Key Challenges

• Infrastructure gaps. Many rural areas lack reliable internet, stable electricity, and affordable IoT devices, making the collection of real-time data and the inference of AI difficult.
• High upfront and operational costs. Sensors, drones, edge compute devices, and software licenses often exceed smallholders’ budgets without subsidies or financing mechanisms.
• Data quality and availability. Agricultural data is fragmented, siloed, and sometimes proprietary. Localized, labeled datasets reflecting smallholder contexts are scarce.
• Limited digital skills and support. Farmers and extension agents often lack training in data-driven tools. Technical support networks are under-resourced or non-existent.
• Contextual variability. Soil types, microclimates, and cropping systems vary from farm to farm. AI models trained on large-scale commercial operations may not generalize to small plots.
• Scalability and maintenance. Pilots often struggle when transitioning from research to large-scale implementation. Long-term maintenance, updates, and troubleshooting remain unresolved.
• Policy and regulatory ambiguity. Rules around data ownership, cross-border data flows, and AI certification are still evolving, creating uncertainty for innovators and farmers alike.

Ethical Considerations

• Equity and inclusion. There’s a risk that AI benefits accrue mainly to larger farms with resources to adopt technology, widening the gap with smallholders.
• Data sovereignty and privacy. Without clear agreements, farmers may lose control over how their data is used, shared, or monetized by third parties.
• Transparency and accountability. Black-box models can generate opaque recommendations. When AI advice leads to crop loss or environmental harm, liability and recourse are unclear.
• Cultural sensitivity and traditional knowledge. Ignoring indigenous practices and local heuristics can erode trust. Ethically sound AI must integrate, not replace, farmers’ ancestral wisdom.
• Environmental footprint of AI. Training and running large models consume significant amounts of energy and water. Deployments should strike a balance between AI’s resource use and its regenerative benefits.
• Economic dependence and vendor lock-in. Reliance on proprietary platforms can trap farmers in costly subscriptions, undermining their autonomy and long-term resilience.

Emerging Approaches & Solutions

• Participatory co-design. Involve smallholders from the outset to tailor AI tools to their actual workflows and decision-making practices.
• Open data and commons models promote FAIR data standards and shared infrastructure, enabling communities to pool resources and insights.
• Edge-AI and lightweight models. Develop energy-efficient algorithms that run offline on low-power devices, thereby reducing connectivity and carbon footprint barriers.
• Community learning hubs. Establish farmer-led training centers and peer-to-peer networks for continuous skill development.
• Public-private partnerships. Blend government subsidies, NGO support, and private innovation to de-risk adoption and ensure fair pricing.
• Justice-based governance frameworks. Adopt policies that guarantee equitable data rights, transparent algorithms, and recourse mechanisms for smallholders.

The deployment of Artificial Intelligence (AI) in regenerative agriculture presents both significant opportunities and critical challenges, particularly for smallholder farmers who operate in resource-constrained environments. Key ethical considerations revolve around data transparency, equity of access, and informed consent in the use of AI-driven technologies.

One of the foremost ethical concerns is who has access to personal and farm-level data collected through AI tools such as mobile applications, remote sensing systems, or machine learning-enabled devices. These tools often require users to input sensitive personal and operational information, including land ownership details, geolocation data, crop types, and production methods. Without robust safeguards, such data could be exploited by third parties, leading to violations of privacy and potential misuse for commercial or political purposes.

Moreover, national and international data protection regulations—such as GDPR in the European context—must be integrated into AI system design and deployment. This ensures legal compliance while protecting users from data breaches and unethical surveillance practices. Developers and implementers of AI systems must proactively design with privacy-by-design and ethical-by-default principles, embedding user rights and control over data at every stage of the technology lifecycle.

Another pressing issue is the digital divide. Many smallholder farmers, especially in low- and middle-income countries, face barriers to AI adoption due to limited digital literacy, lack of internet connectivity, language barriers, and insufficient infrastructure. These constraints can exacerbate existing inequalities and leave small-scale producers behind in the digital agricultural transition.

Strategies for Making AI More Accessible and Inclusive

To ensure AI delivers equitable benefits across diverse agroecosystems, especially in improving soil health and crop productivity, the following measures are essential:

User-Centric Design and Local Adaptation

AI tools should be tailored to the local contexts, languages, and farming systems of smallholder users. This includes co-designing solutions with farmers, extension workers, and local institutions to ensure relevance and usability.

Transparent Data Governance

Clear data governance frameworks should define who owns, accesses, and controls the data. Farmers should retain rights over their data and be informed about how it will be used, stored, and shared.

Capacity Building and Digital Literacy

Training programs should accompany AI deployment to empower farmers with the skills to use, interpret, and question AI outputs. This also helps mitigate the risk of misinformation and algorithmic dependency.

Affordable and Open-Source Solutions

Developing low-cost, open-source AI applications can democratize access to advanced technologies and reduce dependency on proprietary platforms that often limit customization and transparency.

Public Sector and NGO Involvement

Public institutions and civil society should play an active role in mediating AI deployment, ensuring that ethical norms and socio-environmental goals are prioritized over purely commercial interests.

Monitoring and Impact Assessment

Continuous evaluation of AI systems should assess not only technical performance but also socio-economic and environmental impacts on smallholders. Feedback loops must be institutionalized to allow for iterative improvements.