In agriculture, AI is already embedded in autonomous tractors, precision-farming platforms, livestock monitoring systems, and supply-chain management tools. The AI Act interacts with this landscape in two central ways. First, it classifies certain agricultural AI systems—especially safety components in agricultural machinery and AI used in critical infrastructure management—as high-risk, triggering requirements for risk management, data governance, conformity assessment, and post-market monitoring. Second, it imposes transparency and governance obligations on decision-support tools and interactive systems that influence farmers’ choices, while operating alongside the EU Data Act and sectoral data-space initiatives that aim to improve farmers’ access to their own data.
This article examines how the AI Act will affect companies across the agricultural value chain. It focuses on the risk-based structure of the Act, its application to key agricultural use cases, the resulting obligations for machinery manufacturers and digital farming providers, operational impacts on farms and supply chains, and strategic governance choices for firms that rely on AI in agricultural contexts.
1. The AI Act in the agriculture context
1.1 Risk-based structure and agrifood scope
The AI Act classifies AI systems into four categories: unacceptable-risk systems, which are prohibited; high-risk systems subject to detailed ex-ante controls; limited-risk systems with transparency obligations; and minimal-risk systems governed primarily through general law and voluntary standards. High-risk systems fall into two groups: AI that is a safety component of products covered by listed EU harmonisation legislation (Annex I), or itself such a product; and AI used in high-risk use cases enumerated in Annex III.
In agrifood, the most direct link is to agricultural machinery law. Regulation (EU) No 167/2013 on the approval and market surveillance of agricultural and forestry vehicles is explicitly referenced in Recital 49 as a regime that must be aligned with the AI Act when products incorporate AI-based safety components. The Act’s Article 6 then defines the conditions under which AI systems in such machinery are treated as high-risk, while Article 103 amends Regulation 167/2013 to integrate AI-specific requirements into type-approval procedures.
Beyond machinery, the Act applies to AI systems used in agrifood supply chains, farm-management platforms, decision-support tools, and interactive advisory systems whenever they meet the definition of an AI system and are placed on the EU market or used in the EU. This includes systems developed and hosted outside the EU but deployed for European farmers or agribusinesses, reflecting the Act’s extraterritorial reach.
1.2 Amendments to agricultural machinery regulation
Article 103 of the AI Act amends Regulation 167/2013 to require that type-approval procedures for agricultural and forestry vehicles take AI-specific obligations into account when vehicles include AI-powered components. Autonomous driving features, GPS-guided steering, vision-based safety systems, and precision-farming functions integrated into tractors and towed machinery must now be assessed for compliance with both existing safety requirements and AI Act provisions on risk management, data governance, technical documentation, and human oversight.
Legal commentary notes that this dual assessment is intended to avoid parallel certification tracks. The AI-relevant elements should be evaluated within the same type-approval dossier, but using criteria drawn from the AI Act. For machinery manufacturers, this means that AI components can no longer be treated as unregulated “software features”; they become part of the regulated safety architecture that determines whether a vehicle can be sold or used in the EU.
1.3 Agrifood AI as cross-cutting infrastructure
AI in agriculture is not limited to on-farm operations. It supports crop and livestock management, input optimisation, risk management, logistics, and tracing along the food chain. A European Parliament study on AI in the agrifood sector highlights that sensing, data collection, and AI-based analysis are increasingly used to support decision-making in crop and animal production, as well as in processing and distribution. Research from Wageningen University on the AI Act and the food system emphasises that the Act applies across this spectrum of AI uses, from farm-level decision-support to food-safety and traceability systems.
The AI Act therefore needs to be understood alongside other policy instruments affecting agrifood data and digitalisation, including the Data Act, the planned common European agricultural data space, and initiatives on sustainable and resilient food systems under the Green Deal. Companies in the sector will need to navigate this combined regulatory environment rather than treating the AI Act in isolation.
2. Agricultural AI use cases under the Act
2.1 AI safety components in agricultural machinery
AI systems embedded in agricultural vehicles and implements are at the core of the Act’s application to agriculture. Recital 49 and Article 103 explicitly identify agricultural and forestry vehicles under Regulation 167/2013 as products where AI safety components must comply with the AI Act in addition to existing type-approval rules. Article 6 states that an AI system is high-risk when it is intended to be used as a safety component of such a product and the product requires third-party conformity assessment.
In practice, this includes AI-based autonomous or semi-autonomous navigation, obstacle detection for field vehicles, roll-over prevention, automated braking, and safety-related monitoring of implements such as sprayers or harvesters. If the failure of these systems could endanger operators, bystanders, or property, they will be treated as safety components and must meet high-risk obligations, including risk management, data governance, documentation, human oversight, and logging.
