AIOTI WG Agriculture prepared a paper on Use of robots and AI in agriculture.
This document presents a comprehensive overview on the use of robots and artificial intelligence (AI) in agriculture in the European context. It showcases the current state of the art of innovations categorising them by their Technology Readiness Levels (TRLs), ranging from fully deployed systems to early-stage research, and explores their potential to enhance productivity, sustainability, and resilience in farming.
The paper finds that AI is already benefitting a wide spectrum of agricultural applications. Besides the well-known tools like precision irrigation, variable-rate application (VRA) of fertilisers and pesticides, crop health checks, yield forecasts, and local weather updates, AI is also supporting livestock and fish farming. These include smart sensors for animal health monitoring, AI-driven milking systems, and real-time fishery analytics. Many AI models are now increasingly integrated into farm dashboards, enabling holistic real-time monitoring and decision-making. Advanced applications under development include AI agents capable of performing multiple agronomic tasks through modular, interoperable systems. Digital twins are also being tested for scenario planning, climate impact forecasting, and risk simulation. In this way, they offer predictive insights into complex environmental and economic variables. Federated learning models are also gaining momentum, given their capacity to allow collaborative AI development across farms while preserving data privacy and sovereignty—an especially important consideration in the EU context.
Robotics is also offering a diverse ecosystem of technologies with promising applications currently at different maturity levels. At the high end, large-scale autonomous machinery is already operational, bringing positive effects, such as efficiency improvement and labour dependency reduction. These systems are also integrable with precision input application and real-time monitoring AI tools. At the medium TRL level, small-scale Unmanned Vehicles (UxVs) are being deployed for their low soil compaction, energy efficiency, and adaptability to diverse cropping systems, particularly suitable to smallholder farms and fragmented plots. Precision robotics is also under development to cover for tasks requiring delicate handling of high value crops. These include selective harvesting, pruning, grafting, and seeding. Lastly, cooperative robotics constitutes and emerging frontier, with fleets of autonomous agents can coordinate with each other to perform large-scale monitoring. Examples include both human-robot collaboration (e.g., cobots assisting in complex tasks) and swarm robotics.
The synergic integration of AI and robotics can enable more efficiency in tasks such as real-time monitoring, decision-making, and execution, enhancing productivity, accelerating learning cycles, and supporting adaptive responses to climate change and market volatility. However, significant challenges also exist: from technical issues such as AI reliability and cybersecurity to regulatory hurdles related to the EU AI Act and data privacy laws. Just as important are economic barriers to adoption, and societal concerns about digital inequality and the changing role of farmers.
To illustrate these developments, a series of EU-funded use cases are presented, including drone-based monitoring platforms, federated data systems for livestock management, AI-powered farm assistants, and smart viticulture tools. These examples demonstrate the real-world impact of AI and robotics in improving efficiency, sustainability, and decision-making in agriculture.
The paper concludes with a set of policy recommendations aimed at fostering responsible and inclusive adoption of these technologies.
The full paper can be found here.