KALRO boosts Kenyan crop yields with AI-powered precision agriculture
Kenya Agriculture and Livestock Research Organisation (KALRO) implemented an AI-driven system to enhance agricultural productivity among smallholder farmers. Working with CABI, they deployed the PRISE early warning system using Microsoft Azure, Generative AI, and Earth Observation data. The solution provides zone-specific pest and weather advisories to over four million farmers via SMS and digital bulletins. Additionally, solar-powered, computer-vision pest detectors were rolled out, delivering on-site alerts in seconds for rapid intervention. This digital advisory network has been key to scaling agricultural insights and decision-making nationwide. Results included up to 30% reduction in crop pest losses and up to 40% yield increases for vulnerable farmers, illustrating how digital transformation is reshaping smallholder agriculture in Kenya. The scalable setup allows continuous improvement as new data and AI models are integrated.
- Industry
- Agriculture
- Location
- Kenya
- Published
- September 2025
Reported outcomes
40%
timeTime & speed
Strategic outcomes
Catalog median for time & speed deployments: +80% across 203 reported metrics. Compare benchmarks →
Primary read
Use case focus
Showing 3 of 3
- 1AI-driven precision pest monitoring and early warning for smallholder farmers
- 2Automated advisory delivery for yield optimization using cloud analytics
- 3Solar-powered edge-computer vision for real-time pest detection
- Frequent and severe crop pest outbreaks threaten food security and livelihood for millions of Kenyan smallholder farmers.
- Limited timely access to localized pest and weather advisories for rural farmers.
- Traditional detection and intervention methods are slow, resulting in delayed responses and lost yields.
- Smallholders have limited technology access for real-time decision support.
- Deployment of the PRISE early warning system integrating Microsoft Azure and Generative AI for data analysis and rapid advisory delivery.
- Integration of Earth Observation data, weather forecasts, pest lifecycles, and agronomy datasets to produce actionable, localized insights.
- Distribution of advisory content to 4 million+ farmers via SMS and PDF bulletins for accessibility.
- Deployment of solar-powered, computer-vision pest detectors to deliver actionable alerts within seconds at the farm level.
- Reduction of pest losses by up to 30% in pilot regions.
- Yield increases of up to 40% for vulnerable farmers through timely interventions.
- Digital advisories scaled to reach more than four million Kenyan farmers, supporting improved nationwide agricultural productivity.
- Practical demonstration of scalable, replicable digital agriculture transformation for emerging markets.
Architecture
PRISE combines Microsoft Azure cloud and AI services, local data integration (weather, pest lifecycle, Earth Observation), Generative AI for analytics, and edge-deployed solar-powered pest detection devices, all funneling alerts and advisories via automated SMS/PDF platforms to farmers. Digital two-way channels widen access and allow for ongoing update and feedback loops between centralized analysis and frontline growers.
Implementation partners1
Sources & evidence1
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