Digital farming literature highlights that AI-enhanced tractors and implements are central to precision agriculture, enabling variable-rate application of inputs, automated steering, and coordinated machinery operations. Under the AI Act, these benefits do not remove the need for structured risk analysis and traceability. Instead, manufacturers must show that AI-based control systems meet safety and reliability standards comparable to traditional control systems, with additional scrutiny of data and model behaviour.
2.2 Precision farming and decision-support systems
AI-enabled precision-farming systems analyse soil data, remote-sensing imagery, weather forecasts, and historical yield records to generate recommendations on planting density, fertiliser application, irrigation, and crop protection. These systems are typically implemented as decision-support tools, advising farmers and agronomists rather than exercising direct control over machinery or irrigation infrastructure.
Under the AI Act’s risk framework, many of these tools will fall into limited-risk or high-risk categories depending on their function and degree of autonomy. If a system directly controls irrigation or chemical application in ways that could endanger human health or safety—such as by overdosing pesticides near populated areas—it can be classified as high-risk either as a safety component or as part of critical-infrastructure management. If it provides recommendations that farmers implement manually, it may be considered limited-risk but still subject to transparency and documentation expectations.
The Commission-commissioned report on AI for sustainable agriculture highlights both the potential of AI decision-support tools to reduce inputs and manage risk and the structural obstacles to adoption, including regulatory uncertainty, limited data access, and lack of interoperability. The AI Act adds an additional layer of governance. Providers of precision-farming systems must document model design, training data, performance metrics, and limitations, and deployers must consider how these systems influence decisions with economic, environmental, and social consequences.
2.3 Livestock monitoring, animal health, and welfare
AI is increasingly used for livestock monitoring, including computer-vision systems that track animal movement and body condition, acoustic monitoring to detect respiratory diseases, and behaviour analysis to identify distress or welfare issues. These systems may be integrated into barn control systems, feeding robots, or milking equipment.
The AI Act does not create a separate high-risk category for animal-welfare systems, because its primary focus is on risks to human health, safety, and fundamental rights. However, livestock-monitoring AI can still be high-risk when its outputs influence decisions that affect workers or food safety—for example, when AI analyses determine culling decisions, veterinary interventions, or adjustments to environmental controls that affect occupational safety.
Scholars have noted that the AI Act pays limited explicit attention to non-human-centered values such as animal welfare and biodiversity, despite their importance in agrifood systems. This suggests that ethical and sustainability considerations in livestock AI will need to be addressed through a combination of AI Act compliance, sectoral animal-welfare rules, and voluntary codes rather than relying solely on AI-specific legislation.
2.4 Agrifood logistics, traceability, and food safety
AI also plays a role beyond the farm gate, in logistics, storage, processing, and retail. Applications include demand forecasting and routing, predictive modelling of spoilage, and anomaly detection in supply-chain data. AI-enabled traceability systems can help identify contamination sources, manage recalls, and support verification of origin and production methods.
AI systems used for managing critical elements of food-supply infrastructure may be considered high-risk under Annex III if they materially influence access to essential services or health outcomes. For example, AI-driven distribution systems that allocate food supplies across regions or detect potential contamination may fall within the scope of high-risk AI when failure could impact public health or food security.
The European Parliament’s work on AI in the agrifood sector lists food-safety and traceability applications as important areas for AI adoption, but also notes risks associated with opaque decision-making, data concentration, and unequal access to information among different actors in the supply chain. The AI Act’s requirements for documentation, risk analysis, and human oversight are intended to make these systems more transparent and accountable.
2.5 Prohibited practices and manipulative AI in agriculture
While most agricultural AI systems will fall into high-risk or limited-risk categories, some configurations may approach prohibited practices. The AI Act prohibits AI systems that exploit vulnerabilities of specific groups due to age, disability, or socio-economic status in ways that materially distort behaviour and cause harm. Academic work on digital farming notes concerns about power asymmetries between farmers and large agritech platforms, including the potential for AI-based advisory tools or contract platforms to steer farmers toward decisions that favor the platform’s interests.
Current analyses indicate that many standard agronomic advisory tools will not meet the threshold for prohibited practices, but systems explicitly designed to manipulate farmers’ decisions in ways that systematically disadvantage them could face scrutiny under the Act’s vulnerability and manipulation provisions. The line between legitimate decision-support and exploitative design will likely be drawn through enforcement practice and sector-specific guidance.
