Our Solutions Tackle the Biggest Challenges in Sustainable Agriculture

A Global Soil health dataset: Cloud tracks global carbon stocks down to the variation within a single field.

We quantify soil organic matter in topsoil and subsoil with more accuracy than the gold standard of physical in-field sampling.

Verification of Farmland Carbon Markets
Modeling regenerative Ag Practices
Phasing out physical sampling processes

Monitor in-season nutrients in row crops with our Dynamic Nutrient Measurement.

Our measurement of in-season nutrients gives you the information to make targeted applications, evaluate the success of restorative agriculture practices, and understand the ties between plant and soil health -- all at a fraction of the cost of sampling operations.

Water Runoff Identification
Precision Fertilizer Applications
Optimize Transportation Logistics

We can work together to create Custom Insights for your use cases.

Our Custom R&D arm is dedicated to expanding our analytics by incorporating data from third parties to produce value for our partners.

Grain Specifications
Micronutrients
New Organic Products

Powered by a technological breakthrough and
backed by science

Through hyperspectral imagery, we are reinventing verification and visibility for the building blocks of healthy farmland.

Hyperspectral Imagery is like a chemical fingerprint

Hyperspectral has been a de-facto standard for lab-grade chemical measurements for decades. It’s based on the principle that all matter -- especially plants and soil -- interact with light slightly differently depending on their chemical composition.

That means an N-deficient leaf looks slightly different than an N-sufficient leaf. Or that soil with 1.1% organic carbon looks slightly different than soil with 1.15% organic carbon.

Cloud Agronomics is the first private company to scale this lab-grade measurement capability across millions of acres of farmland.

To do this, our science team had to reinvent how hyperspectral data is calibrated, processed, and stored in the cloud. The result is a dataset that contains up to 300x more information per pixel than satellite imagery.

Rich Data powers better AI

We’ve developed in-house AI techniques to learn how to turn terabytes of hyperspectral data into accurate, precise, quantifiable remote measurements of farm chemistry. The high information content of hyperspectral makes for a perfect marriage with AI and deep learning algorithms. And the more farmland we see, the more our algorithms learn and improve.