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.
AWS Services
Purpose-built cloud products
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AWS Solutions
Ready-to-deploy solutions assembling AWS Services, code, and configurations
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Partner Solutions
Software, SaaS, or managed services from AWS Partners
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Total results: 9
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EOSDA Crop Monitoring
EOSDA Crop Monitoring is an online satellite-based precision agriculture platform for field monitoring created by EOS Data Analytics, a global provider of AI-powered satellite imagery analytics. The platform is a one-stop solution that integrates multiple types of data (crop health, weather conditions, crop rotation, field activities, elevation, soil moisture, and a host of other data) all in one place. -
Climate X Spectra - Global Climate Risk Data Analytics
*Global climate risk data & analytics provider delivered via self-serve platform or API. *Asset and company level intelligence tailored to the needs of financial institutions. *All key physical risks covered - Flooding, Hurricanes, Wildfires, Subsidence, Coastal Erosion, Drought +more. *Financial losses (cVAR) - $/% physical damages from climate hazards & business disruption. *All RCP/SSP emission pathways covered for complete scenario analysis. *Models built in-house, backed by academia and achieving up to 95% accuracy. *Already trusted by major real estate, banks, consulting groups + industry
Guidance
Prescriptive architectural diagrams, sample code, and technical content
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Total results: 6
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Geospatial Data Enhancement for Agronomic Data Visualization on…
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.