Agricultural companies can unlock value for producers and food manufacturers by building a comprehensive dataset describing the production of an agricultural product, but seamlessly aggregating data from disparate sources, often in incompatible formats, poses a challenge. Data Interoperability solutions on AWS provide the tools to bring agricultural production data together into a unified format for analysis and artificial intelligence and machine learning (AI/ML) use cases.
Partner Solutions
Software, SaaS, or managed services from AWS Partners
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Machine data translation
Leafs product is a unified farm data API. Companies use our API to send and receive user-permissioned data with other companies in a consistent, standardized way. Leafs Machine Data Translation service accepts .zip files with machine data files inside, detects file types, translates the proprietary files, and returns geoJSON summaries, full point data, and maps of the operation. With Leafs Machine Data Translation service you can become automatically compatible with all major agriculture machine file types (50 and counting) and focus on building new value with the data. Companies of all sizes rely on Leaf including the largest food and agriculture companies in the world. For example, if you wanted to get the date a field was planted, you could access this information from John Deere, Climate Fieldview, AFS connect, or machine data files stored locally in .jdl, .dat, .cn1, .agdata, .isoxml, or more than 50 other proprietary formats. These data sources all store data in different, proprietary ways, have different authentication and hierarchy structures, dramatically different API design, and different abstraction concepts (season-boundaries vs. static field boundary) so your company would need to build and maintain functions to navigate and translate through these different structures. In addition, the data sources will change their APIs and data structures several times per year. With Leaf, your company can connect once to Leafs API and retrieve and send data across all providers without needing to worry about the complexity of each individual provider.
Guidance
Prescriptive architectural diagrams, sample code, and technical content
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Querying Sustainability Documents Using Generative AI…
This Guidance demonstrates how to use Retrieval-Augmented Generation (RAG) for your environmental, social, and governance (ESG) or sustainability knowledge base by combining Amazon Kendra and a large language model (LLM) from Amazon Bedrock—a fully managed service offering high-performing foundation models. -
Building a Secure & Intelligent Search Application on AWS
This Guidance helps you deploy search functionality powered by Amazon Kendra. For many organizations, critical business information is scattered across multiple content repositories, making it challenging for employees to access and securely share the right information.