3. Obligations for agricultural companies
3.1 Risk-management and data-governance requirements
Providers of high-risk AI systems must implement a risk-management system that covers the entire lifecycle of the AI system. For agricultural machinery and digital farming providers, this includes identifying risks to health, safety, and fundamental rights that arise from AI-enabled features, estimating their magnitude, and defining control measures, with updates based on testing and operational experience.
Data governance is central. The AI Act requires that training, validation, and testing datasets be relevant, representative, and sufficiently free of errors for the intended purpose, and that they be managed in ways that comply with EU law, including rules on non-discrimination and data protection. In agriculture, relevant data sources include remote-sensing imagery, soil and yield maps, machine telemetry, weather data, and farm-management records. The Wageningen study on AI and the food system stresses that data access and quality are already significant challenges, particularly for smaller farms and service providers. The AI Act’s data-governance requirements may push companies to invest in more structured data pipelines and documentation.
Technical documentation and record-keeping obligations extend to model architectures, training processes, performance metrics, and human-oversight mechanisms. For machinery manufacturers, these AI-specific files must be aligned with existing technical documentation prepared for type-approval or CE-marking under sectoral law. For digital farming providers, they define the basis on which regulators and clients can evaluate claims about system performance and limitations.
3.2 Conformity assessment and integration with type-approval
High-risk AI systems must undergo conformity assessment before being placed on the market or put into service. For AI that is a safety component of agricultural vehicles under Regulation 167/2013, Article 103 requires that AI Act compliance be integrated into type-approval procedures. This means that notified type-approval authorities will assess not only traditional safety aspects but also AI-related risk management, data governance, documentation, and human oversight.
Legal analyses emphasize that integration is intended to avoid duplicate assessment tracks. The same technical dossier should support both type-approval and AI Act conformity assessment, with AI-specific requirements embedded in the broader product-safety evaluation. For manufacturers, this creates a strong incentive to design AI functionality with compliance in mind from the earliest stages of product development.
Where sectoral legislation allows self-assessment, providers may conduct AI-related conformity assessment internally under an appropriate quality-management system. Where third-party assessment is required, AI-relevant aspects will be evaluated by type-approval authorities or designated notified bodies, depending on national arrangements. In both cases, the outcome is a CE marking that signals conformity with the AI Act and the underlying sectoral legislation.
3.3 Roles: providers, deployers, and data intermediaries
Companies in the agricultural value chain can occupy multiple roles under the AI Act. Agricultural machinery manufacturers that design and integrate AI-based safety systems are providers of high-risk AI systems. Digital farming platforms and agritech startups that develop decision-support tools, farm-management software, or AI-enabled advisory services are also providers when they place systems on the market or make them available as services.
Farmers, cooperatives, processors, and retailers that use AI systems are deployers. They must use systems in accordance with the provider’s instructions, monitor operation, and ensure that human oversight is effectively implemented, particularly where decisions significantly affect safety, economic outcomes, or fundamental rights. Deployers also have obligations to suspend use and inform providers or authorities when serious risks are identified.
The Data Act introduces the role of data holders and data recipients in relation to connected products and related services, giving farmers stronger rights to access and share data generated by agricultural machinery and farm-management systems. Providers of AI systems in agriculture will need to align AI Act compliance with these data-sharing obligations, particularly in relation to training data, access to historical records, and transparency about how data is used in AI models.
3.4 Penalties and enforcement in agrifood contexts
The AI Act’s penalty framework applies to agricultural companies in the same way as to other sectors. Serious infringements—including use of prohibited practices, failure to comply with core high-risk obligations, or breaches of requirements for general-purpose AI models—can lead to fines of up to 35 million euros or 7 percent of global annual turnover. Other major infringements can result in fines up to 15 million euros or 3 percent of turnover.
Market-surveillance and type-approval authorities can order withdrawal or recall of non-compliant AI-enabled machinery, or prohibit the use of certain AI features until they are brought into conformity. Given the seasonal nature of agricultural operations, such measures could have significant operational and financial impacts if they occur during critical periods such as planting or harvest. This adds practical urgency to early compliance planning for both manufacturers and large farming enterprises.
4. Operational impacts on farms and agribusinesses
4.1 Mapping AI systems in agricultural operations
For companies in agriculture, an essential operational step is to map where AI is used across products and services, and to classify each system under the AI Act’s risk categories. This mapping should encompass AI embedded in machinery, cloud-based decision-support tools, logistics systems, risk-management models in insurance or finance arms, and interactive advisory interfaces for farmers or consumers.
Academic analyses note that AI in agriculture often appears as a feature of broader systems—farm-management platforms, equipment telematics, or advisory services—rather than as standalone AI products. The mapping exercise must therefore go beyond explicitly labelled “AI” products and examine how algorithms influence recommendations, automation, and contractual terms. For large agribusinesses with integrated supply chains, this may reveal a complex, layered AI landscape that crosses business units and geographic regions.
