SEGES Innovation revolutionizes sustainable agriculture with AI-powered predictive analytics

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.