Case Study 3: AI for Smart Agriculture in Resource-Constrained Settings

Using RAISEF to deliver AI agronomy recommendations for smallholder farmers while respecting equity, privacy, safety, and local context in resource-constrained regions

This case study follows a program that applied RAISEF across the lifecycle to build an offline-capable advisory platform for farmers with limited connectivity, devices, and data. Teams defined intended benefits and risks up front, co-designed requirements with local stakeholders, and selected indicators tied to fairness, inclusiveness, privacy, robustness, explainability, human oversight, security, and sustainability. Development emphasized curated, consented datasets, low-resource models, interpretable outputs, and traceable artifacts. Testing combined subgroup evaluation with stress checks for weather variability, language and literacy barriers, and adversarial misuse. Deployment used staged rollouts, clear disclosures, and rollback plans, followed by monitoring of yield, adoption, error patterns, and user feedback routed to accountable owners. The result is a practical pattern for improving productivity and resilience without compromising rights, trust, or long-term viability.

Photo by Markus Winkler from Burst

Background and Context

Agriculture is critical for economic development and food security, particularly in resource-constrained regions such as Sub-Saharan Africa. Smallholder farmers, who form the backbone of agricultural activity in these areas, face challenges such as unpredictable weather, limited access to modern technologies, and inefficiencies in resource utilization.

AI-powered solutions, such as crop monitoring and predictive analytics, promise to transform agricultural practices by optimizing water use, improving yield predictions, and reducing waste. However, implementing AI in resource-constrained settings involves unique obstacles, including limited datasets, inadequate digital infrastructure, and cultural barriers to adoption.

This case study examines how RAISEF was applied to develop a hypothetical AI-based smart agriculture system tailored to the needs of smallholder farmers. The system addressed these challenges while balancing competing priorities such as inclusiveness, transparency, and robustness.

Implementation of AI

The initiative introduced an AI-powered platform to assist smallholder farmers in monitoring crops, predicting weather patterns, and managing resources efficiently. The platform utilized satellite imagery, weather data, and ground-level sensors to provide actionable recommendations.

RAISEF guided implementation across lifecycle stages:

Development:

Open-access satellite imagery and crowd-sourced local data were integrated to compensate for the scarcity of localized datasets.
Fairness-aware algorithms ensured that recommendations were equally effective across diverse environmental conditions and farmer profiles.
Sustainability-focused models optimized resource use, particularly water and fertilizer.

Deployment:

A mobile-friendly interface was designed to cater to farmers using basic smartphones.
Training sessions were conducted to improve digital literacy and build trust among farmers.
The system was localized to align with regional languages and farming practices.

Monitoring:

Farmers provided continuous feedback on the system’s recommendations, enabling iterative refinements.
Independent audits ensured accountability and fairness across the platform’s recommendations.

Sector-specific considerations included ensuring cultural sensitivity in deployment and tailoring recommendations to local farming practices and resource availability.

Key Challenges

Technical Challenges:

Adapting AI models to work with incomplete or noisy data from rural regions.
Ensuring robustness under extreme environmental variability, such as unpredictable weather patterns.

Ethical Challenges:

Ensuring inclusiveness by addressing barriers like digital literacy and access to affordable technology.
Preventing biases that could favor wealthier farmers over smallholders with fewer resources.

Regulatory and Cross-Cultural Challenges:

Navigating local regulations around the use of satellite and sensor data.
Building trust among farmers who were initially skeptical of technology due to cultural and historical factors.

Outcomes and Impact

Positive Outcomes (hypothetical):

Crop yields improved by 25%, with better resource optimization and timely interventions.
Water usage decreased by 20%, contributing to sustainable farming practices.
Adoption rates exceeded 40% in pilot regions, with significant participation among women farmers.

Unintended Consequences:

Limited connectivity in some rural areas delayed access to recommendations, highlighting the need for offline functionality.
Some farmers relied too heavily on AI recommendations, reducing the use of traditional agricultural knowledge.

