Use case type
Crop yield prediction solutions estimate future harvest output using weather, soil, crop, and historical farm data. They address uncertainty in planning by helping farmers and agribusinesses make decisions about planting, resources, and logistics.
11
11
1
8 mo
Adoption over time
By case publish month · completed months only
5 cases documented across 37 months (Jul 23 – Jul 26), peaking at 2 in May 2025.
4 earlier cases before Jul 23 not shown
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
Company examples
10 shown from 11 use cases
Dynamics 365 modular ERP/CRM applications help agriculture businesses automate procurement, supply chain, finance, workforce management, and provide predictive analytics including crop yield forecasting with Power BI.
Smallholder farmers, especially in Brazil and similar regions, are leveraging AI-enabled technologies to transform agricultural productivity and resilience in the face of climate volatility and decreasing support from traditional agronomists. Drawing on Microsoft Azure AI, farmers are accessing hyperlocal weather forecasts via satellite and sensor data, making more informed sowing, irrigation, and harvesting decisions. AI-powered mobile apps, such as those built on Azure ML, enable rapid disease detection through photo analysis, minimizing misdiagnosis and unnecessary pesticide use. Livestock owners utilize sensors and AI tools for early illness detection in animals, improving herd health and profitability. To bridge digital divides, voice-based and localized chatbots powered by AI deliver farming advice in native languages via basic smartphones, helping digitally underserved farmers participate in modern agriculture. These innovations address significant barriers to food security, increase yields, and foster inclusion for marginalized rural populations.
AgriPilot.AI, implemented by Click2Cloud with Microsoft Azure, is revolutionizing Brazil's agricultural sector through precise crop yield prediction. Utilizing data sources like weather patterns, soil conditions, and satellite imagery, the solution aids farmers in resource optimization and sustainable practices. It delivers tailored analytics including dashboards, real-time updates, visualizations, and detailed reports to inform farming decisions effectively. This solution promises enhanced profitability, reduced risks, and better market strategies for farmers.
Codewave deployed Microsoft Azure-based AI and IoT solutions for farms across Brazil, tackling agricultural challenges with data-driven intelligence. Leveraging cloud and edge-based image recognition, predictive analytics, and sensor data, their projects enable precise monitoring of crop health, early disease detection via computer vision, optimized irrigation, and improved supply chain planning. Solutions are highly scalable—integrating sensors, drones, and mobile apps to drive yield stability and sustainability. Brazilian farms involved in these pilots achieved demonstrable results: earlier pest and disease alerts, more efficient water and input use, and strengthened compliance with environmental regulations. Codewave’s tailored approach brought AI-powered automation to both small and large agricultural operations, transforming operational efficiency and resource management in Brazil’s agriculture sector.
Bayer's Crop Science division leverages Microsoft Azure Data Manager for Agriculture to unify and analyze agronomic data from more than 220 million acres of farmland globally. Through its Climate FieldView platform, Bayer ingests weather, satellite, sensor, and equipment data, delivering AI-powered insights for disease detection, yield assessment, and optimized intervention. The collaboration unlocks real-time data access and analytics for farmers, agronomists, and insurers, boosting productivity and sustainability. A conversational copilot chat interface allows users to query complex data in natural language, accelerating threat response and digital enablement. Azure Data Manager's connectors facilitate integration with third-party equipment and external data sources, expediting innovation. The ongoing partnership aims for fully digitized Crop Science sales by 2030, with significant progress in outcomes-based, data-driven agriculture and connected ecosystem value for customers worldwide.
A collaboration between Microsoft and Tomorrow.io leverages Microsoft Azure HPC, AI, and satellite data to provide African farmers and agencies with actionable weather insights. The initiative addresses the challenge of unreliable weather data that leads to crop losses and reduced yields across Africa, where agriculture and food security are paramount concerns. By combining AI-powered, high-resolution weather forecasting with scalable cloud computing, the solution enables near real-time climate intelligence and customized alerts to support climate-resilient farming. The system helps farmers optimize planting, improve risk management, and enhance productivity. In partnership with regional governments, the platform strengthens meteorological infrastructure, ensures better protection against extreme weather events, and supports broader global food security efforts. Pilot projects, including those announced at COP27, demonstrate measurable increases in yield and resilience for smallholder and large-scale farms alike.
JBS S.A., Brazil’s leading meatpacking company, partnered with DSM Nutritional Products Brazil to predict and optimize beef production across more than 4 million animals from 5,204 farms. The real-world project tackled the complex challenge of forecasting beef carcass weight, maturity, fat, and quality by integrating animal traits, nutritional inputs, market prices, soil fertility, and climate data from 12 states. The team developed and tuned advanced machine learning models (Random Forest, Generalized Linear Regression, Neural Networks) in R, orchestrated via high-performance computing. The Random Forest model performed best for continuous variables, informing yield prediction and business decisions. Integrated predictions allow better resource allocation, increased sustainability, and strategic production planning. This approach is paving the way for more efficient and sustainable beef production at a national scale and demonstrates the business value of large-scale AI adoption in agriculture.
Microsoft Azure tools were employed in two innovative projects to tackle drought. In Spain, AI and geospatial analytics were used to model water demand for agriculture, enhancing sustainability in Murcia. Meanwhile, in Argentina, S4 Agtech employed Microsoft's cloud to create a drought index for risk assessments in farming. These applications demonstrate AI’s role in addressing water challenges worldwide.
Microsoft collaborated with ICRISAT to develop an AI Sowing App and Pest Risk Prediction API to address challenges in farming caused by irregular rainfall and pest attacks. Participating farmers received sowing advisories and pest risk alerts via SMS, enabling optimized sowing and pest control based on predictive insights. The project benefited over 3,000 farmers, improving crop yields by 10%-30% and minimizing irrigation wastage. Advanced models analyzed long-term weather patterns and soil moisture indexes to calculate optimal sowing times, while the Pest Risk Prediction API proactively warned of potential pest attacks.
SEGES Innovation, Denmark's leading agricultural knowledge and R&D center, embarked on a digital journey to enable farmers and food producers to lead in sustainable agriculture. Leveraging decades of agriculture data, SEGES collaborated with Microsoft and the partner twoday kapacity to modernize its machine learning operations. Their challenge lay in managing vast datasets, ensuring high-yield, healthy livestock, and accurate crop forecasting, all while maintaining environmental and economic sustainability.Historically dependent on fragmented on-premises solutions, SEGES faced inefficiencies in maintenance and scalability, hampering rapid model deployment and innovation.By building a custom MLOps platform powered by Azure Machine Learning, Azure Synapse Analytics, Azure Data Lake, and Azure Databricks, SEGES streamlined the entire machine learning lifecycle—transforming training, deployment, and monitoring of predictive models for cattle health and crop yield forecasting.The organization uses real-time data from IoT sensors and cameras integrated into their data estate, enabling a 90% accuracy rate in predicting cattle health events and precise crop yield estimations per field. Automated retraining, scalable deployment, and governance brought maintenance costs down by over 95% and non-labor costs by more than 80%.The reduction in labor hours allows SEGES to focus on new product innovation and broader farmer outreach, while ongoing improvements help meet Denmark’s and global sustainability goals.
Common questions
