Earth observation data can empower agribusiness professionals, from lenders to insurers, food & beverage manufacturers to digital ag companies, to better manage operations and risks associated with climate change, soil degradation, access to water, and food insecurity to minimize risk and optimize output of crop production.
EarthDaily has a dedicated business unit to serving the needs of Agribusiness. EarthDaily Agro provides space age data and analytics for the organizations and people who feed the planet.
All these efforts, from modelling to prediction, advisory to mitigation, require timely, accurate measurements. Upon the launch of EarthDaily Constellation, we will be ready to take change detection to the next level with earth observation data that boasts the best quality, the broadest coverage, and the highest revisit frequency.
What does the world’s most advanced change detection system look like?
Experience the EarthDaily Constellation
The unprecedented insights unlocked by our constellation will enable our customers to make informed decisions with the magnitude of accuracy and efficiency unseen before.
Impact / Results
With these solutions it is possible to increase bandwidth with the reduction of manual workload and to enable lenders and insurers to actively manage their portfolios. Faster data delivery means traders can capitalize on market opportunities and food and bev companies can have more control over their supply chain.
- 60–100% increase in agricultural production, with everything else unchanged, is imperative to meet the nutritional needs of a future 9–10 billion human population
- Fintech: loss ratio reduced by up to 150% and fraudulent claims well below 2%
- Digital Ag Companies’ Business Growth
- Mitigating Risks for Fintech Companies
- A Competitive Edge for Commodity Traders
- Control for Food & Beverage Companies
Offering customers the ability to extract and Exploring Analysis Ready Samples is an online tool that offers users a simple and efficient way to perform data access and transformation processes. By enabling users to subset data spatially, temporally, and by layer, the volume of data downloaded for analysis is greatly reduced.