Alignment with RAISEF

The following matrix summarizes some examples of how RAISEF’s 15 drivers were addressed:

Driver	How It Was Addressed (Multiple Examples)	Example Tensions and How They Were Resolved
Pillar: Ethical Safeguards
Fairness	Ensured model performance across diverse farm sizes. Balanced recommendations for low-resource farmers.	Fairness vs. Robustness ■ is resolved by balancing equitable model performance across diverse farm conditions with the need for reliable outputs under extreme variability.
Inclusiveness	Localized recommendations tailored to regional farming practices. Mobile-friendly platform for accessibility.	Inclusiveness vs. Trustworthiness ■ is resolved by validating datasets for inclusiveness without compromising the system’s reliability and stakeholder confidence, which is achieved through rigorous testing and stakeholder engagement.
Bias Mitigation	Used fairness-aware algorithms to prevent biases against small-scale farmers. Improved dataset diversity through crowd-sourced regional data.	Bias Mitigation vs. Explainability ■ is resolved by balancing the complexity of fairness-aware algorithms with the need to generate transparent and understandable outputs for farmers, achieved through iterative simplification of key explanations.
Accountability	Established independent audits for model fairness and accuracy. Enabled farmers to provide feedback on outputs.	Accountability vs. Privacy ■ is resolved by creating anonymized audit trails to safeguard farmer data while maintaining transparency.
Privacy	Ensured compliance with local data protection regulations. Anonymized sensitive farmer information.	Privacy vs. Inclusiveness ■ is balanced by implementing clear data-sharing governance, ensuring ethical data use without excluding regions.
Pillar: Operational Integrity
Governance	Defined ethical use policies for AI applications in agriculture. Conducted regular system audits to ensure compliance.	Governance vs. Privacy ■ is resolved by implementing strict governance protocols to ensure ethical data use while safeguarding sensitive farmer information through privacy-preserving measures.
Robustness	Validated system performance under extreme weather conditions. Enhanced accuracy using real-time feedback loops from farmers.	Robustness vs. Explainability ■ is resolved by ensuring the system remains resilient under diverse conditions while simplifying model explanations to maintain usability for farmers.
Interpretability	Designed intuitive user interfaces that allow farmers to visualize decision-making logic easily.	Interpretability vs. Security ■ is resolved by limiting sensitive data exposure while ensuring outputs remain understandable and actionable.
Explainability	Simplified AI recommendations into actionable steps. Provided farmers with training to interpret outputs.	Explainability vs. Robustness ■ is balanced by maintaining key model outputs while simplifying user-facing explanations to ensure usability.
Security	Secured sensitive farmer data using encryption and robust access control. Protected the system from unauthorized access and attacks.	Security vs. Transparency ■ is resolved by balancing the need to protect sensitive data with the obligation to provide clear, actionable information to farmers, achieved through selective disclosure and controlled access.
Safety	Validated recommendations with agricultural experts. Provided manual override features to avoid crop failures.	Safety vs. Fairness ■ is resolved by ensuring that safety mechanisms, such as conservative recommendations, do not disproportionately disadvantage small-scale farmers, which is achieved through iterative validation and adjustments.
Pillar: Social Empowerment
Sustainability	Optimized resource usage, such as water and fertilizer. Reduced waste through predictive analytics.	Sustainability vs. Privacy ■ is resolved by ensuring that data collection for optimizing resources is conducted ethically, with anonymization techniques protecting farmer information while supporting sustainable practices.
Human Oversight	Enabled agricultural experts to review AI outputs periodically. Incorporated farmer feedback into model updates.	Human Oversight vs. Privacy ■ is resolved by ensuring oversight processes protect sensitive farmer information through anonymized reviews and secure data-sharing protocols.
Transparency	Shared system performance metrics and validation results publicly. Integrated explainability tools for clarity.	Transparency vs. Privacy ■ is balanced by providing insights without exposing sensitive farmer data.
Trustworthiness	Shared system performance metrics and validation results publicly. Integrated explainability tools for clarity.	Trustworthiness vs. Inclusiveness ■ is addressed by validating inclusive datasets without compromising system reliability.

Lessons Learned

Inclusiveness Improves Adoption: Designing systems that account for digital literacy and affordability expands access to marginalized groups, particularly women farmers.
Transparency Builds Trust: Simplified recommendations and actionable insights increase confidence in the platform.
Sustainability and Fairness Are Interlinked: Optimizing resource use benefits both environmental and economic outcomes for smallholder farmers.

As articulated in all case studies, these insights reinforce the importance of a holistic approach. Treating all drivers equally is vital to responsible AI.

Broader Implications

This case study demonstrates the potential of AI to address productivity and sustainability challenges in resource-constrained settings by leveraging RAISEF. The initiative balanced competing priorities while ensuring ethical and practical outcomes. The insights gained can guide similar initiatives in sectors like public health, where resource constraints and cultural factors play critical roles.

Sources and References

Open-access satellite data resources