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For agricultural companies, generating actionable insights from complex agronomic and production data can be a challenge. By using Data Analytics solutions on AWS, these companies can access tools to build powerful data systems, take advantage of artificial intelligence and machine learning, and generate powerful analytics. These capabilities help agricultural companies transform production data into a strategic asset for customers, like agricultural producers.
Deploy a multi-channel, multi-language conversational interface (chatbot) that responds to your customer's questions, answers, and feedback, powered by large language models (LLMs).
LiveEO satellite-based vegetation management solution helps increase grid safety, uptime and transparency on work processes, while reducing operational costs.
The reflectance Datacube Processor will ease deployment of analysis ready pixel pipeline supporting modeling effort either at early stage with model design and training but also model serving with fresh and up to date pixels.
CARTO’s Spatial Extension brings advanced geospatial capabilities to Amazon Redshift. Analysts, Data Scientists, and Developers can solve spatial problems by understanding where and why things happen, optimizing processes, and predicting future outcomes. Organizations working with spatial data in Redshift can create stunning map visualizations, ingest thousands of 3rd party location datastreams, develop spatial applications, and run advanced spatial analysis natively in Amazon Redshift, using simple SQL. Our cloud-native approach benefits from Redshift’s speed, security, and scalability, simplifying analytical workflows for faster, more insightful location-based decision making.
This Guidance shows how to predict the weather over the Continental United States (CONUS) by deploying the Weather Research and Forecasting (WRF) model on AWS.
This Guidance helps agricultural companies obtain advanced analytics from connected assets and streamlines management of connected assets with custom relationships.
This Guidance demonstrates how to use Decision Support System for Agrotechnology Transfer (DSSAT) crop growth simulation technology to more efficiently run crop simulation models.
This Guidance provides a technical foundation to visualize and monitor key performance indicators (KPIs) for reporting on climate-related physical risks to your operations.
This Guidance shows how to store and use vector data, which is a type of data made up of objects that represent a physical location such as a farm field, location in a field, or entire agricultural region.
This Guidance helps customers import, process, and display geospatial imagery with Amazon SageMaker geospatial capability. By demonstrating how to use a geospatial capability in the agricultural use case, this is a starting point for customers looking to build an agronomic data platform.
