simultaneously increasing crop yield and sustainability.
2. Achieving supply chain transparency
Transparency is paramount in today’s complex supply chains. Leveraging AI, farmers can streamline data capture and analysis across the supply chain, including farm practices, food storage, and transportation, minimising manual interventions and enabling predictive insights. AI can also help manage shortages or disruptions even before they occur.
This becomes even more important in the face of supply chain fluctuations, geopolitical tensions, and climate change-related challenges.
3. Accelerating food discovery and R&D
Looking ahead, AI holds immense potential to accelerate the research and development of novel, nutritious, and sustainable food solutions. For instance, by harnessing the power of AI and quantum computing in collaboration with Pacific Northwest National Laboratory (PNNL), our colleagues within Microsoft made breakthroughs in chemical screening, reducing the screening time of 32 million battery chemicals in a matter of 80 hours – which would have otherwise taken decades.
The same technology has immense potential in the world of agriculture. We can drastically reduce the time needed to discover new seed types that can survive drought, new fertilisers and pesticides that have minimal environmental footprints, or to find new nutritious food ingredients that are sustainably produced.
4. Democratising digital agriculture
Generative AI can help take technology to the most remote farmer in the world. These farmers do not need to know how to read or write, but can instead converse with an AI agent to get information about subsidies, farming knowledge, and advisories. In India, we partnered with Jugalbandi to build a WhatsApp extension, through which a farmer could converse in their local language about their eligibility for a federal subsidy.
The farm of the future is not a distant dream. With the rapid pace of technological development, a tangible reality is within our grasp. We can accelerate innovation by democratising data-driven agriculture, bringing AI to every farmer and worker across the food production chain, regardless of location.
Augmenting traditional farming knowledge and experience with cutting-edge technology could shape a brighter, more sustainable future for food production.
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