4.2 Aligning AI governance with farm-level realities
Compliance requirements will need to be translated into practices that make sense in farm and agribusiness contexts. European Commission-commissioned work on AI in sustainable agriculture underscores that many farms and smaller providers face limited technical capacity, heterogeneous data formats, and challenges integrating AI tools into existing workflows. The AI Act adds expectations for documentation, monitoring, and human oversight that could be difficult to meet without support.
For example, risk-management processes must consider how AI recommendations are actually used on farms: whether farmers treat system outputs as suggestions or instructions, how they combine AI advice with agronomic knowledge, and what training is provided to operators. Human-oversight mechanisms must be realistic in environments where operations are time-critical and labor resources are constrained. This may require simplifying interfaces, providing clear confidence indicators, and building in safe-fallback modes for AI-enabled machinery.
4.3 Data infrastructure, interoperability, and farmer control
The AI Act’s requirements intersect with broader concerns about agricultural data governance. Studies on digital farming stress that farmers often face limited access to data generated by their own machinery and farm-management systems, as well as lock-in effects created by proprietary platforms. The Data Act seeks to address these issues by granting users of connected products—including farm equipment—rights to access and share data with third parties.
From an AI Act perspective, improved data access and interoperability can support compliance by making it easier to document training and operational data, audit models, and support independent evaluation. However, it also raises questions about responsibility for data quality and the potential for multiple actors to use the same data to train competing models. Companies will need to design data-governance frameworks that respect farmers’ rights, comply with AI Act and Data Act obligations, and manage commercial risks associated with data sharing.
4.4 Adoption gaps and support mechanisms
Reports on AI in agriculture consistently note adoption gaps between larger agribusinesses and smaller farms, as well as between regions with stronger digital infrastructure and those with weaker connectivity and advisory support. The AI Act’s compliance requirements may reinforce these patterns if smaller actors lack resources to evaluate AI tools, understand documentation, or negotiate contractual terms that align with their interests.
To counter this, the Commission’s report on AI for sustainable agriculture recommends using regulatory sandboxes, demonstration projects, and shared infrastructure—such as an agri-food AI “factory”—to lower entry barriers and support trustworthy AI solutions tailored to agricultural needs. The AI Act itself provides for regulatory sandboxes and guidance for SMEs, which could be targeted at agricultural innovation ecosystems. The effectiveness of these instruments will depend on how accessible they are to smaller agritech providers and farmer cooperatives.
5. Strategic implications for agricultural companies
5.1 Business models and bargaining power
The AI Act interacts with existing debates about power relations in digital agriculture. Research on the impact of the AI Act and Data Act on digital farming technologies argues that these laws can both constrain and enable new business models. On the one hand, AI governance requirements may increase compliance costs for platform providers and machinery manufacturers, potentially consolidating markets around larger firms that can absorb those costs. On the other, data-access rights and transparency obligations may strengthen farmers’ bargaining position vis-à-vis digital platforms and agritech providers.
Companies will need to adapt their business models to a context in which farmers and cooperatives can demand more information about how AI systems use their data, how recommendations are generated, and how risks are managed. This may involve offering differentiated service tiers, more transparent contractual terms, or cooperative approaches to data sharing and model development.
5.2 Innovation and competitiveness in agritech
The AI Act may influence innovation trajectories in agritech. Providers will need to factor compliance into their development roadmaps, investing in documentation, testing, and monitoring capabilities that go beyond pure model performance. For some startups and SMEs, these fixed costs could be significant, particularly when systems fall into high-risk categories that require formal conformity assessment.
At the same time, policy documents emphasise that clear regulatory expectations can reduce uncertainty and support investment in AI tools that are aligned with sustainability and resilience goals. The Commission’s report on AI for sustainable agriculture highlights the importance of shared infrastructure, interoperability standards, and regulatory support in enabling SMEs to develop and deploy AI tools that meet both agronomic and regulatory requirements. Companies that integrate compliance into their innovation strategies may be better positioned to access EU funding, participate in data spaces, and expand into other regulated sectors.
5.3 Sustainability, climate goals, and non-human impacts
Digitalisation and AI are central to many strategies for reducing agriculture’s environmental footprint and achieving climate and biodiversity targets. AI-enabled precision farming can reduce fertiliser and pesticide use, improve water efficiency, and support adaptive management under climate variability. The AI Act does not directly regulate environmental impacts, but its governance requirements can influence which AI tools are developed and how they are deployed.
