ICRISAT
ICRISAT has 3 source-linked AI deployments documented in AIUseCaseHub, across 1 industry and 1 country.
Hyperscaler mix
See whether ICRISAT's cases are powered by Microsoft, AWS, GCP, or multiple providers.
How ICRISAT builds AI
Build / Buy / Compose across this company's documented cases
2 of 3 cases classified (67%) · Compare all use-case types
Reported outcomes
1 case reports measurable results
−40%
Sustainability & resources
median · 1 metric
+30%
Productivity & throughput
median · 1 metric
Medians of results published in ICRISAT cases, normalized for comparability. See all benchmarks →
Evidence persistence
2 of 2 judgeable cases are still publicly referenced · 2 show the organization expanding AI use.
Durability of public evidence, not whether systems remain in production. How this is measured →
Technology snapshot
What ICRISAT uses across visible cases
Voice & Speech AI appears in 2 of 3 indexed cases; 8 named technologies are mentioned, led by Machine Learning.
Capability mix
All Use Cases (3)
Indian Farmers Boost Productivity and Sustainability with AI-Powered Decision Support
The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), in collaboration with Microsoft, implemented an AI-powered Decision Support System (DSS) to assist smallholder farmers in India’s semi-arid regions. The DSS integrates real-time data from weather stations, soil sensors, and satellite imagery, and leverages Microsoft Azure AI for predictive analytics to offer farmers precise and localized agricultural recommendations. These recommendations help optimize sowing times, fertilizer usage, pest control measures, and irrigation schedules. By adopting this system, farmers experienced increased crop yields, more efficient resource utilization, and notable reductions in production costs. The system also empowered farmers through risk mitigation such as early pest outbreak warnings and improved income predictability. The AI-enhanced DSS demonstrates how technology can address complex agronomic and socio-economic challenges, especially in developing regions. The initiative provides a scalable model for sustainable digital agriculture, with significant measurable impact on productivity, sustainability, and farmer empowerment.Challenges faced by the initiative included unpredictable weather patterns leading to uncertainty in sowing and irrigation, inefficient resource utilization causing wastage, frequent pest outbreaks and crop losses, and limited access to localized agronomic insights. The DSS solution was specifically designed for resource-constrained smallholder farmers and aimed at bridging the technology adoption gap in rural India.
AI sensors and Azure boost Indian crop yields and democratize healthcare
Microsoft India and partners deployed AI-powered solutions to address challenges in agriculture and healthcare across India. Collaborating with United Phosphorus Ltd, Escorts, and ICRISAT, Microsoft used cloud-connected AI sensors, the Cortana Intelligence Suite, Power BI, and Azure platforms to enable precision agriculture for smallholder farmers. The AI Sowing app delivers sowing date advisories and pest alerts to farmers' feature phones via SMS and voice calls, helping optimize yields with no hardware investment.,In healthcare, Microsoft built an AI-powered heart disease risk score API with Apollo Hospitals, using Indian-specific cardiovascular data. Power BI and Kaizala enable real-time health record analysis and operational insights for hospital partners, supporting early diagnoses and improved patient care. Additional partnerships with SRL Diagnostics and Narayana Health expanded AI efforts to cancer detection and electronic records management. The initiative demonstrates the scalability and impact of AI in delivering both agricultural and health outcomes in rural India.
Indian farmers increase crop yields and forecasting using advanced AI advisories
ICRISAT, in partnership with Microsoft, enabled thousands of smallholder farmers in India to boost crop yields and manage market risk using a cloud-based AI solution.The AI-Sowing App, powered by Cortana Intelligence Suite and Azure, uses 30 years of climatic and satellite data, along with machine learning, to predict optimal sowing times and warn about weather and pest risks.Farmers receive personalized advisories via SMS and automated voice calls—no need for sensors or capital investments—helping them decide when and what to plant, and reduce input costs.The system includes a commodity price forecasting model, giving governments and farmers advanced insights into future pricing trends for crops such as tur, supporting smarter market planning.The initiative was deployed in Andhra Pradesh, Karnataka, Maharashtra, and Telangana, reaching thousands of farmers and improving the sustainability and economic resilience of rural communities.The application exemplifies how scalable AI models driven by satellite imagery and big data can significantly enhance national food security and agricultural outcomes.