Article by Francesca Hennig-Possenti - Chair of CEMA’s Project Team on Artificial Intelligence
The Food and Agriculture Organization (FAO) has long been a beacon of global cooperation for food security, agricultural development, and sustainability. Today, as we face unprecedented environmental and technological change, artificial intelligence (AI) stands out as a transformative force reshaping agriculture. We are at a critical intersection: AI techniques are accelerating and diversifying, while agriculture faces mounting pressure to produce more food, more sustainably. Europe, through the Farm to Fork Strategy and European Green Deal, is leading efforts to integrate AI into agricultural sustainability models.
Agricultural machinery manufacturers have embraced AI, striving to balance productivity, environmental protection, and sustainability. Yet AI is not a single product but a collection of different techniques — it requires a complete ecosystem: robust infrastructure, machines and sensors, consistent testing practices, quality data, context knowledge and AI output interpretation skills, as well as real-world validation. The use of appropriate AI techniques has the potential to increase results. However, success depends on understanding when AI is suitable to deliver efficiency—and when it does not.
AI is not a silver bullet, but it is a powerful enabler of equity, resilience, and innovation if accompanied by proper expertise.
Precision Agriculture and Rational Resource Use
AI-driven precision agriculture allows farmers to tailor inputs—water, seeds, fertilizers, pesticides—not just by field, but by plot and even plant. It boosts resilience by enabling smarter, resource-efficient and economical sustainable farming, essential as the world’s population surpasses 9 billion by 2050 and food demand increases exponentially.
Agriculture consumes massive amounts of freshwater, energy, and land. In certain geographies, this demand can conflict with the needs of local populations. AI models, however, have the potential to help mitigate these issues by optimizing resource allocation. By understanding the local context, AI is a powerful tool to ensure that resources are used efficiently and sustainably, thereby avoiding undue strain on communities and preventing unnecessary resource triage.
Precision agriculture powered by AI might help to reduce econlogical impact and promoting at the same time economical sustainability of farms. Furthermore, the optimal allocation of resources fostered by AI not only benefits individual farmers but also bolsters community resilience. When AI-driven technology is employed responsibly, it becomes a powerful enabler of equity, resilience, and innovation—transforming agriculture into a more sustainable and community-friendly enterprise.
Tackling Rural Depopulation and Empowering Women
Agriculture faces a quiet crisis: rural depopulation. Young people leave farming communities, seeking better opportunities. AI-driven technologies like autonomous machines, smart solutions, and remote management systems allow fewer people to manage more land with less strain. Importantly, AI applications and AI driven machines can make agricultural labor safer, more dignified, and more attractive to the next generation.
This is particularly important for women, who form nearly half the agricultural workforce in many regions and often face barriers due to lack of information and access to best practices, as well as the physical strain from intense farming labor. AI platforms and tools might help bridge these gaps by delivering advice, forecasts, autonomous machines, and providing robotization opportunities, strengthening female farmers’ productivity and community resilience.
Building Data Commons and AI Literacy
AI enables cooperative agriculture. Small farms can pool data to generate collective intelligence, benchmark performance, and adopt best practices. FAO can foster data commons— and shared AI expertise, accessible knowledge hubs for farmers worldwide.
But no technology succeeds without human capacity. FAO’s tradition of agricultural education must now expand to include AI literacy. From farmers in remote villages to policymakers and technologists, AI skills must become an essential part of agricultural education—multilingual, practical, accessible to all. Agricultural universities must train future leaders with multidisciplinary skills, blending agronomy, data science, ecology, law, and ethics.
Regulation, Innovation, and the Road Ahead
A good regulation, policy or law is crucial for trust, safety, and fairness. However, overregulation, rules that are impossible to fulfill or laws that are soon overriden by technological evolution, might stifle innovation, prevent market introduction or make novel approaches unaffordable, especially if laws are designed without understanding rural realities. AI in agriculture demands responsible and ethical data practices as well as evolving regulations that protect farmers while encouraging innovation. Participatory law-making—bringing together international organizations, national agencies, producers associations, civil society, and farmers—offers a path to smart, down-to-earth and inclusive regulation.
FAO can lead this dialogue, ensuring that oversight nurtures, rather than hinders, solutions essential to global food security. Let us work together—across borders, disciplines, and sectors—to ensure AI is guided by principles and purposes, helping agriculture become more resilient, equitable, and sustainable for generations to come.