Academic work on the AI Act and the food system notes that the regulation pays limited explicit attention to non-human-centered values such as ecosystem health and animal welfare, even though these are central to agrifood sustainability. Companies seeking to align with EU climate and biodiversity objectives may therefore need to go beyond minimal compliance, integrating environmental and animal-welfare considerations into AI design and risk assessment even when they are not strictly mandated by the Act.
5.4 Global spillovers and international trade
Agricultural machinery and digital farming services are global markets, and the AI Act is likely to influence practices beyond the EU. Analyses of the Act’s impact on digital farming technologies suggest that non-EU manufacturers serving the European market will face strong incentives to design AI systems that comply with EU requirements, both for regulatory and commercial reasons.
This may create a de facto global benchmark for AI-enabled agricultural machinery and platforms, in the same way that EU product-safety and environmental standards have influenced global manufacturing. At the same time, differences in regulatory approaches across jurisdictions may require companies to manage multiple compliance regimes, particularly where AI-enabled machinery is exported to markets with different safety and data-governance expectations.
6. Governance recommendations for agricultural companies
6.1 Establish cross-functional AI governance
Agricultural companies should build cross-functional AI governance structures that include engineering teams, agronomists, data scientists, legal and compliance staff, and business-unit leaders. This governance should oversee AI system inventories, risk classification, conformity assessment, data-governance policies, and incident management, with clear lines of responsibility at senior management and board level.
For machinery manufacturers, AI governance can be integrated into existing product-safety and quality-management systems that already handle conformity assessment under Regulation 167/2013 and related frameworks. For digital farming providers, it should be aligned with software-development and customer-support processes, ensuring that documentation, updates, and user training reflect AI Act requirements.
6.2 Align AI development with agronomic and sustainability objectives
Compliance with the AI Act should be treated as one dimension of responsible AI in agriculture, alongside agronomic effectiveness and sustainability. Companies should involve agronomists, farmers, and advisors in the design and evaluation of AI systems to ensure that recommendations and automation align with real-world practices and environmental objectives.
Risk assessments should not only consider immediate safety risks but also medium-term impacts on soil health, water use, and biodiversity where relevant, even if these are not explicitly required by the AI Act. This broader perspective can help companies anticipate future regulatory developments and align with policy priorities under the Green Deal and Farm-to-Fork strategy.
6.3 Invest in data infrastructure and farmer-centric governance
To meet AI Act and Data Act requirements, companies should invest in data infrastructure that supports high-quality, auditable AI while respecting farmers’ rights and expectations. This includes clear data-sharing agreements, transparent explanations of how data is used in model development, and mechanisms that allow farmers to access and port their data to alternative providers.
Shared infrastructure initiatives—such as the proposed common European agricultural data space and sector-specific data platforms—can help reduce duplication and lower barriers for SMEs. Companies that participate in these initiatives and adopt interoperable data formats may gain advantages in terms of regulatory compliance, ecosystem integration, and trust among farmers and advisors.
6.4 Engage with standardization and regulatory dialogue
Finally, agricultural companies should engage actively in standards development and regulatory dialogue. The ITU Focus Group on AI for Agriculture (AI4A) and various European standardization initiatives provide venues where companies, researchers, and regulators can discuss technical, ethical, and regulatory aspects of AI in agriculture.
Participation in these processes allows firms to anticipate emerging norms, contribute practical experience to the design of AI-related standards, and clarify how AI Act provisions should be applied to specific agricultural contexts. Early dialogue with type-approval authorities, market-surveillance bodies, and data-protection regulators can reduce uncertainty and support more predictable pathways for bringing AI-enabled products and services to market.
Conclusion
The EU AI Act introduces a structured governance framework for AI that has significant implications for companies operating in the agriculture industry. By classifying AI safety components in agricultural machinery as high-risk, integrating AI requirements into Regulation 167/2013, and applying risk-based obligations to decision-support and supply-chain systems, the Act reshapes how AI is designed, documented, and deployed in agrifood contexts.
For agricultural machinery manufacturers, digital farming platforms, and agribusinesses, compliance will involve integrating AI risk management into existing product-safety and quality-management systems, strengthening data-governance and logging practices, and aligning AI design with both regulatory requirements and agronomic realities. For farmers and cooperatives, the combination of AI Act obligations and data-access rights under the Data Act may open opportunities to negotiate more transparent and equitable relationships with digital service providers, though support will be needed to overcome capacity and adoption gaps.
The companies that approach the AI Act as a design constraint and governance framework—rather than as a narrow legal hurdle—are more likely to develop AI systems that are robust, auditable, and aligned with broader objectives for sustainable and resilient agriculture in Europe.
References
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